Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Lee, Adam
Master of Applied Science - MASc
University of British Columbia
Cycling as a method of transportation offers benefits to the individual and to the society as a whole. It promotes a healthy lifestyle, is economically cost effective, and can reduce traffic congestion, noise pollution and greenhouse gas emissions. Some of the main barriers that prevent individuals from cycling as a primary mode of transportation are the perceived discomfort and increased risk that one feels when using cycling infrastructure. In order to help promote cycling, it is important to be able to quantify and predict a cyclistâs perception of existing and proposed cycling infrastructure. This study develops and proposes a statistical model that predicts cyclist perceptions. The model was developed using a bicycle outfitted with sensors which would measure the speed, proximity and size of adjacent motor vehicle traffic. 105 volunteer cyclist participants completed a survey where they provided demographic information and their opinions on cycling infrastructure. The participants then rode four predetermined cycling routes, indicating their perceived comfort and safety after each ride. Using the demographic information, the opinions of the participants, their reported perceptions of comfort and safety as well as the data collected from the sensors a statistical model was fitted. The results indicated that there are two levels of cyclists within the participant population (level 1 and level 2), with level 2 being the more confident group. Assessing the opinions of the participants was the best way to determine their level. It was measured that a 2.1 m bicycle lane gave significantly improved levels of comfort and safety, as was separation from motor vehicle traffic or the lack thereof. Safety and comfort were discovered to be similar but not identical measures of cyclist wellbeing and a new variable named âwellnessâ was proposed to measure the overall perceived wellbeing of a cyclist on a cycling route. The level 2 cyclist group consistently reported feeling higher levels of wellness compared to the level 1 cyclist group. As the participants in this study were self-selected cyclists comfortable with cycling, additional studies are needed to determine the perceptions of more timid cyclists in the population as well as children.
SUSTAINABLE TRANSPORT SAFETY: A STUDY ON PREDICTING CYCLISTSâ PERCEPTION OF SAFETY AND COMFORT by Adam Lee B.A.Sc., The University of British Columbia, 2012 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) August 2020 © Adam Lee, 2020 i The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: SUSTAINABLE TRANSPORT SAFETY: A STUDY ON PREDICTING CYCLISTSâ PERCEPTION OF SAFETY AND COMFORT Submitted by Adam Lee in partial fulfillment of the requirements of the degree of Master of Applied Science. Dr. Gordon Lovegrove, School of Engineering Supervisor Dr. Rehan Sadiq, School of Engineering Supervisory Committee Member Dr. Bernard Momer, Irving K. Barber School of Arts and Sciences Supervisory Committee Member Dr. Sylvia Esterby, Irving K. Barber School of Arts and Sciences University Examiner ii Abstract Cycling as a method of transportation offers benefits to the individual and to the society as a whole. It promotes a healthy lifestyle, is economically cost effective, and can reduce traffic congestion, noise pollution and greenhouse gas emissions. Some of the main barriers that prevent individuals from cycling as a primary mode of transportation are the perceived discomfort and increased risk that one feels when using cycling infrastructure. In order to help promote cycling, it is important to be able to quantify and predict a cyclistâs perception of existing and proposed cycling infrastructure. This study develops and proposes a statistical model that predicts cyclist perceptions. The model was developed using a bicycle outfitted with sensors which would measure the speed, proximity and size of adjacent motor vehicle traffic. 105 volunteer cyclist participants completed a survey where they provided demographic information and their opinions on cycling infrastructure. The participants then rode four predetermined cycling routes, indicating their perceived comfort and safety after each ride. Using the demographic information, the opinions of the participants, their reported perceptions of comfort and safety as well as the data collected from the sensors a statistical model was fitted. The results indicated that there are two levels of cyclists within the participant population (level 1 and level 2), with level 2 being the more confident group. Assessing the opinions of the participants was the best way to determine their level. It was measured that a 2.1 m bicycle lane gave significantly improved levels of comfort and safety, as was separation from motor vehicle traffic or the lack thereof. Safety and comfort were discovered to be similar but not identical measures of cyclist wellbeing and a new variable named âwellnessâ was proposed to measure the overall perceived wellbeing of a cyclist on a cycling route. The level 2 cyclist group consistently reported feeling higher levels of wellness compared to the level 1 cyclist group. As the participants in this study were self-selected cyclists comfortable with cycling, additional studies are needed to determine the perceptions of more timid cyclists in the population as well as children. iii Lay Summary Widespread adoption of cycling as a mode of transportation offers significant benefits for public health, the economy, and the environment. To encourage an uptake of cycling in Canada, it is crucial to understand why and where people choose to cycle. This insight will allow the desirability of cycling infrastructure to be better predicted, and improvements in design implemented. In this thesis, one hundred five participants were recruited to complete a detailed survey and ride a bicycle fitted with sensors and cameras which collected data regarding the cyclistsâ surrounding environment. Survey data was utilized to group cyclists of different risk tolerances in order to gain a better upstanding of how different types of cyclists perceive the same cycling infrastructure. Data from the sensors and survey responses were utilized to develop a statistical model that predicts and quantifies cycling infrastructure desirability. iv Preface This research was made possible through financial contributions of the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Canada Graduate Scholarships Masterâs Program, NSERC Discovery Grant and NSERC Engage Grant. Urban Systems Limited, Kelowna, BC were partners via the NSERC Engage Grant. Additional contributors include UBC Okanagan through the Work Study program, and the University Graduate Fellowship, and the Transportation Association of Canada Foundation through the Western Association of Highway Officials Legacy Scholarship. The opinions expressed in this thesis are those of the researcher and not the project funder. Preliminary results were published in conference proceedings of the International Cycling Safety Conference 2014 held in Gothenburg, Sweden. Lee, A., Dias, L., Mohanty, S., Carvalho, T., Commandeur, J., & Lovegrove, G. (2014). Using Instrumented Probe Bicycles to Develop Bicycle Safety and Comfort Prediction Models. International Cycling Safety Conference 2014, November, 1â20. James Donnelly, Urban Systems Ltd. provided professional advice during preliminary research. Preliminary and the full research were approved by the UBC Okanagan Behavioural Research Ethics Board and the UBCO School of Engineering. Certificate Numbers H14-01345 and H15-00946. This research was further supported by the contributions of Kelowna Area Cycling Coalition, Kelowna Cycle, The Hot Box Yoga, Cyclepath Kelowna and Arrowhere through the donation of gift cards and prizes for the volunteer research participants. This research was supported by over one hundred volunteers who graciously contributed their time and effort to the data collection work. v Table of Contents Abstract ......................................................................................................................................... ii Lay Summary ............................................................................................................................... iii Preface .......................................................................................................................................... iv Table of Contents ........................................................................................................................... v List of Tables ................................................................................................................................ ix List of Figures ............................................................................................................................. xii List of Equations ........................................................................................................................ xiv Acknowledgments ........................................................................................................................ xv 1 Introduction ............................................................................................................................ 1 1.1 Benefits of Cycling .......................................................................................................... 1 1.2 The Decision to Cycle ...................................................................................................... 5 1.2.1 The Cycling Environment ......................................................................................... 6 1.2.2 The Individual ............................................................................................................ 7 1.2.3 Comfort and Safety .................................................................................................... 8 2 Literature Review ................................................................................................................. 10 2.1 Research on Bicycle Level of Service ........................................................................... 10 2.2 Research on Cyclist-Automobile Interactions ............................................................... 22 2.3 Research on Cyclist Typology ....................................................................................... 25 2.4 Research on Route Choice ............................................................................................. 31 2.5 Research on Bicycle-Cyclist Dynamics ......................................................................... 35 3 Objectives and Contribution ............................................................................................... 37 4 Methodology.......................................................................................................................... 40 4.1 Preliminary Pilot Study .................................................................................................. 40 4.2 Instrumented Probe Bicycle ........................................................................................... 41 vi 4.2.1 IPB 1.0 Design and Operation ................................................................................. 43 4.2.1.1 IPB 1.0 Challenges .......................................................................................... 46 4.2.2 IPB 1.1 Design and Operation ................................................................................. 49 4.2.2.1 IPB 1.1 Challenges .......................................................................................... 53 4.3 Participants ..................................................................................................................... 54 4.4 Questionnaire ................................................................................................................. 56 4.5 Test Routes ..................................................................................................................... 57 4.5.1 Section 1 .................................................................................................................. 57 4.5.2 Section 2 .................................................................................................................. 60 4.5.3 Section 3 .................................................................................................................. 61 4.5.4 Section 4 .................................................................................................................. 62 4.6 Experimental Procedure ................................................................................................. 64 4.7 Model Development ....................................................................................................... 66 4.8 Data Analysis and Modelling Techniques ..................................................................... 67 4.8.1 Pearson Correlation ................................................................................................. 67 4.8.2 T-Test ....................................................................................................................... 67 4.8.3 Principle Component Analysis (PCA) ..................................................................... 68 4.8.4 K-Means Clustering ................................................................................................. 69 4.8.5 One-Way Analysis of Variance (ANOVA) ............................................................. 69 4.8.6 General Linear Model (GLM) ................................................................................. 70 4.8.7 Analysis Assumptions ............................................................................................. 71 5 Data and Post Processing ..................................................................................................... 74 5.1 Malfunctions and Creation of Data Sets ........................................................................ 74 5.2 Post Processing Data ...................................................................................................... 76 5.3 Descriptive Statistics ...................................................................................................... 78 5.4 Accidents and Close-Calls (Group D Data) ................................................................... 81 vii 6 Analysis and Discussion ....................................................................................................... 89 6.1 Model Development - The Response Variables ............................................................ 89 6.1.1 Observations of Means (Group C Data) .................................................................. 89 6.1.2 Observations of Correlations (Group D Data) ......................................................... 95 6.1.3 Principle Components Analysis (Group D Data) .................................................... 98 6.1.4 Safe-Comfort Index (Group C Data) ..................................................................... 105 6.2 Model Development - The Cyclist Typology .............................................................. 106 6.2.1 Initial Variable Screening using Two-Step Cluster Analysis (Group D Data) ...... 107 6.2.2 Secondary Variable Screening using Principle Component Analysis (Group D Data) 108 6.2.3 K-Means Clustering Analysis Results (Group C and D Data) .............................. 112 6.2.4 Assigning New Participants to Existing Cyclist Typology ................................... 116 6.2.5 Analysis of the Cyclist Typology (Group D Data) ................................................ 117 6.3 Model Development - The Input Variables ................................................................. 119 6.3.1 Vehicular Traffic (Group A and B Data) ............................................................... 119 6.3.2 Infrastructure (Group C Data) ............................................................................... 121 6.4 Model Development - The Prediction Model .............................................................. 123 6.4.1 Section 1 Specific Univariate GLM (Group A Data) ............................................ 123 6.4.2 Section 2 Specific Univariate GLM (Group B Data) ............................................ 125 6.4.3 Section 1 to Section 4 - Repeated Measures GLM (Group C Data) ...................... 127 6.4.3.1 Within Subject Effects â Section (Bicycle Infrastructure Type) ................... 129 6.4.3.2 Between Subject Effects â CyclistTypology ................................................. 132 6.4.3.3 Parameter Estimates of Repeated Measures GLM ........................................ 133 6.4.4 Utilizing the Prediction Model .............................................................................. 135 6.5 The Importance of Bicycle Lane Width (Group A and B Data) .................................. 141 7 Conclusions ......................................................................................................................... 150 viii 7.1 Key Findings ................................................................................................................ 150 7.2 Limitations and Future Work ....................................................................................... 152 Bibliography ............................................................................................................................... 158 Appendices ................................................................................................................................. 165 Appendix A â Summary of 105 trial runs and final treatment of data.................................... 165 Appendix B â Variable Definitions ........................................................................................ 169 ix List of Tables Table 4-1. Sensors configured on IPB 1.0 .................................................................................... 44 Table 4-2. All proposed sensors and devices for IPB 1.1 ............................................................. 49 Table 4-3. Google Map Data on Section length, elevation change and calculated slope ............. 57 Table 5-1. IPB 1.1 Sensors, Data provided and data streams. ...................................................... 74 Table 5-2. Outline of malfunction scenarios and their remedy .................................................... 75 Table 5-3. Data Set Summary Description ................................................................................... 76 Table 5-4. General Participant Descriptive Statistics ................................................................... 79 Table 5-5. Group D Data - Cycling Accident and Close Call Rates ............................................ 81 Table 6-1. Group C Data - Mean response values (range: 0,10) for all 9 response variables on each road section........................................................................................................................... 91 Table 6-2. Group D Data - Pearson Correlation of Section 1 Response Variables ...................... 95 Table 6-3. Group D Data - Pearson Correlation of Section 2 Response Variables ...................... 96 Table 6-4. Group D Data - Pearson Correlation of Section 3 Response Variables ...................... 97 Table 6-5. Group D Data - Pearson Correlation of Section 4 Response Variables ...................... 97 Table 6-6. Group D Data - PCA Variance explained by each component ................................. 100 Table 6-7. Group D Data â Communalities for 1 component extraction for Section 1 through 3 and 2 Component Extraction for Section 4. Extraction Method: Principal Component Analysis .................................................................................................................................................... 102 Table 6-8. Group D Data - KMO and Barlettâs Test .................................................................. 103 Table 6-9. Group D Data - PCA Pattern Matrix for each Section. Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization. ......................... 104 Table 6-10. Group C Data â Descriptive Statistics and Pearson Correlation between Safe and Comf for each Section ................................................................................................................ 106 Table 6-11. Group C Data â Paired Sample T-Test between Safe and Comf for each Section . 106 Table 6-12. Group D Data - PCA Variance explained by each component for Opinion Questions .................................................................................................................................................... 110 Table 6-13. Group D Data - PCA Pattern Matrix of Opinion Questions ................................... 111 Table 6-14. Group C and D Data â K-Means Clustering Trials for Cyclist Typology .............. 114 Table 6-15. Group D Data - Trial 7 - K-Means Clustering Summary of Results ....................... 115 x Table 6-16. Group D Data - Descriptive Statistics of Level 1 and Level 2 Cyclists against Safe- Comfort Index Variable "Well" for all Sections ......................................................................... 117 Table 6-17. Group D Data - Test of Homogeneity of Variances of Level 1 and Level 2 Cyclists against Safe-Comfort Index Variable "Well" for all Sections .................................................... 118 Table 6-18. Group D Data - ANOVA of Level 1 and Level 2 Cyclists against Safe-Comfort Index Variable "Well" for all Sections ....................................................................................... 118 Table 6-19. Group A Data - Univariate GLM Results for Section 1 and Section 2 ................... 120 Table 6-20. Group B Data - Univariate GLM Results for Section 2 .......................................... 121 Table 6-21. Group C Data - Paired Sample Statistics and Pearson Correlations of Safe-Comfort Index for Section 1 through 4 ..................................................................................................... 122 Table 6-22. Group C Data - Paired Sample Test Statistics of Safe-Comfort Index for Section 1 through 4 ..................................................................................................................................... 122 Table 6-23. Group A Data â Section 1 Specific Model Development Descriptive Statistics .... 124 Table 6-24. Group A Data â Section 1 Specific Model Development Levene's Test of Equality of Error Variances ........................................................................................................................... 124 Table 6-25. Group A Data â Section 1 Specific Model Development ....................................... 124 Table 6-26. Group A Data â Section 1 Specific Model Development Parameter Estimates ..... 125 Table 6-27. Group B Data â Section 2 Specific Model Development Descriptive Statistics .... 126 Table 6-28. Group B Data â Section 2 Specific Model Development Levene's Test of Equality of Error Variances ........................................................................................................................... 126 Table 6-29. Group B Data â Section 2 Specific Model Development Tests of.......................... 126 Table 6-30. Group B Data â Section 2 Specific Model Development Parameter Estimates...... 127 Table 6-31. Group C Data - Descriptive Statistics of Section and CyclistTypology ................. 129 Table 6-32. Group C Data - Mauchly's Test of Sphericitya ........................................................ 129 Table 6-33. Group C Data â Tests of Within-Subjects Effects .................................................. 131 Table 6-34. Group C Data â Tests of Within-Subjects Contrasts............................................... 132 Table 6-35. Group C Data â Levene's Test of Equality of Error Variancesa .............................. 132 Table 6-36. Group C Data â Tests of Between-Subjects Effects â CyclistTypology ................ 133 Table 6-37. Group C Data â BCSPM Prediction Model Parameter estimates ........................... 134 Table 6-38. Group C Data â BCSPM Equations ........................................................................ 135 Table 6-39. Group C Data â Current Desire Prediction Model Table ........................................ 136 xi Table 6-40. Group B Data - Descriptive Statistics of S1_Well with respect to the state of section 1(Sec1State) and CyclistTypology ............................................................................................. 142 Table 6-41. Group A Data â Pair 1 to Pair 10 Sample Mean T-Tests and Correlation of Car Passing Speed, Car Length, Car Passing Distance, and Car Volume comparing a 2.1 m bike lane Section 1 with a 1.5 m bike lane Section 2. ................................................................................ 145 Table 6-42. Group A Data â Pair 11 to Pair 17 Sample Mean T-Tests and Correlation of Car Passing Speed, Car Length, Car Passing Distance, and Car Volume comparing a 2.1 m bike lane Section 1 with a 1.5 m bike lane Section 2. ................................................................................ 146 Table 6-43. Group A Data â S1_CarMinPassDist Distribution ................................................. 147 Table 6-44. Group A Data â S2_CarMinPassDist Distribution ................................................. 148 xii List of Figures Figure 1-1.Breakdown of factors contributing to a decision to cycle............................................. 6 Figure 1-2. Elements within the cycling environment.................................................................... 7 Figure 4-1. IPB 1.0, configured sensors and mounting system .................................................... 45 Figure 4-2. Final form of IPB 1.1 ................................................................................................ 50 Figure 4-3. Discontinued Sensors - Sensors initially considered for IPB 1.1 but not utilized ..... 51 Figure 4-4. IPB 1.1 Sensor mounting configurations ................................................................... 52 Figure 4-5. Google map of Section 1............................................................................................ 58 Figure 4-6. Old Section 1 with worn pavement ............................................................................ 59 Figure 4-7. Newly Paved Section 1 with marker lines painted .................................................... 60 Figure 4-8. Newly Paved Section 1 with double lines painted ..................................................... 60 Figure 4-9. Google Map of Section 2 ........................................................................................... 61 Figure 4-10. Section 2 - Paved bike lane estimated to be several years old, some cracks present61 Figure 4-11. Google map of Section 3.......................................................................................... 62 Figure 4-12. Section 3 - Gravel pathway ...................................................................................... 62 Figure 4-13. Google map of Section 4.......................................................................................... 63 Figure 4-14. Section 4 - Paved residential street â pavement estimated to be several years old . 63 Figure 4-15. Experiment Map....................................................................................................... 65 Figure 4-16. Staging, Holding and Equipment Storage Area ....................................................... 65 Figure 4-17. Model of cycling environment, perception, and cyclist behaviour interaction in reality ............................................................................................................................................ 66 Figure 4-18. Simplified statistical model representation of cyclist perception and environment relationship ................................................................................................................................... 67 Figure 5-1. Participant Age Histogram ......................................................................................... 80 Figure 5-2. Group D Data - Cycling Accident (Crash) Involvement Details ............................... 84 Figure 5-3. Group D Data - Cycling Accident (Crash) Locations Details ................................... 85 Figure 5-4. Group D Data - Cycling Accident (Crash) Causes Details ........................................ 85 Figure 5-5. Group D Data - Cycling Collision Involvement Details ............................................ 86 Figure 5-6. Group D Data - Cycling Close Call Involvement Details.......................................... 87 Figure 5-7. Group D Data - Cycling Close Call Location Details ............................................... 88 Figure 5-8. Group D Data - Cycling Close Call Causes Details .................................................. 88 xiii Figure 6-1. Group C Data - Bar plot of the mean responses variables ......................................... 90 Figure 6-2. Group D Data - Scree plots for Section 1, Section 2, Section 3 and Section 4 ......... 99 Figure 6-3. Group D Data - Scree plots for Opinion Questions ................................................. 110 Figure 6-4. Group D Data â Component plots ........................................................................... 112 Figure 6-5. Group C Data - Estimated Means of Wellness for all Sections for both Cyclist Types .................................................................................................................................................... 135 Figure 6-6. Example of Separated Paved Path Way âCawston Avenue (Local Street), Kelowna BC â Google Street View ........................................................................................................... 139 Figure 6-7. Example of Separated Paved Path Way âHornby Street (Collector), Vancouver BC â Google Street View..................................................................................................................... 139 Figure 6-8. Example of Separated Paved Path Way â Abbott Street (Local), Kelowna BC â Google Street View..................................................................................................................... 140 Figure 6-9. Example of Separated Paved Path Way â 10th Avenue West (Collector), Vancouver BC â Google Street View ........................................................................................................... 140 Figure 6-10. Example of On-Street Paved Bike Way and Traffic Calming â 10th Avenue West (Local), Vancouver BC â Google Street View ........................................................................... 141 Figure 6-11. Group B Data - Mean Predicted Perceived Wellness (S1_Well) according to Cyclist Typology and state of Section 1 ................................................................................................. 143 xiv List of Equations Equation 2-1. Bicycle Compatibility Index by Harkey et al. (1998) ............................................ 13 Equation 2-2. Bicycle Compatibility Index for intersections by Harkey et al. (1998) ................. 13 Equation 2-3. Sidepath Safety Model by Petritsch et al. (2006) .................................................. 17 Equation 2-4. Cycling Stability Index by Joo & Oh (2013) ......................................................... 20 Equation 2-5. Bicycle Monitoring Index by Joo & Oh (2013) ..................................................... 20 Equation 2-6. Bicycle Level of Service Model by Yamanaka et al. (2013) ................................. 21 Equation 6-1. Cluster Assignment of new participants .............................................................. 116 xv Acknowledgments Thank you to my family for supporting me: Parents Benedicte Lee & Kin Min Lee Uncle Doug Ledgerwood & Auntie Ann Wife, Tien Tran Thank you to all of my teachers for their guidance: Dr. Gordon Lovegrove Dr. Rehan Sadiq Dr. Bernard Momer Dr. Sylvia Esterby Dr. Ahmed Idris Dr. Abbas Milani Thank you to my colleagues who helped to construct the bicycle and write the code: Justin Choi Tyler Reid Luis Dias Thank you to my colleagues who were there to offer advice, insight and encouragement: Abdul Rahman Masoud Mohammed Hegazi xvi Thank you to all those who helped with the preliminary study: James Donnelly, Urban Systems Limited, Kelowna BC Abdul Rahman Masoud Victoria You Thiago Silviera Farhad Faghihi Luis Dias Sudatta Mohanty Thank you to all the businesses that supported this project: Kelowna Area Cycling Coalition Kelowna Cycle The Hot Box Yoga Cyclepath Kelowna Arrowhere Thank you to all of my 105 participants for volunteering to ride the IPB for without you all this study would not be possible. Special Thanks to my Mentors who kept me motivated: David Goggins Sam Harris Joe Rogan Bruce Lee 1 1 Introduction Cycling as a mode of transportation provides significant benefits to both society and the individual. These benefits can only be fully appreciated when we examine the plethora of systemic problems that cycling can alleviate â automobile dependence, excessive greenhouse gas emissions, sedentary life styles, noise pollution, traffic congestion, and high transportation costs. This chapters begins by explaining how transportation has shaped todayâs cities; leading to the aforementioned systemic ailments. Cycling as a primary mode of transportation is explored as a solution, followed by an overview of the factors that govern an individualâs decision to cycle. 1.1 Benefits of Cycling Prior to 1870, walking was the main mode of transportation and the size of most cities was limited to a walk able radius of approximately 4.8 km. After 1870, significant change in the development of cities occurred beginning with the introduction of efficient public transportation in the late 19th and early 20th century â first with horse drawn trams followed by electric trams and buses. This allowed urban growth to spread outward along these transit corridors and electric trains in the 1930s allowed cities to expand even further (Hall, 2002). Shortly after World War II, rising incomes and car ownership allowed for a highly mobile population, which combined with a lack of zoning regulations, and increased investments on roads, lead to unrestricted urban growth in all directions known as urban sprawl (Nechyba & Walsh, 2004; De Vos & Witlox, 2013). According to Nechyba & Walsh (2004) urban sprawl has two fundamental characteristics; a low population density, and increased suburban land development. Urban sprawl is often associated with the segregation of residential and non-residential land use which encourages automobile travel for even the most basic necessities (Dunn et al., 2009). A positive feedback loop creates a situation where increased sprawl leads to increased automobile use, and increased automobile use allows more sprawl ( Lee, 2004). This is evident in the observed rise of car ownership in North America and around the world. By 1952, most American households already owned at least one car (Nechyba & Walsh, 2004). Today, the same rise in car ownership and urban sprawl is observed in developing countries (Delucchi & Kurani, 2014; Kutzbach, 2009). 2 Urban sprawl and automobile dependence leads to a plethora of negative externalities ranging from traffic congestion, increased greenhouse gas emissions, inefficient use of resources, and an increased risk to the health and safety of the public. These societal ailments are so prevalent that they have become the norm in many urban centers globally. Many solutions have been proposed with most including provisions designed to encourage active transportation, particularly cycling. Cycling provides significant benefits for public health and the economy, and has been shown to be an effective solution for climate change and traffic congestion (Ayres, 2014; Johan de Hartog et al., 2010; Pirlone & Candia, 2015; Sælensminde, 2004) Congestion In Bangkok, Manila, and Shanghai, traffic congestion has decreased the average car travel speeds to 10 km/h during the weekday (Kutzbach, 2009) â the speed of the average jogger. In Mexico City, it can take more than 4 hours to cover 20 km â a distance that can be covered in less time by walking (Pirlone & Candia, 2015). Traffic congestion is likely to worsen as the number of automobiles in developing countries continue to increase (Kutzbach, 2009). Congestion, not only increases travel time, but also wastes valuable resources. In a U.S. study conducted in 2001, nearly 200 extra liters of fuel is wasted per person annually (Schrank & Lomax, 2003). Traffic congestion in Warsaw, Marseille, Rome, and Paris is estimated to cost 80 billion euro per year. Utilizing ccycling as a primary mode of transportation in conjunction with walking is a successful solution to decrease traffic congestion. Many cities such as Amsterdam, Copenhagen and Seville have implemented this solution already (Pirlone & Candia, 2015). Greenhouse Gas Emissions In 2010, the transport sector was responsible for 22% of global CO2 emissions (International Energy Agency, 2012), with nearly 60% of greenhouse gas (GHG) emissions originating from cars and light trucks (United States Environmental Protection Agency, 2006). Furthermore, as vehicles are generally less efficient at slower travel speeds, increased congestion exacerbates the situation, leading to further increases in released emissions (Boriboonsomsin & Barth, 2008; Nechyba & Walsh, 2004). Cycling 3 produces no emissions and can aid in the reduction of GHG emissions, as well as noise pollution within communities. Increased uptake of cycling can be a factor that slows climate change (Ayres, 2014). A study of Latin American cities estimates that an 8.4% reduction of GHG emissions can be attained by simply increasing cycling mode share from 1% to 10% (Kahn Ribeiro et al., 2007). Public Health Automobile dependence contributes to an increasingly sedentary life style that is detrimental to health. Six of the seven leading causes of death are non-communicable diseases such as heart disease, stroke, chronic obstructive pulmonary disease, lung cancers, dementia and diabetes (World Health Organization, 2018b). The situation in Canada is similar with the four leading causes of death being cancer, heart disease, stroke and lung disease (Statistics Canada, 2017b). These are all diseases that an active lifestyle can help reduce. The World Health Organization (WHO) estimates a 15% reduction in mortality rate for riding a bicycle for just 30 minutes per day (Pirlone & Candia, 2015). Physical activity is proven to have profound health benefits and has been linked to decreased levels of obesity, increased cardiovascular health, and greater overall fitness (Pucher & Buehler, 2010). A systemic review of the health benefits of cycling revealed a host of advantages and strong support for promoting cycling to improve public health. Cycling was associated with increased cardio-respiratory fitness, and health and functional benefits in children. Cycling was connected with improvements in disease risk factors. Furthermore, cycling was related to substantial reductions in all-cause and cancer mortality, and cardiovascular, cancer and obesity morbidity (Oja et al., 2011). Physical activity from cycling can increase total life span by 3 to 14 months, and outweighs any negative effects such as increased exposure to air pollution and risk of traffic accidents (Johan de Hartog et al., 2010). Another study estimates that just 75 to 150 min per week of exercise (equivalent to brisk walking) can increase life span by 1.8 to 4.5 years (Moore et al., 2012). Cycling is a means of providing an opportunity for such exercise, and it been estimated that cycling gives benefits that outweigh the negative health impacts by about 19 times (OECD/International Transport Forum, 2013). 4 Economics Financial losses from road collisions can be up to 6.6% of a nationâs GDP with Canada experiencing losses totalling 5% of GDP (World Health Organization, 2013). A large modal shift from personal automobiles to bicycles can decrease these losses substantially. Additionally, the total economic cost per km travelled for cycling has been shown to be 6 times less than the cost for driving (Lyngby & Tel, 2009). The economic benefits of cycle networks were projected to be a minimum of 4-5 times the investment cost, making cycling one of the most beneficial transportation modes to society (Sælensminde, 2004). Finally, cars are extremely expensive compared to bicycles â only a small portion of the global population can afford to own and operate automobiles while the majority of the global population can afford access to a bicycle. A modal shift to bicycles will increase the accessibility of our urban environment to larger portions of the populations. IMPORTANT SAFETY NOTE Widespread automobile dependence and urban sprawl has contributed to massive safety concerns due to large numbers of deaths and injuries worldwide from traffic collisions (World Health Organization, 2013; Grammenos et al., 2008). Road collisions are the 8th leading cause of death worldwide, and the foremost cause of death among young people ages 5 to 29. Road collisions are responsible for 1.35 million deaths, and 20 to 50 million injuries every year globally (World Health Organization, 2013, 2018b, 2018a). In Canada, traffic collisions are the 10th leading cause of death, accounting for 1841 deaths, 9960 serious injuries and 154,886 total injuries in 2017 (Statistics Canada, 2017b, 2017a). As a vulnerable road user, cyclists are at much greater risk and suffer much higher mortality rates per distance travelled compared to other modes of transportation (Feleke et al., 2018; Garder, 1994; Pirlone & Candia, 2015). Globally, fifty percent of traffic deaths are vulnerable road users â cyclists, pedestrians, and motorcyclists (World Health Organization, 2013). Additionally, the total number of bicycle crashes is much higher than reported numbers as minor crashes are often not reported. As much as 85% of crashes may occur without the 5 involvement of an automobile and are thus not included in automobile crash data. Cyclists as a group are chronically over represented in traffic fatalities (Allen-Munley et al., 2004). We have discussed at length how implementing cycling as a primary mode of transportation is a cost-effective solution to reduce traffic congestion, improve public health, and decrease high GHG emissions. However, it is paramount that the safety of vulnerable road users (pedestrians and cyclists) is prioritized when implementing a modal shift towards cycling and walking. Cyclists, pedestrians and automobiles should always use infrastructure that is spatially and/or physically (barriers) separated. Mode separation is essential to maintain public safety. 1.2 The Decision to Cycle People consider many factors both consciously and unconsciously when deciding whether to cycle and where to cycle. Decisions can be influenced by current mood, personality, lifestyle, values, socio-economic situation, logistical situation, health & fitness, and the cycling environment. Someone who is late for an appointment may choose to drive instead of cycle if driving is faster. Some individuals may cycle to save money while others cycle to save natural resources and to prevent climate change. Someone who is physically fit would be more likely to cycle than someone who has fallen ill. A child forced to cycle to school on a road next to passing automobiles is less likely to cycle than one who has access to a separated bicycle path. Figure 1-1 breaks down some of the main factors that an individual may consider when deciding whether or not to cycle. 6 Figure 1-1.Breakdown of factors contributing to a decision to cycle This study focuses on analyzing the first factor and the fourth factor, the cycling environment and individual. The cycling environment encompasses all external physical stimuli which affect the desirability of the cycling experience. The individual encapsulates the features that make people unique decision makers and perceivers of their own experience. 1.2.1 The Cycling Environment The Cycling environment can be further broken down into six elements: Infrastructure, Weather, Bicycle, Vehicular Traffic, Other Road Users and Surroundings. The details are shown in Figure 1-2. 7 Figure 1-2. Elements within the cycling environment As a group, transportation engineers, planners and policy makers have varying levels of control over each element. Weather (2nd element) can mitigated by offering sheltered bicycle parking and shower facilities at work places. Conflicts between road users (5th element) can be mitigated by designing separate facilities for pedestrians and cyclists â mode separation. Rules that improve bicycle maintenance (ie. use of lights, reflectors etc.) can be enforced (3rd element). The physical features (1st element) and physical location (4th and 6th elements) of the bicycle infrastructure are within the most direct control of transportation engineers and planners and is the main focus of this study. 1.2.2 The Individual In some countries, such as the Netherlands, cycling has become a primary method of transportation. However, it can be a challenge to encourage the same degree of adoption in other cities, especially in North America. There are many barriers for an individual to overcome before he/she may choose cycling as their primary method of transportation. These barriers may include factors such as possessing a minimum level of physical fitness, cycling experience and the tenacity to endure adverse weather conditions. 8 Some of the most significant barriers are an individualâs personal perception of their own comfort and safety when cycling. Research has found that high â stress cycling environments (Furth et al., 2016), and high automobile traffic volumes and speed discourages cycling (Jacobsen et al., 2009). Some cycle routes can feel very unsafe, especially when adjacent automobile traffic is present without a physical barrier separating the automobile from cyclists. Other routes can be very uncomfortable with rough riding surfaces such as gravel or steep hills. In order for individuals to cycle, they must be able to tolerate a certain level of discomfort, and risk. What is perceived as risky for one person is not necessarily perceived as risky for another â The same can be applied to comfort. The feelings of comfort and safety are human perceptions, which can be difficult to quantify. Perceptions affect decision making significantly, therefore, determining the expected desirability of cycling infrastructure (i.e. bike lanes, separated bicycle paths etc.) can be challenging. The aim of this project is to address this challenge. 1.2.3 Comfort and Safety Within this thesis, Comfort is defined in as the physical and mental exertion one experienced while cycling â In other words comfort is a measure of oneâs exposure to stimuli that are a nuisance. Safety is defined in this study as the vulnerability and risk of injury while cycling â In other words safety is a measure of oneâs feeling of exposure to danger or harm. Desirability is defined in this study as the combination of a cyclistâs perceived comfort and safety. Hence, the desirability of a section of cycling infrastructure is simply an individualâs perception of the comfort and safety that it has to offer. It will be seen later that a variable called wellness is created to measure desirability. It is crucial to note, that this study measures the perception of safety and comfort. For comfort, perception of comfort is real comfort. However, the perception of safety is not the same as objective safety. Cyclists have been known for misjudging the safety of their actions and partaking in unsafe cycling behaviour such as cycling on the sidewalk or against the flow of motor vehicle traffic (Garder, 1994). For instance, cycling on a sidewalk can be very unsafe since automobile drivers pulling out of driveways are not expecting and/or cannot see a cyclist 9 on a sidewalk. However, some cyclists can feel very safe on a sidewalk in comparison to a bike lane â they feel more protected from passing adjacent automobile traffic when on a sidewalk. Nevertheless, perceptions influence the decisions people make every day. It can be assumed that the majority of cyclists will select cycling routes that they perceive to be comfortable and safe. Hence, engineers and planners are responsible to ensure that cycling infrastructure is both safe and also feels safe. It is paramount that cyclists are attracted to the safest routes and that a false sense of security is not created inadvertently. 10 2 Literature Review In light of the health, social-economic and environmental benefits of cycling transportation, planners and engineers are seeking ways to promote greater use of bicycles for commuting purposes. Promoting cycling comes with a host of challenges: investment, public support, and the perceived concerns for cycling safety, comfort, and practicality relative to driving (its most direct competitor). To increase ridership in North America, a better understanding of cyclistsâ perception is required so that more desirable bicycle infrastructure can be implemented. The challenge is a lack of reliable empirical tools that can evaluate planned and existing infrastructure and predict/infer the perceived safety and comfort of a rider. University of British Columbia (UBC) researchers at the Sustainable Transport Safety Research Laboratory (STS) collaborated with local industry to develop bicycle comfort and safety prediction models (BCSPM) to address these needs. In this chapter, previous related bicycle research is reviewed. 2.1 Research on Bicycle Level of Service Level of service (LOS) can be described as the qualitative measure of the performance of transportation infrastructure. Level of service takes into account not just traffic flow, but also safety, and comfort. Generally, bicycle LOS models tend to be focused on one of three aspects. The first is a focus on the geometric and functional compatibility of roadways for bicycle use, the second is a focus on estimating the objective safety of cycling infrastructure, and the final aspect focuses on the cyclistsâ perception of safety and comfort. Each aspect has its own merits; a model that focuses on the geometric and functional attributes of roadways for bicycle use can rapidly estimate the suitability of an existing road network for cyclist use. A model that assesses the objective safety of cycling infrastructure will be able identify sections of existing road networks that require remediation due to safety concerns. Such models can also be an aid in the planning and design of safer cycling infrastructure. Finally, a model that can predict a cyclistâs perceptions will be able to estimate which sections of an existing road network will be desirable to cyclists. Additionally, such a model can also aid in the planning and design of more desirable cycling infrastructure to promote cycling as a primary mode of transportation. Additionally, within these three aspects of focus there are a myriad of data collection and data 11 analysis methodologies. Bicycle LOS is a relatively new field of research and this literature review offers a chronological overview into some of the key models developed to assess bicycle level of service over the years. Bicycle safety modelling began as early as the 1980s and 1990s . Some of the early models include Davisâ Bicycle Safety Index Rating (BSIR) in 1987, Turnerâs Bicycle Suitability Score in 1997 (Turner et al., 1997) and Harkeyâs Bicycle Compatibility Index (BCI) in 1998 (Harkey et al., 1998). Turnerâs Bicycle Suitability Score (BSS) was developed by the Texas Department of Transportation (TxDOT) to assess the bicycle friendliness of Texas highways for the purposes of integrating it into the planning, design and maintenance of Texas roadways. Turnerâs Bicycle Suitability Score is a numerical value assigned to roadways ranging from -8 to 8. Higher numbers are better and 0 indicates the minimum desirable bicycling conditions. The BSS scores roadways based mainly on the geometric cross section and readily available data in roadway inventories. This was a simple approach that allows all roadways to be quickly assessed for a BSS. The 4 variables that are used in the BSS include the shoulder or travel lane width, average daily traffic volume for each lane, automobile posted speed limit and pavement surface quality. Each variable is assessed a score ranging from -2 to 2 by comparing its value to a table of pre- determined ranges of values for each criteria with the middle range representing the minimum desirable condition. The table is calibrated for intermediate and experienced cyclists. The scores of the four variables are summed to give the BSS which indicates equal weighting of each factor for simplicity. Within Turnerâs report he describes results of an email survey of cyclists in the United States which found that most cyclists included one or more of the 4 variables as being the most important factor in determining bicycle suitability. Furthermore it was found that cyclists preferred specific data on roadways rather than a suitability score which was difficult for them to decipher. Cyclists input was recommended for future improvement of the BSS (Turner et al., 1997). The BSS was a very good start for assessing the desirability of cycling infrastructure. Its strengths are that it is easy to implement, and accounts for many of the main factors that affect cycling infrastructure desirability. However, it can be further refined with more cyclist feedback, and the BSS considers only one type of infrastructure â namely sharing the road with automobiles or cycling on the shoulder. Furthermore, the four variables that were 12 assessed were assumed to be equally weighted and to be independent of each other. For instance, one can suppose that as shoulder widens, automobile speeds and volume would have less negative effect on bicycle suitability. Harkeyâs Bicycle Compatibility Index (BCI) was developed in 1998 in response to the goals of the United States Department of Transportation (USDOT) to double the amount of trips taken by walking and cycling and to also decrease the number of pedestrians and cyclist deaths and injuries in automobile crashes by 10 percent. The BCI was designed to provide planner and engineers a way to assess roadways for compatibility for sharedâuse between automobiles and cyclists for the purposes of evaluation, design and planning. Development of the BCI utilized a video survey where video footage of various automobile traffic conditions and roadway geometric layouts were viewed and assessed by participants. The methodology of using a video survey was validated in a pilot study and proved to be effective. The pilot study also identified the presence of driveways, right turning traffic and large/heavy vehicles as additional sources of cyclist concern. 202 participants were drawn from three cities, Olympia, Austin and Chapel Hill with ages ranging from 19-74. Participants were asked to view stationary video footage of roadways from a cyclist point of view with various levels of automobile traffic volume. Footage was taken from 10 cities across the United States in 5 different states, representing a range of geographical conditions. The roadways consisted of either a bicycle lane/paved shoulder or standard/wide curb lane. Roadways were selected so that a variety of automobile traffic speeds, lane widths, bike lane/paved shoulder widths and the number of automobile traffic lanes were represented. In total, 67 different sites were selected for the survey. Participants were asked to rate their overall level of comfort riding the roads shown in the video footage on a six-point scale as well as provide their ratings on the level of comfort specifically in regards to automobile traffic volume, automobile speed and the space available to ride the bicycle. A lower rated value indicates better riding conditions or a better level of comfort. Harkey et al. (1998) used regression modelling to develop the final BCI model shown below: 13 Equation 2-1. Bicycle Compatibility Index by Harkey et al. (1998) BCI = 3.67 - 0.966*(BL) - 0.410*(BLW) - 0.498*(CLW) + 0.002*(CLV) +0.0004*(OLV) + 0.022*(SPD) + 0.506*(PKG) - 0.264*(AREA) + AF Where BL indicates the presence of a bicycle lane or paved shoulder, BLW is the measure of the bicycle lane width in meters, CLW is the curb lane width in meters, CLV is the curb lane volume in vehicles per hour, OLV is the other lane volumes in the same directing measured in vehicles per hour, SPD is the 85th percentile speed of the traffic, PKG indicates the presence of parking with over 30% occupancy, AREA indicates the type of roadside development, and AF is a factor accounting for the presence of right turning vehicles, truck volume, and parking turnover. Additionally, Harkey et al. (1998) conducted a pilot study to develop a version of the BCI for assessing intersections using a total of 19 intersections in the study. Half the participants in the pilot study were asked to rate their overall comfort in riding through the intersection on a six-point scale. The other half of the participants were asked to provide comfort ratings on the volume of right turning traffic, speed of approaching traffic, width for them to manoeuvre through intersection and the clarity of signage and markings that guide automobiles and cyclists. Two main groups of cyclists were identified with Classification and Regression Tree (CART), the average comfort ratings were 3.49 and 4.10 and these two groups represented the commuters who had experience riding on major city streets and those who ride for recreational purposes. The BCI intersection model developed is shown below: Equation 2-2. Bicycle Compatibility Index for intersections by Harkey et al. (1998) BCI(int) = 2.22 - 0.76*(BL) + 0.49*(SHIFT) + 0.003*(RVOL) + 0.001*(TVOL) Where BL indicates the presence of a bicycle lane, SHIFT indicates the need for cyclists to shift left and cross a right turning automobile lane in order to travel through the intersection, 14 RVOL is the right-turn volume in vehicles per hour, and TVOL is the total volume in vehicles per hour. The model had an R squared value at 81 percent. The video survey approach by Harkey et al. (1998) is quite ingenious as it allows many cyclists to give their perceptions as participants from a safe environment. If this methodology is repeated today, advances in online video-sharing applications will allow thousands of people to participate instantly. In a completely different approach, Leden et al. (2000) developed a risk index model that incorporates expert judgments into a Bayesian error model. To incorporate expert judgment a quantitative expert judgment model was utilized. A survey was sent to 22 people considered experts on the safety of cyclists. 13 responses were received from experts in Europe, North America and Australia. The survey gives a scenario where a right turning automobile nearly collides with a cyclists proceeding through an intersection where the cyclist has right of way. The experts are asked a series of questions in an 11 page survey including questions related to risk of collision, and risk of injury given a series of variables include the speeds of both the cyclists and the automobiles and volumes of bicycle and automobile traffic. The model itself comprises of three parts, the risk index model, an expert assessment model and a risk attribute model. Risk index model defines the measure of risk based on attributes and parameters input into the model. Risk was estimated in a multiplicative way allowing each attributeâs effect on risk to be evaluated separately. The attribute model estimates the attributes on which the risk is calculated and the expert assessment model incorporates the assessments of experts using the output from the attribute model. As the attributes are a random variable in this approach, Monte-Carlo simulation was used to estimate the risk probability distribution. Leden et al. (2000) applies the model to evaluate and compare the safety of a new bicycle crossing design featuring a raised crossing painted with more visible brighter colours to the existing bicycle crossing. The model estimated a 20% improvement to safety for the new design. The three approaches discussed thus far all provides certain contributions and advantages. The BSS developed by Turner et al.(1997) provided a means of rapidly quantifying 15 the geometric and operational conditions of a roadway for general bicycle use suitability. Harkey et al. (1998) developed the BCI, which estimates a cyclistsâ perception of comfort relating to roadway geometric and operational conditions utilizing video footage as a means of collecting survey responses. Finally, Leden et al. (2000) was able to utilize expert judgement to assess objective safety concerns. This is very valuable since a cyclistsâ perception of safety does not necessarily translate directly to objective safety. In contrast, Allen-Munley et al. (2004) and Petritsch et al. (2006) utilizes bicycle and automobile crash data to develop objective cycling safety models. A model developed from real crash data is extremely valuable because it removes speculations and perception from the analysis and focuses on results drawn from historical records thus offers arguably the most important approach to developing a cycling safety model â using real crash data. Both Allen- Munley et al. (2004) and Petritsch et al. (2006) used unique approaches to develop their models. Allen-Munley et al. (2004) constructed an ordinal logistic route safety-rating model that estimates injury severity using bicycle crash data collected from Jersey City, New Jersey from 1997 to 2000. The study was launched in response to renewed interest in promoting cycling for its benefits to the environment and public health. Thus the study focuses on non-discretionary urban adult commuting cycling trips during peak-hour. To work around the lack of data regarding vehicle kilometres travelled (VKT) for bicycles which would be necessary to calculate crash rates as a safety measure, Allen-Munley et al. (2004) approaches the problem using injury severity as injury severity is independent of bicycle volume. The model predicts the expected injury severity of a crash that would occur in a location with the reasoning that safe routes are as those with the less severe crashes. Data was collected from The New Jersey Department of Transportation (NJDOT) traffic crash database, as well as police reports, zoning maps, transit maps, local municipal codes and internal records. From 1997 to 2000, Jersey City reported 329 bicycle crashes. Fifteen of the records were eliminate due to insufficient data leaving 314 crashes for study. Allen-Munley et al. (2004) noted that although crashes involving automobiles are generally the most severe, they constitute only a small fraction of all bicycle crashes. Therefore, there is bias towards injuries of 16 greater severity. Thus, the model does not estimate absolute injury severity. However, the model can still be used as a tool to assess the comparative safety of cycling infrastructure. The ordinal logit model was derived using the maximum likelihood estimation (MLE) method and backward elimination process for parameter selection. Two models were developed, with the first fitted to a 90% confidence level and the second fitted to a 95% confidence interval. The nine parameters in the first model included Daylight (night versus day), Density (population density), Grade (being on a slope), Hwy (being on a highway), One Way (being on a one-way street), Pave (pavement condition), Truck (presence of trucks), Volume (automobile volume) and Width (road width). The second model fitted to 95% included only Grade, One Way and Width. Allen-Munley et al. (2004) yielded valuable results backed up by objective data. It was found that being on a one-way street, a slope and/or a highway increased the severity of crashes. Increased road widths and population density increased the estimated severity of crashes as was the presence of trucks. At first a wider road leading to an estimated increase in severity seems counterintuitive, however Allen-Munley et al. (2004) reasons that wider roads allows for greater automobile speeds, more automobile over-taking manoeuvres and possibly a false sense of security from the cyclist. Increased population density increases the likelihood of automobile- cyclist interactions and thus crashes while slopes and trucks both decrease line of sight with trucks themselves being much less manoeuvrable. In contrast, increased automobile volume decreased operating speed and thus also crash severity. Better pavement quality also decreased crash severity. The model and methodology presented by Allen-Munley et al. (2004) is a tremendous contribution of knowledge, and brings insight to what factors makes a cycling route objectively safer, less safe and what factors can be ignored. The motivation is that if we are to promote a modal switch, safety must be a priority. Petritsch et al. (2006) develops one of the most interesting cycling safety models on behalf of the Florida Department of Transportation District One Planning Office. The purpose was to determine whether a side path (separated cycling path) would be an appropriate alternative to an on-road cycling facility (bike lane) from a safety perspective. The statistical 17 model that was developed used operational and geometric data, user counts, and crash data for 21 roadway sections throughout Florida. The model predicts relative bicycle â automobile crash rates between on-street and side path bicycle facilities and was simply one component of seven steps that were laid out to determine the appropriateness of a side path. The full assessment includes an analysis of travel demand, feasibility of a side path, alternative routes, probable destinations, the design of the side path itself and its termini, the level of comfort and safety the side path would provide cyclists and also the objective safety of the side path itself (statistical model). Pearson correlation and factor analysis was conducted on geometric and operational data in regards to the difference in crash rates followed by stepwise regression. The final operational and geometric variables selected as independent variables for the model are the width of the side path, the distance between the side path and the road way, the posted speed limit on the road way, and the number of lanes of the road way. Other variables were not included due to inadequate correlation with the dependent variable (difference in potential crash rate) or collinearity with other strongly correlated variables. The final model shown below achieved an R squared value of 0.81 at 95% confidence levels. Equation 2-3. Sidepath Safety Model by Petritsch et al. (2006) â= ððð ð ð ð ï¿½6.311 â 0.465ððð ð ð ð ï¿½ + ð·ð·(0.015ðð â 0.685) â 1.528 ln(ð¿ð¿) â 17.555 Where Î is the difference between the predicted crash rate for a roadway and a sidepath along that same roadway (positive values indicating a side path performs better and has less crash rates than the road way). Wsp is the width of the side path, D is the distance between the side path and the road way, S is the speed limit of the adjacent roadway and L is the number of lanes on the adjacent roadway. A bicycle lane width of 7 feet was found to be provide maximized safety, although Petritsch et al. (2006) noted that this can be too narrow for some road users (ie. inline skaters) and will not allow cyclists to pass each other. The main reason for 7 feet being an optimized width is that it slows down riding speed so that cyclists do not enter intersections between a road 18 way and a side path too quickly. It was noted that a chicane can be used as an alternative, or a narrowing of the sidepath lane width near the intersections. It was found that at lower roadway speeds it was safer to bring the side path closer to the roadway rather keep it further, and at higher speeds it was safer to bring the side path further rather than closer. If side paths are located far from roadways at midblock they can be moved closer to roadways at intersections Petritsch et al. (2006). It can be assumed that this is because it is safer to be at a designated roadway intersection when crossing an intersecting roadway while riding a sidepath rather than crossing at midblock. It is clear from the results found by Petritsch et al. (2006) that managing the expectations of road users leads to safer infrastructure design. Further it was found that when roadways speeds are slow side paths could be more hazardous, but when the roadway speeds were fast side paths were more safe. Finally, Petritsch et al. (2006) found that as the number of road way lanes increase, a side path becomes increasingly less safe compared to on-street cycling facilities as drivers begin to focus on the complex roadway environment and motor vehicle travel lanes and less on the side of the street. Research such as those by Allen-Munley et al. (2004) and Petritsch et al. (2006) is paramount as they focus on determining the objective safety of cyclist infrastructure using real crash and injury data. The study by Leden et al. (2000) is particularly valuable as it can be used to predict the safety of proposed bicycle crossings designs. In subsequent years, more sophisticated data collection techniques and sensors allowed bicycle research to progress considerably. Yao et al. (2011) and Winters et al., (2011) used interview surveys to assess the perceived comfort of cyclists and linked the perceptions of cyclists to geometric and operational infrastructure variables. Yao et al. (2011) developed a partial proportional odds model to identify the most significant contributing factors for cyclists comfort. The model was developed using comfort ratings provided by interviewing a total of 730 cyclists riding on 29 designated bicycle roadway segments in Nanjing, China. The researchers found that the geometric planning of bicycle roadways has a significant impact on rider comfort. The study found that the width of the bike path, separation from pedestrian lanes, the number of uncontrolled access points, the presence of bus stops and the land type adjacent to the bicycle path were the most significant variables. 19 Winters et al., (2011) conducted a vast survey of 1402 potential and current cyclists in Metro Vancouver. Seventy three different cycling motivators and deterrents were assessed in the surveys. The top motivators of cycling included being away from noise and pollution and being on a route with beautiful scenery. Flat routes were also given preference as well as shorter travel distances below 5 km. Integrating cycling with public transportation was a significant motivator as was the availability of secure bicycle storage facilities. On the other hand, the top deterrents included the need to carry bulky items, adverse weather, road debris, risk of collisions, and high automobile traffic volume and speed. However, the findings are consistent with many other studies utilizing experiments or naturalistic studies. The top deterrents and motivators that are identified by Winters et al., (2011) are important because it represents the most effective places to invest resources. The research by Winter's et al., (2011) and Yao et al. (2011) revealed valuable insight into the most important factors which contribute to a cyclistsâ perception of comfort and safety. However, the studies relied solely on the stated preferences of cyclists collected from surveys. This approach omits collection of real-time riding data; however, it has the advantage of allowing a large sample size to be collected more easily. The following studies involved experiments conducted utilizing instrumented probe bicycles (IPB) in order to record real-time individual riding data for analysis in conjunction with the survey results of participating cyclists. Joo & Oh (2013) developed the Bicycle Monitoring Index (BMI) in Korea by conducting experiments with an IPB. The BMI utilizes binary logistic regression modelling and fault tree analysis to evaluate the level of service on a given route by assessing two aspects of the bicycle environment: safety and mobility, where failure of either aspect is a failure of the bicycle environment or facility (infrastructure). Data was collected on 1.4 km route with four links (segments of road, two bicycle/car links and two bicycle/pedestrian links). Twenty university students volunteers were asked rate the links as a dichotomous variable (satisfactory or unsatisfactory). 20 To evaluate safety, Joo & Oh developed the Cycling Stability Index (CSI) shown in equation (1): Equation 2-4. Cycling Stability Index by Joo & Oh (2013) Cycling Stability Index (CSI) = Pr(STAn = 1|Xn) = exp(f(Xn,β)1 + exp[f(Xn,β)] The variable STAn represents cycling stability. STAn = 1, indicates satisfactory stability and a 0 represents unsatisfactory stability. The CSI provides the probability that STAn = 1 or satisfactory, dependent on the independent variable Xi and coefficient β. A threshold CSI value (k1) was chosen to represent a failure event should the CSI value be less than k1. To evaluate mobility, Joo & Oh (2013) used bicycling speed and a threshold speed of 5 km/hr (k2) was chosen. The reasoning behind this threshold originates from the âcomfortableâ pedestrian walking speeds, (typically 5 km/hr). The premise is that if the bicycle environment forces a cyclists to travel under the âcomfortableâ walking speed (5 km/hr), it has failed to meet the minimum requirements of cycling mobility. From Fault Tree Analysis, final BMI was given by Equation 2-5, where the maximum (higher the better) score a segment can achieve is a BMI index rating of 1. Equation 2-5. Bicycle Monitoring Index by Joo & Oh (2013) BMI = Ï = 1 â {1 â Pr(CSI < k1)} â {1 â Pr (Speed < k2)} Yamanaka et al. (2013) in Japan conducted research that is arguably one of the most comprehensive studies in this review. The study uses an IPB to study cyclist comfort and safety with the aim to determine key factors that influence a cyclistsâ perception. In their study, Yamanaka et al. (2013) developed models to address five measures of comfort and safety: 1. safe sense to other traffic 2. discomfort in roughness of road surface 3. discomfort of narrow bicycle space 21 4. comfort of cycling speed 5. total level of comfort In what must have been an enormous effort, Yamanaka et al. (2013) conducted 1432 IPB trials on a total of 74 street segments in seven cities across three countries. Four to six riders rode each segment between four and eight times. In total each segment was ridden between 16 and 32 times obtaining a total of 1164 valid samples. To evaluate the five measures of comfort and safety, participants provided ratings Likert scale (Five points scale) using a microphone. Parameters such as speed, braking, acceleration, and traffic volume were evaluated using more than 30 independent variables. Pedestrian, cyclist, and vehicle density was assessed with video camera footage. To estimate density, road-users (pedestrians, cyclists and vehicles) within 10 m in front of the bicycle were counted at four second intervals. Vehicles beside bicycle lanes were also counted depending on the bicycle lane type. Distances on the video were estimated using the size of people, bicycles and cars. Final flow rate was estimated using assumed bicycle, pedestrian, and vehicle travelling speeds. Factor analysis and correlation analyses were utilized to observe the relationships between the independent variables themselves as well as between independent variables and the five safety and comfort measures. Factor analysis grouped all variables into one of six factors: speed stability, stop, vibration, steering, density, and braking. Variables within groups are highly correlated with each other. The correlation between each variable and the provided ratings for each comfort and safety measure was utilized with the results of the factor analysis to select the variables in the regression model. The ordinal logit regression model was utilized to quantify the β values for the each selected variable. Equation 2-6. Bicycle Level of Service Model by Yamanaka et al. (2013) Prob(score â¤ K ) = 1/ {1 + exp (âÎ²o + ï¿½ βiXi)n i=1 } The final models provide a Level of Service (LOS) index for each of the five comfort and safety measures. Yamanaka et al. (2013) concluded the speed of cycling in relation to desired cycling speed, the standard deviation of steering angle (wobble), bicycle vertical vibration level 22 (pavement roughness), braking behaviour, traffic density in front of the cyclist, and the distance to side objects (buffer zone) can be useful in estimating the level of service from the cyclists perspective. Yamanaka et al. (2013) and Joo & Oh (2013) was able to determine a method of quantifying a bicycle level of service based on perceptions reported by their riders and using real-time data collected from the IPBs. However, both researchers utilized only a small number of participants. In order to apply the results of the research to a population (ie. a city or town), a larger sample size would be beneficial. Research from the Czech Republic by Bil et al (2015) utilized an IPB to study the comfort of cyclist paths by relating vibration to comfort. The researchers developed a reliable comfort index (CI) based solely on the vertical acceleration of the bicycle during a ride. This research was able to utilize a larger sample size of 33 cyclists in the study in which a correlation of 0.96 was found between the subjective track quality and the comfort index. The surfaces assessed included cobble stones, asphalt of various conditions, interlocking concrete pavement. This research was a vast leap forward in assessing cycle track quality and roughness, which until recently has been commonly classified on a Likert scale. 2.2 Research on Cyclist-Automobile Interactions Importance is often given to how cyclists may be affected by the behaviour of drivers; however drivers are also affected by the behaviour of cyclists. Understanding the interaction between cyclists and drivers is crucial in designing transportation infrastructure that maximizes the objective safety and the perceived safety and comfort for both cyclists and drivers. The following researchers have focused their efforts in this area. Walker (2007) conducted research focused on the relationship between the manner in which drivers overtake cyclists based on the appearance and behaviour of the cyclist. In his paper, Walker (2007) reveals that in a British study of nearly 5000 cyclists collisions between vehicles and bicycles occurring during an overtaking manoeuvre account for less than 4% of reported incidents. However, they comprise 13% of the official recorded collisions between 23 cyclists and automobiles within the same time period and region in the aforementioned British study. This suggests that collisions that occur when a car is passing a cyclist draw more police attention (Walker, 2007). We could assume that collisions that occur in a passing manoeuvre are more severe due to the greater speed of collision compared to collisions that occur at intersections where both the automobile and the cyclist are likely moving slower. Walker (2007) recognizes that research literature focuses more on the frequent collisions at intersections. Walker (2007) focuses on three factors that affect driver overtaking behaviour: (1) Rider position from the edge of the road, (2) helmet use, and (3) vehicle type (trucks and busses vs cars). For his study, The study utilized an IPB equipped with a Massa M-5000/95 sensor to measure the proximity of passing vehicles in the immediately adjacent lane. Walker used SPSS exploration to find outliers with priori analysis used with G* Power. ANOVA was used to find the correlations between the factors under analysis and passing proximity. Passing distances in a sequence of cars was found to be independent. In regards to riding position, it was found that contrary to previous belief, riding further out towards the automobile lane lead to closer proximity in cars overtaking. It was also revealed that wearing a helmet is associated with closer passing distances. Long vehicles have less ability to give cyclists room therefore are responsible for more collisions. Probably the most interesting of Walkerâs findings is that if one appears as a woman from behind, drivers will give more passing room. Walker's (2007) findings are significant because it shows that cyclists can have a significant effect on their perceived surroundings. In fact, it is very possible that cyclists due to their appearance or behaviour will affect how drivers behave and therefore create a different cycling environment around them. This should be taken into account or at least considered when analyzing data from future experiments. In other words, on the same road segment, a professional male cyclist on a racing bicycle sporting an aerodynamic helmet will likely experience small lateral passing distance from automobiles, while a casually dressed female cyclist would experience larger lateral passing distances. In essence, the cyclist themselves will create the environment around them to some extent. Llorca et al. (2014) took the study of vehicle overtaking as previously studied by Walker (2007) to a greater level. Focusing on seven two-lane rural roads in Spain, the study looked at 24 2928 overtaking manoeuvres. Two different instrumental bikes were used: a mountain bike and a racing bike. As with Walkerâs study one cyclist was used for the entire experiment, however, in this study conducted by Llorca et al. (2014) a professional cyclist was used. Similar to Walkerâs study, Llorca et al. (2014) observed vehicle type, vehicle speed and over taking lateral overtaking distance, but on rural roads which have no specific lane markings and with much more advanced equipment compared to Walker (2007). Llorca et al. (2014) used three cameras on their IPB in order to have perspectives on the front view, back view and side view. Sensors consisted of a GPS, a Racelogic VBOX data logger a T100 laser system in order to measure the speed of the overtaking manoeuvre, and S200 laser rangefinders to measure the lateral distance from the bicycle to the car during the overtaking manoeuvre. Each segment was test ridden twice, once with a mountain bike and then with a racing bike. The roads covered a diverse range of geometric characteristics such as lane widths, shoulder widths, rolling and flat terrain as well as various traffic volumes (AADT). Overtaking vehicle categories were classified as passenger car, small van or SUV, large van, or truck. Several methods were examined in order to combine the different variables of speed of overtaking vehicle, type of overtaking vehicle and the lateral distance, as it was found that cyclist perception was mostly affected by an interaction between these variables. The most significant relationship was found between the perception of risk and V^2(3-d). Where v is the velocity of the vehicle and d is the distance between the vehicle and the bicycle. This formulation indicates a multiplying relationship between the velocity and lateral passing distance of the overtaking automobile, with velocity having an exponential relationship to risk and lateral passing distance having a linear relationship with risk. This formulation also shows that at either a distance greater than 3 m or at velocities close to 0, there would be minimal perceived risk. Additionally, Llorca et al. (2014) discovered that the percent of heavy vehicles encountered correlated well with risk perception. Perhaps the most important result is objective data showing that road standards for lateral passing distance should be directly related to the speed of the road. Llorca et al. (2014) was able to estimate the separation distance required at different automobile passing speeds in order for cyclists to perceive the same level of risk. The presence of heavy vehicles greatly increases perceived risk and overtaking lateral distance standards should reflect this. The effect of opposing vehicles was not present, however, the 25 presence of opposing vehicles slowed down the adjacent passing vehicles. The lateral spacing and speed variable was greater on tangent sections of the road. The study by Llorca et al. (2014) provides valuable insight on how road safety standards and guidelines should be re-written in terms of specifying mandatory lateral spacing to cyclist during a vehicle overtaking manoeuvre . It also sheds light on possible lane width design guidelines. The study did not assess the effects of different bicycle infrastructure, such as the existence of a bike lane or a physical median (separated paths or bike paths). Therefore, further study in this area will need to be conducted to determine the effect that specific infrastructure has on cyclist risk perception. The main contribution of this research is the characterization of lateral clearance, motor vehicle type, and speed with the ridersâ subjective perception of risk in a rural setting. However, as studies in rural settings cannot always be easily transferred to an urban cycling setting, further work is still necessary in this area. Additionally, only one professional cyclist was use â for a more complete understanding of cyclist perceptions, a wide range of cyclists is needed. 2.3 Research on Cyclist Typology As research evolved, it became increasingly clear that not all cyclists can be treated equally. Experience has shown that avid cyclists prefer fewer turns and undulations and do not mind riding in traffic, while most other recreational cyclists will be discouraged by the prospect of riding near traffic. Being able to differentiate between cyclist types and understand their different behaviours and habits will allow for better decision making for planners and engineers when designing and implementing new cycling infrastructure or policies in hopes of increasing ridership. During the development of the Bicycle Compatibility Index (BCI), Harkey et al. (1998) was able to distinguish different types of cyclists within the participant population. Two-step clustering methodology was performed based on questions related to riding patterns such as the number of trips, distances ridden per week, and trip purpose. These three variables proved to be the most important to assess experience level. Harkey et al. (1998) identified three types of cyclists: the experience commuter bicyclists, experienced recreational bicyclists and casual 26 recreational bicyclists. The responses for experienced cyclists were comparable during the development of the BCI, whether they were commuter or recreational. There was a different between 0.3 to 0.5 points on a 6 point comfort scale between the casual cyclists and the experienced cyclists (Harkey et al., 1998). Also among the first to discuss cyclist typology was Mette Jensen (1999) who combined cyclists and public transit users into three categories: 1) cyclist/public transit users of heart â These commuters choose cycling or transit consciously and wishes not to own a car. 2) cyclist/public transit user of convenience â These commuters choose cycling or transit because it is the most convenient choice to them. 3) cyclist/public transit user of necessity â These commuters choose cycling or transit because they cannot own a car or are unable to drive one. This is a simple and useful way to approach cyclist typology in terms of mode choice from conscious choice, utility or mode captivity. However, there is likely much overlap of choice, utility and mode captivity within each group. Although this approach may tell us a main reason why someone cycles, it may not reveal what kind of cyclist they are (whether they are very confident, or are timid) and how they would react or feel in a situation. While Jensen in 1999 sorted cyclists by mode choice, Bergström & Magnusson in 2003 decided to separate cyclists by cycling frequency and winter cycling frequency. , Bergström & Magnusson (2003) created four categories: winter cyclists, summer-only cyclists, infrequent cyclists, and never cyclists. What they discovered was that winter cyclists value exercise, summer cyclists were negatively affected by adverse weather or road conditions, and that infrequent and never cyclists are similar to group two from Jensen's research, and value travel time. While Jensen (1999) assessed at cyclists from a mode choice perspective, and Bergström & Magnusson (2003) assessed cyclists from a cycling frequency perspective, Gatersleben & Haddad (2010) analyzed the perceptions that both cyclists and non-cyclists had about a âtypicalâ 27 cyclist. Gatersleben & Haddad (2010) utilized factor -cluster analysis to help them identify four main types of cyclists: responsible cyclist, lifestyle cyclist, commuter and hippy-go-lucky (not happy-go-lucky). This is an important study because attitudes towards certain modes of transportation has been found by Handy, Cao, & Mokhtarian (2005) to have a greater effect on mode choice than the built environment. Furthermore, Daley & Rissel (2011) found that attitudes and perceptions about cyclists can have a significant effect on motivating new cyclists to take to the road. In short, these two studies suggest that if cycling is made the cool, sexy and responsible way to travel, this would significantly encourage mode shift to cycling. Although these cyclists types that Gatersleben & Haddad (2010) identified may look and behave differently in the minds of the respondents, and may have similar cycling motivators, they could potentially be equal in the eyes of other cycling typologies such as that of Jensen (1999) or Bergström & Magnusson (2003). Furthermore, they may have equal tolerance of adverse traffic, infrastructure conditions or weather, making them functionally the same cyclist from a safety and comfort perception perspective. Probably the most famous cyclist typology is Roger Geller's in 2009 developed for the City of Portland. Roger Geller (2009), divided cyclists into four groups according to both cycling frequency and attitude. The four groups are: âNo way, no howâ, âInterested but concernedâ, âEnthused and Confidentâ and âStrong and Fearlessâ. The âStrong and Fearlessâ constitute those who will cycle regardless of the road conditions. Within the city of Portland Roger Geller (2009) estimates that fewer than 0.5% of the population are âStrong and Fearlessâ cyclists. The âEnthused and Confidentâ cyclists are estimated to make up 7% of Portlandâs population and are those who are comfortable to share the road with automobiles, but would prefer to cycle on dedicated cycling infrastructure and have an appreciation for bicycle lanes and bicycle boulevards. The âInterested but Concernedâ group is estimated to make up 60% of the population of Portland and includes cyclists that would ride if they felt safer on the roads. They require slower automobile speeds, and volumes on quieter streets and separated cycling infrastructure. Finally, theâ No way, no howâ group are uninterested or unable to cycle and are estimated to make up approximately 33% of the population of Portland. 28 Some of the benefits of this categorization are that it is based on the comfort levels when cycling on different types of infrastructure and streets. This cyclist typology was analyzed by Dill & McNeil (2013) who interviewed 908 people and asked them to rate on a 4-point scale how comfortable they would feel on different bicycle riding infrastructures and scenarios. Dill & McNeil (2013) used the responses to separate the participants into the four categories developed by Roger Geller (2009). Next, Dill & McNeil (2013) classified the cyclists based on cycling frequency into non-cyclists, recreational cyclists and utilitarian cyclists. The interesting but also non-surprising result from their study was that oneâs cycling comfort was related but did not determine oneâs cycling frequency. Across all of Roger Geller's (2009) categories non- cyclists, recreational cyclists and utilitarian cyclist were represented, with the split being rather even across all of Roger Geller's (2009) categories. Even within the same classification of cyclists interesting results were observed. For example, Dill & McNeil (2013) found that in a typical winter month 10% of the interested but concerned utilitarian cyclists cycled 20-31 days while none of the strong and fearless utilitarian cyclists cycled 20-31 days. The in-depth analysis by Dill & McNeil (2013) shows that there is more than just cycling comfort and cycling infrastructure at play when it comes to determining a personâs cycling frequency. Logistics, finances, health and habit, all play a role as discussed earlier. A recent cyclist typology developed by Damant-Sirois et al. (2014) aims to address the short comings of previous cyclist typologies by incorporating cycling determinants such as a personâs preferences, background, motivations, and deterrents. Motivations and deterrents may include factors such as weather, influence of peers, and childhood cycling encouragement from parents. The goal of this new cyclist typology aims to help provide better insight on a cyclist in multiple dimensions so as to provide a better policy making tool in order to allocate resources to increase cycling mode share and frequency. Damant-Sirois et al. (2014) suggests splitting cyclists into four categories, all of which have their own specific riding behaviour, motivations and deterrents. Damant-Sirois et al. addressed 35 variables based on previously identified determinants of bike use; many of which such as separation from traffic, speed, traffic volume and weather conditions are echoed by many other studies (Winters et al., 2011; Lee et al., 2014). 29 After using principle component analysis and k-means cluster analysis, cyclists were split into four types using seven different dimensions: 1. Dedicated cyclists 2. Path-using cyclists 3. Fairweather utilitarians 4. Leisure cyclists The dimensions assessed are: 1. Weather and effort 2. Time efficiency 3. Dislike cycling near cars 4. Bicycle route infrastructure 5. Peer and institution encouragement 6. Cycling identity and enjoyment 7. Parental encouragement The contribution Damant-Sirois et al. (2014) made with this new typology allows policy makers to better understand the cyclists and provides more tools to increase cycling mode share. For instance, Damant-Sirois et al. (2014) identified four main interventions for increasing cycling: encouragement, stress-free networks, fast direct route, and extreme conditions alleviation. Different strategies will help encourage more cycling from different groups. For instance, a fair weather utilitarian will respond well to encouragement such as initiatives at school or work, educational campaigns, free bike sharing memberships or showers installed at work (Damant-Sirois et al., 2014). However, a dedicated cyclist would not need such encouragement and instead will respond best to faster direct routes or to have extreme conditions alleviated. Damant-Sirois et al. (2014) introduced a typology that is very suitable for a holistic approach that incorporates urban planning as well as engineering design, and community wide sustainable development. It should be firmly noted that the typology that Damant-Sirois et al. (2014) created is very valuable and is what is needed to have a whole system and lasting 30 approach to increasing cycling mode share. It will be crucial that any comfort and safety prediction model produced consider cyclist type. Perhaps the newest addition to cyclist typology comes with the introduction of a new mode of bicycle transportation in the form of electric-assist bicycles (e-bikes) which change cyclistsâ behaviour significantly. Dozza (2014) has found that riders on e-bikes have vastly different behaviours that are similar to avid cyclists than regular cyclists. In fact, he argues that they could change the current state of traffic as it can be considered a completely new mode of transportation. The main differences are that cyclists on e-bikes tend to travel faster, farther and take more risks. In addition to e-bikes being heavier and possibly having less manoeuvrability, cyclists on e-bike consider fewer barriers like slopes and prefer road over bike paths. In 2015, a large series of experiments were carried out on 59 participants riding classic bicycles (c-bike) and electronic assist bicycles (e-bikes), by the Dutch Road Safety Research Institute (SWOV) in The Hague, Netherlands (Vlakveld et al., 2015). Both the c-bikes and e- bikes were IPBs developed jointly by SWOV and the Delft University of Technology (TU Delft). The data collected from these experiments are being applied to study nearly all aspects of bicycle research including speed choice and rider mental work load (Vlakveld et al., 2015), rider training and rider behaviour. Research by Vlakveld (2015) has confirmed the results found by Dozza (2014) that e-bikes users travel faster, more frequently and take longer trips, increasing their exposure to potential collisions. As e-bikes are increasingly popular, especially with older cyclists who may have otherwise ceased cycling all together (Vlakveld et al., 2015) they are an important cyclist type to address on future bicycle research. In summary, it is paramount to consider the effects of cyclists typology as a part of all future cycling studies, as cyclists of different typologies have unique riding behaviours (Damant- Sirois et al., 2014; Dill & McNeil, 2013; M Dozza & Piccinini, 2014; Gatersleben & Haddad, 2010; Geller, 2009; Vlakveld et al., 2015). Furthermore, it is has been demonstrated that cyclist appearance and behaviour will affect driver behaviour in turn (Llorca et al., 2014; Walker, 2007). These interactions will affect the use, safety and comfort perception and objective safety of any bicycle infrastructure â as well as the efficacy of any safety policies. 31 2.4 Research on Route Choice In 2009, Sener et al. evaluated the factors that influence bicycle route choice by looking at the following 6 factor categories: 1. Bicyclistsâ characteristics 2. On-street parking 3. Bicycle facility type 4. Roadway physical characteristics 5. Roadway functional characteristics 6. Roadway operational characteristics A web based bicycle survey was used to obtain the stated preference of the participants and was disseminated through cyclist groups as well as university listservs. A total of 6484 choice occasions were assessed from a total of 1621 individuals. 814 of the individuals cycled for commuting purposes (801 of those also used their bicycle for non-commuting purposes). Eight hundred and seven cyclists used their bicycles only for non-commuting purposes. The sample was found to be skewed slightly towards males and away from young adults. No difference in commute characteristics such as travel time and distance were found among those who commute. The results of the research by Sener et al. (2009) indicated that all cyclists prefer routes with no on-street parking â parking reduces sightlines, gives less room to manoeuvre and is a safety risk. All cyclists except those between the ages of 18-24 preferred cycling in areas where automobiles were parking on an angle over being parallel parked. Angled parking gives more room for manoeuvring and allows better visibility for cyclists who can see vehicles that are backing out. Parallel parking also creates more conflicts as the process of parallel parking blocks the travel lane for cyclists for a longer period of time than angled parking and thus forces the cyclist to travel around the vehicle that is parallel parking or to wait until the cyclistâs path is cleared of the parallel parking vehicle. In addition, further conflicts with cyclists are introduced as drivers and passengers enter and exit a parallel parked vehicle due to car doors opening into the path of travel for the cyclist. This is a conflict that would not occur in an angled parking 32 situation due to the orientation of the parked vehicle which prevents opening vehicle doors from swinging into the cyclistâs path. Interestingly, 18-24 year olds are indifferent to the type of parking. Male cyclists are more likely to avoid routes with parking . It was also found that parking is more of a factor for longer commute trips with distances greater than 5 miles. This may be due to the fact that when travelling longer distances, watching for the potential movements of a parked car can become mentally draining for cyclists. When it comes to bicycle facility characteristics, the study looked at 5 options: 1 3.75 feet bicycle lane 2 6.25 feet bicycle lane 3 No bike lane 10.5 feet wide outside lane 4 No bike lane 14.0 feet wide outside lane 5 No bike lane 17.5 feet wide outside lane No difference was found between a 3.75 and 6.25 feet bicycle lane and results show that bicyclists may prefer a general purpose lane. This may be due to the fact cyclists dislike the feeling of being confined within their own lane. Sener et al. (2009) does warn that this result may be due to the fact that many participants are drawn from bicycle listservs where the mentality vehicular bicycling is may be more common (ie. Riding bicycles as if they are automobiles). It should be noted that perhaps non-bicyclists may cycle if more bike lanes were available. One of the most significant finds by Sener et al. (2009) is that the continuity of bike facilities was very important. Parallel parking counted as creating a discontinuity in biking facility. This sheds light for planners and engineers on how to design roads to best facilitate bicycle movement. When considering roadway characteristics, Sener et al. (2009) found a slight preference for moderate hills- especially for non-commuting cyclists. Commuting female cyclists avoided steep hills while commuting male cyclists marginally prefer steep hills to flat terrain, but prefer moderate hill to steep hills. Non-commuting female cyclists are indifferent to steep and flat terrain, but prefer moderate hills. Non-commuting male cyclists prefer steep hills to moderate hills and prefer hills over flat terrain. These findings are interesting as one would expect cyclists 33 to prefer flat terrain over hills. A Higher number of traffic controls were also found to be less desirable; however, males and experienced cyclists are not as affected by them. When it comes to roadway function characteristics, higher traffic volume and speed were not desirable. Bicyclists that are commuting are generally more sensitive to this while travel time affects people differently. A similar study in Vancouver, BC by Winters et al. (2010) examined the differences between the theoretically shortest path taken by a commuter versus the path actually travelled. The study assessed both cars and bicycles examining 50 trips for bikes and 67 trips for cars. Similarly to Sener et al. (2009), the study collected data through interviews and surveys. Variables that were assessed included total distance, road type, slope steepness and the built environment. Bike facilities such as stencils, signs and bike activated signals along a path were summed using Hawthâs Count Points in the ArcGIS Polygon tool in a detailed Shape file. The study assessed the number of connecting roads as a ratio of four way intersections against all others intersection types. Land use was split into nine categories: agriculture, commercial, education, entertainment, industrial, office park, single-family residence, and multifamily residence. Road classes were split into 5 categories: highway, arterial, collector, local and trail. Differences in trip distance and the environmental metrics were measured using paired t-tests and sign-ranked tests. The study found that bike trips were 360 m longer than the shortest possible route, and that 75% percent of trips are within 10% of the distance of the shortest route. For bike commuters, actual trips contained significantly more bicycle facilities such as traffic calming features, stencils and signage compared to the shortest path. The average path had 10 more stencils, seven more signs and two more traffic calming features than the shortest routes. Hills, air pollution, greenness and connectivity did not have statistically significant differences. The authors noted that this was likely due to a consistency in the biking environment. A significant find was that the individualâs perception of safety was influential. If the person was concerned about safety there was a greater chance to change routes from the shortest routes. On average, actual routes taken on a bicycle featured an increase of 1583 m in bicycle routes, 712 m less arterial roads, 570 m more local streets and an additional 525 m off street paths. 34 As GPS technology became more readily available, Hood et al. (2011) conducted a study in San Francisco which sought to understand cyclists decision making using real-time collected GPS data. The advantage of this study was the use of CycleTracks, an app that allows for recording personal characteristics and trip purpose â which is very important when considering the difference between utilitarian trips and recreational trips. Improved understanding about cyclist preferences can help to settle debates such as the one between wide curb lanes or bike lanes. Recruiting for this study was performed through email listservs at bicycle coalitions and schools as well as at local events and media outlets in the San Francisco bay area. It should be noted that the participant samples are biased towards cyclists who are members of the cycling coalition and to those with a smartphone. A multinomial logit model was used to analyze the data. Bike facilities were divided into class 1(bike path), class 2(bike lane) and class 3 (bike route), where bike paths are separated paths and bike routes are shared roads between cyclists and cars. The studies are in general agreement that cyclists prefer shorter routes with fewer turns, and bicycle infrastructure with more separation from vehicular traffic. While Sener et al. (2009) described qualitative findings regarding terrain grade, Hood et al. (2011) was able to further quantify the relationship between route choice and terrain features (including grade). It was found that the average cyclist will avoid a turn if it costs no more than 170 m in travel distance and will avoid a hill 10 m in height as long as the detour is no farther than 590 m. Most interestingly, average cyclists will travel the wrong way down a road if it saves the cyclist more than four times in distance travelled. Finally, average cyclists were willing to add 1 mile on bike lanes for half a mile on ordinary roads. To summarize, cyclists did not like climbing hills, turning and deviating excessively from the minimum shortest path but would do so for more desirable riding conditions. In contrast with the findings by Sener et al. (2009), cyclists preferred bike lanes. Finally, a benefit-cost analysis was performed, estimating bike lanes to generate 0.61 USD per km traveled. Bike lanes are thus a worthwhile investment wherever a route would carry more than 235 trips per day One of the latest studies in bicycle route choice was by Krenn et al. (2014) who conducted a study on the effects of the environment on cycling. Krenn et al. (2014) studied the City of Graz, Austria using the same methods used by the previous studies (Winters et al., 2010; 35 Sener et al., 2009; Hood et al., 2011) assessing actual routes taken by cyclists and comparing that to the shortest routes. A total of 113 participants recorded data for 278 different bicycle trips. Detours were calculated in meters and percentages by comparing them with the shortest path found using ArcGIS 9.1. A 25 m buffer was used to consider the characteristics of the surrounding areas. Four types of land use areas were considered including residential, traffic, industrial, green/aquatic. Street types were considered in proportion to their existence in meters along the length of the route. Point data was considered in terms of number of points per km. Krenn et al. (2014) took gender, age, education and body mass index all into account. The results found that 78% of trips were less than 4 km in length, with the median trip length at 2.3 km. Fourteen percent of trips did not detour and any detours ranged between 0 and 37%. No significant difference was found for any of the demographic measures. In general, the finding agreed with the previous research (Winters et al., 2010; Sener et al., 2009; Hood et al., 2011). Actual taken routes had smaller numbers of traffic lights and fewer crossings, bicyclists avoided busy streets, bicyclists preferred green and aquatic paths. Less hilly paths were preferred as is by now expected. Bicyclists used many bicycle pathways and lanes and rarely used roads that did not have them. In this study, 61% of the taken route consisted of bicycle paths and lanes while only 50% of the shortest route was bicycle paths and lanes. There are many more bicycle route choice studies. These studies in route choice provide an insight into the cyclistsâ revealed and stated preferences which indirectly measures the desirability of infrastructure that is used. 2.5 Research on Bicycle-Cyclist Dynamics In the 2000s, bicycle simulators were developed allowing data to be collected for researchers to study bicycle and rider dynamics in a controlled laboratory setting. Kwon et al. (2001) developed one of the first interactive bicycle simulators in Korea in 2001. Bicycle simulators are a challenge to develop due to the unstable dynamics of the bicycle in conjunction with the rider (Kwon et al., 2001). In 2005, a very similar bicycle simulator was developed in China. The design accounted for tire flexibility, road profile, as well as riderâs pedal, handlebar and tilt inputs (He et al., 2005). Zhang, Liu, Trkov, & Yi (2012) developed a bicycle simulator 36 equipped with a motion capture camera system to study the human sensorimotor mechanism for balancing control and to record the interactions of the bicycle with the rider. In 2013, Dozza and Fernandez (Marco Dozza & Fernandez, 2013) developed one of the most well equipped IPBs to date to study bicycle dynamics and cyclist behaviour. Their vision for the future is the development of intelligent applications to improve the safety and mobility of cycling (i.e. curve speed warning, collision warning) in the same way that similar applications have been developed for driving. In their study, Dozza & Fernandez recommends several methods of collecting various data from IPBs including methods to assess surface roughness and angular acceleration during manoeuvres. The majority of bicycle crashes are single bicycle crashes that do not involve any collision with another road user; the cyclist either falls by themselves or collides with an obstacle. In Sweden, 70% of bike crashes occur in this way (Werneke & Dozza, 2014). Therefore, the study of bicycle dynamics and rider behaviour in a real-time in-situ environment is crucial to bicycle safety and was made possible by the IPB. What is the chain of events or mechanisms that lead to these single bicycle crashes? How can we design bicycles, or bicycle infrastructure to reduce these mishaps? In order to answer these questions, a deep understanding of rider and bicycle dynamics is required (Marco Dozza & Fernandez, 2013). Although the aim of this thesis is to assess the cyclist comfort and safety perception related to the cycling environment, the IPB developed by Dozza and his team was an invaluable tool for this type of research also. 37 3 Objectives and Contribution As demonstrated in the literature reviewed above, reliable empirical tools for engineers and planners are crucial for predicting cyclist perceptions and cyclist safety. Since 1987, numerous indices such as the Bicycle Suitability Score and Bicycle Compatibility Index (Harkey et al., 1998; Turner et al., 1997) have been developed to quantify the level of service of bicycle infrastructure based mainly on the geometric and operational variables of cycling infrastructure. Harkey et al. (1998) used video footage to link geometric and operational variables to participant perceptions of comfort. Researchers later incorporated multiple expert judgments into safety models for the assessment of bicycle crossing (Leden et al., 2000). Further, researchers proposed statistical models that directly measure safety using crash data (Allen-Munley et al., 2004; Petritsch et al., 2006). More recently, researchers utilized surveys on a large scale to study cyclist perception and preferences (Winters et al., 2011; Yao et al., 2011). Researchers linked real time riding data collected from IPBâs to the cyclists reported comfort and safety (Joo & Oh, 2013; Yamanaka et al., 2013) . Perhaps one of the most revealing studies is the route choice models that utilized GPS data from bike share programs (Hood et al., 2011). A cyclistâs preference can vary based on circumstances; this is readily observed in an observed route choice study rather than a written survey. The research undertaken for this thesis contributes to existing knowledge by developing a reliable statistical Bicycle Comfort and Safety Prediction Models (BCSPMs) using cyclist reported comfort and safety most similar to Joo & Oh (2013) and Yamanaka et al. (2013). However, this study will utilized much larger groups of participants, more sophisticated sensors, and incorporate cyclist typology. The models can assist engineers and planners in infrastructure investment planning, management, and design. The objective of the study is as follows: 1. Use real-time data of the riding environment collected during test rides from individual riders 2. Incorporate individual rider traits (cyclist typology) 38 3. Accurately quantify the most significant factors that affect comfort and safety perception - vehicular traffic volume, lateral proximity of vehicular traffic, terrain slope, bicycle path type, size of adjacent vehicular traffic and vehicular traffic speed. 4. Develop and build an instrumented probe bicycle that can be used to collect data for this study 5. Develop reliable statistical Bicycle Comfort and Safety Prediction Models (BCSPMs) that predicts the comfort and safety perceptions of riders on a given bicycle path and thus predicts the desirability of that path. The following list outlines the unique contribution of this project: 1. This study has the largest sample size (n = 105) to date compared to other similar research methods found in the literature. 2. The statistical model developed accounts for variations within the population of cyclists utilizing cyclist typology within the analysis. 3. BCSPM developed utilizes a selection of the most relevant variables from other studies. 4. The data collected is extensive. There is more to be learned from this data and it is made freely available to other researchers. 5. An IPB design that has since been duplicated in the UBC STS Laboratory and can be used for future research. The BCPSMs developed can be used to evaluate planned and existing bicycle facilities, and for bicycle infrastructure management across Canada. Moreover, BCSPMs for bicycle facility planning, design, and management will benefit Canada through: 1. Advanced understanding of sustainable transport facility safety planning across the industry, 2. Improved decision making in the investment of public funds; and, 3. Promoting vibrant and sustainable communities across Canada. 39 IMPORTANT NOTE: It should be noted that this thesis assesses the perceived comfort and safety of a rider. Perceived safety does not always reflect objective/actual safety. For example, cyclists commonly feel safer riding on a sidewalk; however, riding on a sidewalk is not necessarily safer than riding on the road. 40 4 Methodology The experiments were conducted over the summer of 2016 in Kelowna, British Columbia involving 105 individual volunteer cyclists. Participants are asked to complete a survey of their riding experience and demographics, followed by a test ride of various pre-determined routes on the instrumented probe bicycle (IPB). The Bicycle Comfort and Safety Prediction Model (BCSPM) was then constructed from the participantsâ assessments and from data collected from the sensors mounted on the IPB. Sensors include but are not limited to: cameras, hand-brake sensors, handle-bar sensors, proximity sensors, and GPS-aided inertial navigation systems. Volunteers rode between three and four road segments. Each road segment was selected to provide a different cycling experience. Differences between cyclist perception due to personal preferences and experience is addressed using cyclist typology similar to that developed by Damant-Sirois (Damant-Sirois et al., 2014). T-Tests and Analysis of Variance (ANOVA) was utilized to pre-screen all variable significance and to help observe any meaningful relationships between variables. K-Means clustering is utilized to separate cyclists into different types using demographical data and cycling habits. The cyclist typologies served as inputs within the final BCSPMs. Finally, repeated measure ANOVA was used to create the final model as a general linear model. The methodology section is divided into seven sections which individually detail the preliminary pilot study, IPB development, participant recruitment, questionnaire design, test route selection, experimental procedure and a brief description of the methods used in developing the statistical model. 4.1 Preliminary Pilot Study The initial pilot was conducted in summer of 2013 and allowed for the development of the IPB and for preliminary BCSPMs to be developed as a proof of concept (Lee et al., 2014). The primary objective was to identify the most significant factors that affect the perceived comfort and safety of cyclists. The second objective was to determine the relationship between those factors and the feelings of safety and comfort. 41 Lee et al. (2014) used an instrumented probe bicycle (IPB) shown in Figure 4-1 to collect data regarding the riding environment while participants completed a post ride questionnaire. The bicycle was equipped with a BionX electrical assist motor which would classify the bike as an E-bike. However, the electric motor was not utilized during the experiments and the battery was removed to decrease the overall weight. The bicycle for the duration of the experiment functioned as a regular bicycle. In order to select the routes utilized in the consideration for three types of road conditions were assessed: road hierarchy, bike path type, and on-street parking. These conditions varied on 2 or 3 categories each. For instance, a road can be classified as a local road, collector road or arterial road (3 categories). While bike path type was classified on four categories: no bike specific infrastructure, bike lane, and separated shared path. By varying the three different road features, a total 19 different scenarios were identified. Streets were then identified across Kelowna, BC to match these features. This allowed testing to be compared among different road conditions. In order to observe differences. In total 26 road segments were selected to account for 17 of the 19 scenarios. Each segment was ridden between 3 and 7 times by 7 participants. In total 102 test ride samples were collected and 36 factors were assessed. The factors most significant to the perceived safety and comfort of cyclists was found to be lane width, traffic volume, noise levels, car speed, riding experience, fitness and bike path type. The initial results from the pilot study were similar to findings by Yamanaka et al (2013) and Joo & Oh, (2013). The open space in front of the cyclist as well as the path type and cycling speed were confirmed to be significant factors for perceived safety and comfort. Other significant factors confirmed in the pilot study include vehicular passing speed, vehicle volume, and bike lane width as well as the cyclists experience and fitness. 4.2 Instrumented Probe Bicycle The latest technology available in bicycle research is the instrumented probe bicycle (IPB). Configured with various sensors, the IPB can be used to monitor in real-time nearly any aspect of the bicycle environment and rider experience in the field. IPBs are now used for a 42 plethora of research topics including: the prediction of perceived bicycle comfort/safety(Lee et al., 2014; Joo & Oh, 2013; Yamanaka et al., 2013), identifying riding patterns , assessing various risk exposures between child and adult cyclists, and even to monitor air pollution exposure (Mohanty et al., 2014). Data used for the development of the BCSPM was collected from experiments conducted utilizing one of the first IPBs in Canada, developed specifically for this project at the Sustainable Transportation Safety (STS) research laboratory at UBC. The design and construction of the instrumented probe bicycle (IPB) is an integral part of this project. Projects of this nature depend heavily on the capabilities of IPB, with the limitations of IPB itself dictating the data that can be collected to a large extent the scope of the project. Hence, a section dedicated to the discussion of IPB design is crucial in order to appreciate the scale of the project. Participants provide their assessment of the comfort and safety of various routes after riding them on the IPB. The BCSPM will then be constructed from the participantsâ assessments and from data collected from the sensors configured on the IPB. Sensors include cameras, dual laser range finders, air temperature sensors GPS and inertial sensors. The design of UBCâs IPB and the careful selection of required sensors was completed after mindful consideration of other IPBâs in leading research from Sweden, Korea, Spain, the Netherlands Great Britain, and Japan (Marco Dozza & Fernandez, 2014; Joo & Oh, 2013; Llorca et al., 2014; Twisk et al., 2013; Walker, 2007; Yamanaka et al., 2013). The IPB development process saw two iterations of the IPB herein named IPB 1.0 and IPB 1.1. IPB 1.0 was utilized in the pilot study (A. Lee et al., 2014) and served as a proof of concept IPB collecting data for a preliminary study on a cyclistâs perception of comfort and safety. IPB 1.1 is a vast improvement on its predecessor. The increased complexity and expanded objectives of this project required IPB 1.1 to be more sophisticated and versatile. IPB 1.1 re-designed IPB 1.0 with an expanded set of sensors and brand-new custom designed and fabricated IPB sensor mounting systems and configuration. In the next two sections design and challenges of both the IPB 1.0 and IPB 1.1 will be described briefly. 43 4.2.1 IPB 1.0 Design and Operation The bicycle used for the IPB 1.0 was a 2011 Marin Alpine Trail mountain bike with 17â size frame and 29â wheel diameter. The IPB was initially also an electronic assist bicycle (E- bike) equipped with a BionX wheel motor and battery. However as this study focuses on the more numerous traditional cyclist group the BionX battery was removed, although the motor (which is part of the wheel was left installed). The sensors configured on the IPB included the 3DM GX3 -45 GPS-Aided Inertial Navigation System that is mounted on the frame of the IPB. A straight line potentiometer was mounted on the rear hand-brake to measure braking action of the rear wheel. A Hall effect sensor was mounted onto the steering column in conjunction with a rare-earth magnet attached to bicycle frame adjacent to the steering column to record steering motion. Parallel metal struts attached to the frame by Velcro and hose clamps extended from either side of the bicycle frame in order to hold a front-facing Logitech web camera and a rider-facing Microsoft Kinect camera approximately 60 cm in front of the handle-bar and 40 cm above the front tire. This positioning was required to achieve the minimum 2 foot distance requirement from the riderâs face for the Microsoft Kinect Camera. Sensors were connected via USB cables to a Lenovo tablet computer which recorded the data and also supplied the power. The only exception was the Microsoft Kinect camera which drew power from its own battery pack. The tablet was carried in the side pannier of IPB 1.0. Due to the BionX electric assist motor, the sensors and the mounting system, the operating weight of the IPB 1.0 measured approximately 35 kg. The weight of a typical mountain bike of this kind is 15 kg. Table 4-1 outlines the sensors configured on IPB 1.0 shown in Figure 4-1. Before each test run, individual software responsible for running each of the sensors was manually initiated before the rider could begin riding. The process of synchronizing the video with other data streams involved giving a visual signal displayed in front of the camera in conjunction with initialization of the software for the 3DM-GX3-45 sensor to serve as a reference during post processing. 44 Table 4-1. Sensors configured on IPB 1.0 Sensor/Device Data Provided Logitech HD Pro Webcam C910 RGB Video Footage â Facing Road Microsoft Kinect Depth Video, RGB Video, IR Video Straight line potentiometer (PTB6043- 2010BPB103) Hand-brake sensor Hall Effect sensor (A1324) with rare earth magnet Handlebar Position 3DM GX3 -45 (Inertial Measurement Unit & GPS) GPS Position, Velocity (3-axis), Roll/Pitch/Yaw, Elevation, Acceleration (3-axis) 45 Figure 4-1. IPB 1.0, configured sensors and mounting system Straight-line potentiometer Hall effect sensor Front facing camera Microsoft Kinect 3DM GX3-45 Saddle bag for Lenovo Tablet Metal struts for mounting camera and Microsoft Kinect 46 4.2.1.1 IPB 1.0 Challenges In this section operational challenges of IPB 1.0 are detailed and the design solutions implemented for IPB 1.1 in the full study are included for the interest of any researchers looking to design their own IPB for their bicycle studies. Battery Life The battery life of the operating IPB was extended from a maximum of 2 hours (IPB 1.0) to over 4 hours (IPB 1.1). This allows more test rides to be conducted at any given instance allowing for faster data collection. The solutions were threefold: 1. First, a new lap top tablet was purchased, the Surface Pro 3 running Windows 8.1 Pro 64- bit. This tablet has a much longer battery life than the Lenovo tablet. 2. Second, sensors in IPB 1.1 were configured to operate from their own power source. Thus sensors no longer operated on the battery life of the tablet as was the case in IPB 1.0. 3. Third, the batteries that powered the different sensors on the IPB 1.1 were of better quality and capacity. In total, including the tablet battery there are a total of five sources of power equipped with the IPB 1.1 rather than two sources with IPB 1.0. The Logitech web camera was replaced with a front-facing and rider facing GoPro Hero 3 wich each uses its own battery. The MP-M-300/95-300420-501 (Ultrasonic Sensor), the T100/T200 Laser Range Finder System is supplied by an external MP3450E-60 27,000 mAh Dual External Battery Pack. Finally, the DL1 Club Data Logger, is supplied by its own battery pack from the manufacturer and which also powered any sensors it was logging (such as the air temperature sensor). The DL1 Club Data Logger had the least battery life among all batteries- this was addressed by constructing a second battery pack for the DL1 Club Data Logger to extend its operating time in the field and overall operating time of IPB 1.1. See the next section for more details on IPB 1.1 design. Inertial Measurement Unit The 3DM-GX3-45 inertial measurement unit encountered vast difficulties during operation in consistency, synchronization and reliability of obtaining data. The DL1 Club was an alternative device that replaced the 3DM-GX3-45 in IPB 1.1 and was utilized by Yamanaka et 47 al., 2013. In addition to providing all the data that the 3DM-GX3-45 could provide as an inertial measurement unit (IMU) and GPS, the DL1 Club was also able to fully integrate any sensor that provided a voltage output, and would log the data with its own IMU and GPS output. Additionally, the DL1 Club would operate all sensors connected to it initiating all sensor simultaneously. Microsoft Kinect 1 Originally, a Microsoft Kinect 1 camera was mounted to the IPB 1.0 in order to record and analyze the cyclistâs facial expressions. Facial expressions were discussed as one way to observe the cyclists perceived safety and comfort. The sensor was difficult to operate on the IPB 1.0 due to the large amount of power that it required as its power was meant to be supplied by a wall 110V wall outlet. It also did not function well on a moving bicycle in sunlight as it is meant to be stationary and used indoors. The Microsoft Kinect 2 camera was acquired for the IPB 1.1 as a newer sensor, it was expected to perform better. In the end, it was no longer used in IPB 1.1 for primarily ergonomic reasons. The Kinect 2 mounting apparatus badly offset the balance and handling of the bicycle as it needed to be at least four feet from the cyclists face. It was also not always possible for the Kinect 2 to track the cyclistâs face as the cyclist turned their head to make maneuvers. Additionally, determining and applying a method of quantifying and interpreting facial expressions also proved challenging and beyond the scope of this study. Hand Brake Sensor and Handlebar Sensor Both the Hand Brake Sensor (PTB6043-2010BPB103) and Handle-Bar Sensor (A1324) did not produce reliable data output in the IPB 1.0 and hence their data was not utilized in the preliminary study. Alternatives to both sensors were considered. For the Handlebar sensor, the Vert-X â 1631 Rotational Potentiometer was a better alternative to the Hall effect sensor A1324. A custom mounting apparatus was designed and fabricated to install the sensor directly above and to attach it to the steering column in order to give a direct measurement of the handlebar rotation. Additionally, the Vert-X â 1631 could be directly connected with the DL1 Club Data Logger allowing for perfect synchronization of data streams. Unfortunately, after the installation of the Vert-X â 1631, the sensor was found to be 48 difficult to calibrate as it provided inconsistent voltage readings for the same degree of handle bar rotations. The Hand Brake Sensor (PTB6043-2010BPB103) was replaced with two 2â Flex Sensor potentiometers to improve on the ergonomic design of having a sensor on the hand brakes of the IPB. The flex sensors were lighter and smaller than the straight-line potentiometer that was on the IPB 1.0. However, it was difficult to secure the Flex sensor on the hand brakes without it interrupting the natural use of the hand brakes. As an alternative, a straight line potentiometer was installed on the brake cable itself. This was achieved by cutting away a portion of the protective plastic housing in which the brake cable was installed in order to expose the brake cable and attach the cable to the potentiometer. Unfortunately, it was found that exposing the brake cable in this manner affected itâs use and for safety reasons this method was no longer utilized. Due to time constraints a brake sensor was no longer used in IPB 1.1 Cameras Two new cameras, both GoPro Hero 3 models replaced the existing Logitech web camera. One GoPro Hero 3 was mounted facing forward and the other was mounted to face backward to observe the cyclist. Connection Issues During the preliminary study, it was found that data was frequently lost from either data cable or power cable connections loosening during the test rides. For IPB 1.1, secure fasteners were used to connect all USB cables. Battery pack connections were replaced with trailer plugs to ensure that the connection would be unaffected by vibrations and bumps. An issue that affected the data from IPB 1.0 was a laptop with a conventional hard drive. The spinning hard drive frequently paused during vibrations and bumps and would therefore stop recording of any data. A new Surface Pro3 tablet was acquired for the IPB 1.1. The Surface Pro3 tablet utilized a solid state hard drive, ensuring that the hard drive of the tablet would remain running despite bumps and vibrations. Sensor Mounting and Bicycle weight 49 The forward mounted beam was removed for a much more ergonomic and light weight camera and sensor mounting system. Sensors were bolted directly to custom made metal racks that are attached to the bike rack. The rear motorized wheel of the bicycle itself was replaced with a lighter weight conventional wheel to ensure as the weight of the bicycle was minimized. 4.2.2 IPB 1.1 Design and Operation In the development of IPB 1.1 for this study, the primary objective was to develop an IPB that collects detailed traffic data, one of the most significant of the factors which affect the safety perception. A secondary objective was to address the challenges with IPB 1.0 and to construct an IPB that could collect the most comprehensive set of variables of any IPB developed so far. The IPB 1.1 uses the same bicycle as that of IPB 1.0. The IPB 1.1 experienced several evolutions before reaching its final form. It was originally outfitted with a set of twelve sensors and devices. Part of the secondary objective was to construct an IPB that was able to collect the most comprehensive set of variables possible and it was with this objective in mind that a full set of sensors were installed and listed below: Table 4-2. All proposed sensors and devices for IPB 1.1 Sensor/Device Data Provided 2â Flex Sensor (potentiometer) Hand-brake depression Vert-X â 1631 (Rotational Potentiometer)*^ Handlebar Position 3DM GX3 -45 (Inertial Measurement Unit & GPS) GPS Position, Velocity (3-axis), Roll/Pitch/Yaw, Elevation, Acceleration (3-axis) Straight line Potentiometer Brake cable movement XBOX Kinect 2 Head angle, facial expression recognition, ambient sound level Mounted GoPro Hero 3* RGB Video Footage â Facing Road Mounted GoPro Hero 3* RBG Video Footage â Facing Rider Air Temperature Sensor* Temperature MP-M-300/95-300420-501 (Ultrasonic Sensor)*^ Lateral Distance to Cars T100/T200 Laser Range Finder System* Car Length, Passing Speed, and Passing Distance 50 Sensor/Device Data Provided Pulse Sensor Heart Rate DL1 Club Data Logger* Data Logger, GPS location, Velocity (3-axis), Acceleration (3-axis) Time Stamp Button* Used to synchronize data collected by T100/T200 Laser Range Finder System, with GoPro Camerage footage and also data collected in the DL1 Club Data Logger *Sensors that are included in final form of IPB 1.1, ^Sensors configured on IPB 1.1 but whose collected data was unreliable for use in data analysis. Eventually, many of the sensors were reluctantly removed due to two reasons: (1) unsatisfactory performance, ergonomics or appropriateness and (2) time constraints for the completion of IPB 1.1 development in order to be used in data collection for the full study on cyclist perception of comfort and safety. The sensors that made it to the final form of IPB 1.1 are marked in the Table 4-2 above with an asterisk. Figure 4-2. Final form of IPB 1.1 Ultrasonic Sensor GoPro Cameras Thermometer Data Logger, GPS & IMU Handlebar Sensor Laser Range Finder Time Stamp Button Laptop Saddle Bag 51 Figure 4-3. Discontinued Sensors - Sensors initially considered for IPB 1.1 but not utilized in the final form of IPB 1.1 Brake Sensor Hand Brake Sensor 3DM-GX3-45 IMU and GPS Pulse Sensor Microsoft Kinect 2 52 Figure 4-4. IPB 1.1 Sensor mounting configurations (Left Side) IPB 1.1 With DL1 Club, 3DM-GX3-45 and Ultrasonic Sensor Mounted on the rear rack (Right Side) IPB 1.1 breadboard connecting additional sensors to DL1 Club Data Logger (Left Side) IPB 1.1 with sensor protective casing mount (Right Side) IPB 1.1 with cymbol stand sensor mounting system (Left Side) IPB 1.1 with front mounted laser range finder (Left Side) IPB 1.1 with rear mounted laser range finder 53 The sensors operated in four data streams. The T100/T200 Laser Range Finder System recorded data through the Surface Pro3 using proprietary software provided by Laser Technologies. DL1 Club Data Logger collected data on acceleration (3-axis), velocity and GPS location, and also logged data from the ultrasonic sensor, air temperature sensor and a time stamp button in an onboard SD Card. The file was saved in a proprietary format and could be opened for analysis by proprietary software from Race Technologies. This data could then be exported to Microsoft Excel for further post processing and analysis. Finally, both the GoPro Hero 3 cameras store video footage on local onboard micro SD cards. The DL1 Club Data Logger records time from 0 to X number of minutes. The T100/T200 Laser Range Finder System timestamps the data it collects using the time on Surface Pro3. The data streams are synchronized using the GoPro cameras which film the clock on the Surface Pro3 and also films a button press which activates time stamp that inputs a signal on the data logger. 4.2.2.1 IPB 1.1 Challenges Time-consuming operation The start-up process at the beginning of each test ride using the IPB 1.1 requires approximately 6 to 8 minutes. The procedure includes connecting all the power sources, starting the sensors and also the executing the synchronization process. This can be time consuming in the long run and it would be beneficial to decrease this start up time and also automate the synchronization process. Ergonomics and manoeuvrability Compared to the IPB 1.0, the IPB 1.1 was an exceptional improvement. However, further improvement to the manoeuvrability of the IPB and comfort of cyclists riding the IPB 1.1 is needed. The IPB 1.1 was at least 4 to 5 kg heavier than the typical bicycle and the boom arm that extends beyond the handle bar which holds the GoPro Cameras and part of the T100/T200 Laser Range Finder System is still somewhat distracting to first time riders. The boom arm also continues to hinder the manoeuvrability of the bicycle, especially during turning manoeuvres. 54 Additional Sensors The IPB 1.1 requires additional sensors in order to be a more comprehensive tool for bicycle studies. As it stands, the main focus of the bicycle is to record the traffic beside the cyclist. Additional sensors to record handle bar turning angles, pedal cadence and hand brake usage will need to be added to future IPBs for other types of studies. Unsolved Errors & Loose connections Errors with data collection from the data logger and the laser sensor still occurred. Despite the lengths taken to secure the connections for data streams and power supplies, the bumps and vibrations of cycling still loosened connections. Many data were lost from loose connections while other errors in data collections such as missing data, and blank data files currently remain unresolved. 4.3 Participants Information regarding the participantâs perception of the test ride is collected on an individual basis. The individual serves as the basic unit for the survey. Individuals recruited for the study satisfied the following criteria: 1. Be at least 19 years of age 2. Be able to ride a standard bicycle 3. Have no health conditions that put them at increased risk of collision or physical danger 4. Be able to follow basic bicycle traffic rules Participants were recruited locally from the City of Kelowna. The scope of the experiments is for the non-winter riding conditions. Test rides were carried out during the months of April, June, July, August and September in 2016. In order to recruit as many participants as possible, several methods of advertising the study were implemented in the months prior to conducting the experiments. The locations of advertisement postings are listed below: 55 1. Email invitations to Kelowna Area Cycling Coalition (KACC) email list. 2. Email Invitations to UBC Cyclists email list 3. Advertisement in the OGO Carshare Newsletter 4. Advertisement in the Greenstep Bike to Work Week Newsletter 5. Invitations on the Trails of the Okanagan, CITE, AREMA and UBC Engineers Facebook Page 6. Invitations on the Kelowna Mountain Biking Club website 7. Advertisement posted throughout UBC Okanagan Campus, and over 20 businesses throughout Kelowna including bicycle shops, outdoor shops, sports shops, cafes, yoga studios, physiotherapy clinics, pharmacies and many others. Presentations to the public and at different organizations were also conducted to advertise the study and invite participation. In total, there were 213 interested participants who signed up on an email list from the following events: 1. KACC Annual General Meeting 2016 2. Visits to 2nd and 3rd year engineering classes at UBC in 2016 3. Transportation Talksâ CITE & AREMA Event 2015 4. Urban Systems Inc. Invitational Luncheon 2016 5. Tree Brewing KACC Bike to Work Week Meeting 2016 6. CITE Local Chapter Meeting 2016 7. Urban Systems Bike to Work Week Lunch Talk 2016 8. Bike to Work Week Celebration (5-day Event) 2016 To thank participants for their contribution, several businesses and non-profit organizations also generously donated prizes. The prizes included free giveaways and gift certificates. Participating organizations include: 1. The Hot Box Yoga Kelowna 2. Cyclepath Kelowna 3. Kelowna Cycle 56 4. Kelowna Area Cycling Coalition Participants were scheduled manually over the phone, in person or email and also using an automatic online booking system called youcanbook.me which was accessed through a paid subscription. Participants can book directly into an available time slot of their convenience from an online calendar. Email confirmations and reminders were sent out to each participant to confirm booking and 24 hours prior to the appointment. In total 105 participated in the study. 4.4 Questionnaire The survey plays a central role in this study. While the IPB collected input data for the model (cycling environment), the survey collected the output data (perceptions of the cyclists). The survey also collected data on demographics, basic physical characteristics and the cycling attitudes of the participants which was used to separate the participants into different groups. Most of the survey used in this study was adapted from two other surveys, the Calgary Downtown Commuter Cyclist Survey (City of Calgary, 2007), and the survey used in Damant- Siroisâ cyclist typology study (Damant-Sirois et al., 2014). The survey was also updated throughout the study taking feedback from participants. The information collected by the survey was used for the following: 1. Assess the level of perceived safety felt by the cyclist due to traffic 2. Assess the level of comfort perceived by the cyclist due to the slope/gradient of the bike route 3. Assess the level of comfort perceived by the cyclist due to the surface roughness 4. Assess cyclist perception of traffic volumes, and driveway presence 5. Collect basic demographic information such as age, gender, income etc. 6. Collect basic fitness data such as body mass index, resting heart rate, and blood pressure. 57 7. Collect the cycling attitudes of the participants to divide the participants into cyclist typologies. 8. Collect data on the cyclistsâ riding history pertaining to accidents, and close calls. 4.5 Test Routes The test rides was conducted between April 2016 and September 2016, in Kelowna, BC, a city about 400 km east of Vancouver with a population of approximately 194,882 in 2016 (Statistics Canada, 2016). Two routes were selected which encompassed a large portion of the different types of bicycle path types one may expect to encounter in a typical North American city. The two routes were divided into two sections each for a total of four distinct sections. Table 4-3 summarizes the length of each section, the elevation change and the slope. Table 4-3. Google Map Data on Section length, elevation change and calculated slope Section Distance One Way Elevation Change In One Direction Gradient Section 1 1100 m 16 m up and 4 m down 1.1 percent Section 2 1600 m 23 m up 1.4 percent Section 3 1100 m 12 m up 1.1 percent Section 4 550 m 6 m up 1.1 percent 4.5.1 Section 1 The first section starts at the Springfield Road and Durnin Road intersection and proceeds to the Springfield Road and Ziprick Road intersection. It features a bicycle lane, adjacent to a collector road with parks and forests to the right of the cyclists. Figure 4-5 depicts the Google satellite map of Section 1. 58 Figure 4-5. Google map of Section 1 During the study, Section 1 went through road reconstruction which involved the widening of the bike lane from a 1.5 m to 2.1 m. Additionally, the bike lane was painted with double lines to emphasize its presence to vehicles. This offered an opportunity to take a look at Section 1 in a before and after study to assess the effect of bike lane width. Section 1 was ridden on during three distinct phases: 1. Old â this is the original state of section 1, where it had 1.5 m bike lanes 2. Newly paved with marker lines â this is section 1 after it has been newly paved and the road was once again open for public use. Permanent road lines have not yet been painted. Thinner marker lines have been painted indicating where permanent lines are to be drawn. It was clear at this point the bike lane is now 2.1 m wide, but the double lines were not yet present and thin lines that existed looked more like chalk markings than the thick painted lines that separated vehicular lanes. 3. New paved lines drawn â this is section 1 after it has been newly paved and permanent lines are drawn. 59 Photographs of all three phases of Section 1 have been provided for clarity in Figure 4-6, Figure 4-7, and Figure 4-8. Figure 4-6. Old Section 1 with worn pavement 60 Figure 4-7. Newly Paved Section 1 with marker lines painted Figure 4-8. Newly Paved Section 1 with double lines painted 4.5.2 Section 2 Section 2 begins where Section 1 ends, at the Springfield Road and Ziprick Road intersection and ends at the Springfield Road and Hollywood Road intersection. Section 2 features a 1.5 m wide bicycle lane, adjacent to a collector road with single family homes to the right of the cyclists. Driveways from these homes intersect with the bicycle lane. Figure 4-9 and Figure 4-10depicts the Google satellite map of Section 2 and its view from the point of view of the cyclists. 61 Figure 4-9. Google Map of Section 2 Figure 4-10. Section 2 - Paved bike lane estimated to be several years old, some cracks present 4.5.3 Section 3 Section 3 runs along the Greenway adjacent to Mission Creek and south of Springfield Road until the Greenway reaches Graham Road. Section 3 features a wide gravel path that is 62 shared with pedestrians, surrounded by forest and tree cover. Figure 4-11 and Figure 4-12 the Google satellite map of Section 3 and its view from the point of view of the cyclists. Figure 4-11. Google map of Section 3 Figure 4-12. Section 3 - Gravel pathway 4.5.4 Section 4 Section 4 begins where Section 3 ends, at Graham Road runs along Creekside Road until the intersection of Creekside Road and Gerstmar Road. Section 4 features a residential local street with driveways and no bicycle lane, surrounded by single family homes. Figure 4-13 and 63 Figure 4-14 depicts the Google satellite map of Section 3 and its view from the point of view of the cyclists. Figure 4-13. Google map of Section 4 Figure 4-14. Section 4 - Paved residential street â pavement estimated to be several years old 64 4.6 Experimental Procedure Upon arrival at the staging area the participants were greeted, offered water and given a brief background on the study explaining the purpose of the experiment. The experiment then proceeded as follows: 1. The study consent form was signed 2. The studyâ s definitions of comfort and safety perception were explained 3. Printed maps of the routes were reviewed with the participant. 4. The participant was asked to complete the demographics section of the questionnaire 5. The equipment on the IPB was set up while explaining to the participant what each instrument will measure. 6. Section 1 and Section 2 of the test ride was completed with the participant 7. The participant completed the section pertaining to Section 1 and Section 2 in the questionnaire. 8. Section 3 and Section 4 of the test ride was completed with the participant 9. The participant completed the section pertaining to Section 3 and Section 4 in the questionnaire. 10. The participants were offered a snack and free give away from the sponsors. Their names were entered in a draw for a further prize. Section 1 and Section 2 were completed together with the participant riding from Springfield Road and Durnin Road to Springfield Road and Hollywood Road South. The participant then crosses the street and rides back to Springfield Road and Durnin Road returning to the staging area to complete part of the survey. Section 3 and Section 4 were completed together with the participant riding to Gerstmar Road via the Greenway trail and Creekside Road. The participant then performs a U-turn and rides back to the staging area to complete the rest of the survey. Figure 4-15 and Figure 4-16 depicts a map and photograph of the staging area and its location with respect to Section 1, 2, 3 and 4. 65 Figure 4-15. Experiment Map Figure 4-16. Staging, Holding and Equipment Storage Area Section 1 and 2 Staging, Holding and Equipment Storage area Section 3 and 4 66 4.7 Model Development The development of the BCSPM operates on the understanding that a cyclists perceptions create the perceived feeling of comfort and safety based on inputs from the riding environment. The perceptions of the cyclists change their riding behaviour which in turn changes the riding environment. These behavioural changes can be as simple as selecting an alternative route, or moving closer to the inside of a bicycle lane. Figure 4-17 depicts a flowchart that illustrates what is described. The key piece of information to note is that stimuli from the riding environment are processed through an individualâs perception which then governs feelings of safety and comfort. Perception varies between individuals and can also vary by mood or state of mind. Figure 4-17. Model of cycling environment, perception, and cyclist behaviour interaction in reality Broadly speaking, we can attempt to categorize different cyclistsâ perceptions by assigning individual cyclists to a certain typology. We can also quantify the most pertinent features of the riding environment as input variables. Finally, using both the cyclist typology and the input variables, the BCSPM can then estimate a response variable, which in our case is the feeling of safety and the feeling of comfort. Figure 4-18, depicts a flowchart that illustrates what was described, additionally each section of the analysis addresses in detail one of these components. 67 Figure 4-18. Simplified statistical model representation of cyclist perception and environment relationship 4.8 Data Analysis and Modelling Techniques This thesis utilizes Principle Component Analysis (PCA), K-Means Clustering, and the parametric analyses: Pearson correlation, T-Tests, Analysis of Variance (ANOVA), repeated measures ANOVA and Multivariate ANOVA (MANOVA). The following is brief description on how the main methodologies are utilized in important aspects of the analysis. 4.8.1 Pearson Correlation Pearson Correlation was utilized throughout the thesis to test the correlation between variables following the example of Petritsch et al. (2006), Yamanaka et al. (2013) and others. The calculated Pearson correlation coefficient can range from a value of -1 to 1 and measures the association of two variables. A positive correlation indicates that an increase in one variable is matched by an increase in the other, while a negative correlation indicates that an increase in one variable is matched by a decrease in the other (G. Gignac, 2019). Pearson correlation was used as an assessment tool throughout the thesis either as a stand-alone test or as part of other more complex analyses to assess the significance of any apparent correlations observed between both independent and dependent variables. 4.8.2 T-Test The paired sample T-Test (a.k.a, dependent samples t-test) is utilized to assess if differences in the means within-subjects are statistically significant. A paired sample T-Test is utilized when two means are linked (G. E. Gignac, 2019a). In our case, the results that are tested are the safety and comfort ratings of different sections but from the same participants making 68 this the appropriate T-Test to utilize .This test was used in the final step of response variable index construction. 4.8.3 Principle Component Analysis (PCA) Following the example of Petritsch et al. (2006), and Yamanaka et al. (2013), Principle Component Analysis (PCA) was used in determining the significant response variables and in determining the significant survey questions to include in creating the cyclist typology clusters. PCA is a dimension reduction procedure that aims to represent a set of variables by transforming them into a lesser number of orthogonal principle components (eigenvectors). Principle components can be thought of as new variables that act in a direction which maximizes the variations in the original data and are a transformation of the original dataset. A component loading for each principle component is calculated for each of the original variables which makes PCA a useful tool in identifying variables that are related. The resulting component loadings allow us to measure how each variable associates with another and to which principle component they primarily act upon. PCA also calculates the relative importance of each principle component through eigenvalues. More variability (a larger eigenvalue) in a particular principle component (eigenvector) indicates theoretically something more significant. When constructing a prediction model, the insight that PCA provides helps reduce redundancy and avoid over fitting a model with highly collinear variables. PCA is performed in a two-step process: 1. In the first step we perform a PCA procedure without limiting the number of principle components. The purpose of this is to observe the scree plot and total variance explained by each principle component that is calculated in order to determine the appropriate number of principle components to use. 2. Once the appropriate number of principal components are determined, the PCA procedure is performed a second time, specifying the determined appropriate number of principal components. The resulting component loading for each variable can then be analyzed and interpreted. 69 PCA was utilized by Yamanaka et al. (2013) in his IPB study to group independent variables into larger global factors. In a similar fashion PCA is used in this thesis to explore the relationship between the 7 to 9 response (dependent) variables collected from the participants in each section of the test ride. PCA is the first step in the analysis to help identify response variable redundancies, and relationships (through component loadings) and most importantly to determine the significant underlying factors (ie. principle components) that the responses are alluding to. In order to select an appropriate number of principle components, parallel analysis was utilized. Parallel analysis was chosen as a more rigorous approach compared to the more subjective elbow method, or Kaiser-Guttman method of keeping principle components with eigenvalues greater than 1.0. The parallel analysis procedure outlined by OâConnor (2000) was followed. 4.8.4 K-Means Clustering The literature review on page 25 outlined the importance of using cyclist typologies in giving policy recommendations and assessing cyclist behaviour. In this thesis, the K-Means clustering algorithm was used to group the participants into cyclist typologies based on their survey responses to demographic questions as well as questions which assessed their personal opinion and perception on cycling transportation. The K-Means clustering algorithm was employed by Damant-Sirois et al. (2014) in his own study in Montreal. 4.8.5 One-Way Analysis of Variance (ANOVA) One-Way ANOVA is used throughout the thesis in its simplest form to test whether there is a significant difference among two or more means (for instance, whether there is a difference in cyclists perceived safety given according to cyclist typology which has two or more groups). The Null hypothesis in One-Way ANOVA is that means are rqual across all groups. The alternative hypothesis is that means are not equal across all groups. Rejecting the null hypothesis of One-Way ANOVA means there is a statistically significant difference between the groups means, however this is usually followed up with a Post-Hoc Testing (a.k.a. Multiple Comparisons) to determine exactly which particular groups are responsible for rejecting the One- Way ANOVA null hypothesis (G. E. Gignac, 2019b). To summarize ANOVA, in essence it is 70 used to test the significance of whether variability in one variable is associated with variability in another (Abraham & Ledolter, 2006). Within this thesis One-Way ANOVA was used extensively to aid in determining the appropriate criteria for utilizing the K-means clustering procedure for determining cyclist typology. One-Way ANOVA was also utilized to verify the that cyclist typology was a significant input variable to include in the final model (variable screening). 4.8.6 General Linear Model (GLM) General linear models are utilized to implement linear regression and significance testing with ANOVA. With a general linear model multiple categorical variables and continuous covariate variables may be used as inputs. Univariate GLM can be utilized for testing whether variation in a continuous variable affects a dependent continuous variable. In contrast, One-Way ANOVA tests whether variations in the means of a categorical variable affects the mean of a continuous dependent variable. Univariate GLM was utilized in this thesis to screen continuous traffic related variables (ie. average volume, passing speed, separation and length) against cyclist perceptions of comfort and safety prior to fitting the variable into the final model. Univariate GLM was also utilized to construct the final models Section 1 and Section 2 specific BCSPMs. Repeated measures GLM is similar to Univariate GLM, however it allows for repeated measures data to be incorporated (ie. when the dependent variable is measured from the same group of participants across a varying factor such as time.) In the case of this thesis, repeated measures GLM was utilized to incorporate the difference between sections (within-subject effects) while still addressing cyclist typology (between-subjects effects). Repeated measures GLM was used for the final comprehensive BCSPM. 71 4.8.7 Analysis Assumptions The following assumptions and justifications for these tests are discussed below: Independent Observations The test rides were conducted with each individual independently of others. Participants did not speak with other participants during the test. Normality of Data The Dependent variable should be normally distributed within each group. We can use the Shapiro-Wilk test or QQ Plots of the dependent variable to verify this however it has been shown that ANOVA is robust to non-normality so we donât have to worry about this too much (Blanca et al., 2017) Equality of Variance In ANOVA the assumption of equality of variance states that the variance in the dependant variable between groups remains the equal. This is tested by the Leveneâs test and will be addressed at the time of analysis in the thesis. For repeated measures GLM, an analogous test is the Mauchlyâs Sphericity test. To have sphericity means that the variances of the differences throughout all the test variables should are equal. Sphericity is analogous to the equality of variance assumption for one-way ANOVA. This assumption is addressed at the time of analysis in the thesis. Continuous Data for dependent variable The main discussion here is using Likert Scales in a Parametric Analysis such as ANOVA. In this study a likert scale is used in recording the responses of the participants. Likert scales represent discrete and ordinal data types (such as ranking clarity of a liquid from 1 being opaque to 5 completely clear, with all integers in between). In total we would have only 5 discrete levels. This is different from scale data types (such as actually measuring the brightness of light that can pass through the liquid) in those cases the data is continuous. 72 A lot of analysis known as parametric analysis such as t-tests, ANOVA and linear regression assume a normal and continuous distribution in the response variables. In regards to likert scales, they are not categorical data but they are not continuous data either. Debates on how to analyze likert data has existed for some time and no consensus exists. Studies on comparing parametric and non-parametric analysis on likert scale data indicate that we can use either and do not have to be concerned with running into a false positive (Winter & Dodou, 2012). In this study we choose to use parametric tests such an ANOVA and t-tests as being appropriate for the following reasons: 1. Sensitivity â Non parametric tests do not assume normal or continuous distributions but have been known to have concerns in regards to a lower ability to detect differences when they exist. 2. Continuous Data â The inbound and outbound data for Section 1 and Section 2 (as explained in post processing) was merged creating more continuous looking data for the safety, comfort and enjoyment responses. Which meant that when the two responses for the outbound trip and the inbound trip for section 1 and section 2 are averaged we create data that is no longer Likert in nature but continuous. Hence our scale may still be from 1 to 7 but we are not limited to just analyzing the whole numbers because of averaging. In this way the response is much more like a continuous variable than a likert scale. Additionally, we are predicting desire which was created by taking the mean of safety, comfort and enjoyment, creating even more continuous data. 3. Sample Size â Even if we had to use the likert scales directly, there is sufficient sample size to assign a likert scale as an output for ANOVA or other parametric tests even if we did not have the averaging effect discussed earlier. Generally, the rule of thumb is we need 15 samples for each group if we have three groups. In this case we satisfy this general rule. 4. One of the concerns of using likert scales with ANOVA is that a âstrongly agreeâ and a âstrongly disagreeâ in some situations are not the same as two âneutralsâ. In other words, it is more of a categorical data in some cases (however not to our data). In other 73 cases continuous data has been turned into a likert scale in the case of grade point average calculations used in many universities. In cases like this one would feel comfortable reasoning that the ordinal data of GPA can be treated like a scale. In our case we are asking about comfort, where 7 was absolutely uncomfortable and a 1 was absolutely comfortable. I believe our data works well as a scale just like GPA so that it can be treated like a scale such that we can proceed with the ANOVA analysis and other parametric tests. Previous research has stated that likert scales on a 5 to 10 point scales can be used like scales data (Dawes, 2018) and can be used with parametric statistics. 74 5 Data and Post Processing The test rides suffered from many malfunctions and errors leading loss of data or incomplete data. In this section we review how incomplete data were addressed, the post processing procedure of the data, the post processing of the data into variables, the resulting data sets, the descriptive statistics, and details on cycling accidents (collisions) and close calls. 5.1 Malfunctions and Creation of Data Sets A total of 105 riders participated in the experiment. Each test run with each participant produced 5 separate streams of data. The questionnaire data was digitized manually by hand into an Excel spread sheet, while the instruments account for the remaining four data streams. Table 5-1 outlines which data streams each sensor contributed to. It was discovered during the experiment that many malfunctions arose in data collection. Table 5-2 outlines the different error scenarios encountered and their resolution. Table 5-1. IPB 1.1 Sensors, Data provided and data streams. Sensor/Device Data Provided Data Stream Mounted GoPro Hero 3* RGB Video Footage â Facing Road 1 Mounted GoPro Hero 3* RBG Video Footage â Facing Rider 2 Air Temperature Sensor* Temperature 3 MP-M-300/95-300420-501 (Ultrasonic Sensor)* Lateral Distance to Cars 3 T100/T200 Laser Range Finder System* Car Length, Passing Speed, and Passing Distance 4 DL1 Club Data Logger* GPS location, Velocity (3-axis), Acceleration (3-axis) 3 Red Button* Time Stamp 3 *Sensors that were included in final form of IPB 1.1 75 Table 5-2. Outline of malfunction scenarios and their remedy Error Scenario Resolution Number of Test Ride affected A Laser Malfunction - Did not record none 5 B Laser Malfunction -Laser Data Cut off early none 3 C Section 2 inbound and outbound was not differentiated keep them merged and also merge the inbound and outbound data for section 1 57 D Error During Post Processing - No Data Processed none 22 E Participant Opted Out none 1 F Error in data recorded or missing data none 2 G Section 3 and 4 missing none 1 Many test rides were affected by multiple error scenarios. A complete table detailing what scenarios affected each test rides are listed in the Appendix. Due to malfunctions in equipment and post-processing, and also due to changes in section 1 due to construction, the data was split into different data sets depending the data that was intact and the state of section 1. Different data sets will be used for different analysis. This is not an ideal situation as it complicates the analysis, however, this approach was taken so that all data collected by each participant could still be utilized. Within Chapter 6 of this thesis, the data set utilized is noted within the headings and within data tables. Table 5-3 summarizes the different groups of data available for analysis. 76 Table 5-3. Data Set Summary Description Sample Group Name Number of Samples Section 1 construction complete Laser Range Finder and DL1 Club Data Logger Data Intact Group A 53 Yes Yes Group B 72 No Yes Group C 71 or 72 (including the non-respondent) Yes No Group D 105 No No 5.2 Post Processing Data In order to âcleanâ the data of as much noise as possible, the following assumptions were applied during post processing: 1. Actual car speeds are calculating by adding the bicycle speed from the Data Logger stream to the relative speed from the laser data. The button press was filmed by the GoPro Cameras along with the computer clock and this was utilized as the method to synchronize the Data Logger data stream with the camera footage and the laser data which is time stamped with the computer clock. 2. All recorded slow passing vehicles with speeds below certain level is removed. The rationale was to remove vehicles that are slowly overtaking the cyclist as everyone comes to a stop at a red light. Including such vehicles will lead to an abnormally lower average vehicle passing speed not representative of the passing speeds a cyclist encounters when riding midblock. These slow passes likely do not affect the comfort or safety of the cyclist. 3. All recorded passing vehicles above a certain speed are removed. Certain recorded speeds were absurdly fast. These were presumed to be errors and were deleted. 4. Stopped times at red light â when bicycles are stopped â passing vehicles are not recorded 5. Slow cycling times as bicycle approach a stop â when bicycle speeds are below a certain speed â passing vehicles are not recorded. 6. Data collected beyond pre-determined GPS coordinates that lie outside the experimental road sections are removed. 77 7. GPS Coordinates are used to separate data collected in different sections in post processing Additional processing procedures further removed inconsistencies in the data. These procedures were implemented as their needs arose. 1. Deleted all questions that are incomplete and introduced later when testing already commenced. 2. Several times the data was split into the 3 sections when there should have been 4. It is clear from the bicycle speeds that the inbound and outbound portions of Section 2 was often joined into one. In order to create consistency all Section 1 and Section 2 inbounds and outbound data were merged for further analysis. 3. Section 3 inbound and outbound were also merged for consistency and to simplify analysis later. 4. Confusing opinion and perception questions were removed â during the testing process it was discovered that the meaning of many questions was ambiguous and many participants asked for clarification. It was clear that these questions were interpreted in multiple ways. 5. If left blank on how much they cycled in the winter, it was assumed that cyclists would ride two levels down from their summer frequency. 6. Responses that are inaccurate and inconsistent for the same runs were removed. 7. The variable that calculated the time intervals between cars passing the cyclist was removed because that variable is inherently represented by the vehicular volume variable. 8. Redundant percentile calculations removed. 9. Data entry errors adjusted â level 6 and 7 Likert scale entries were changed to level 5 where there were only five levels. 10. Errors adjusted â within section 3 some riders stated levels of 2, 4 and 7 to question of traffic safety indicating they felt unsafe due to adjacent traffic. However, this was later confirmed that they felt unsafe due to pedestrian traffic. This was corrected to 1 as the questions was meant for vehicular traffic and there is absolutely none in section 3. It is assumed that any safety risk they felt due to pedestrian traffic would also be reflected in the general safety question. 78 11. All response variables and variables asking about of traffic noise was normalized on a 0 to 10 point scale for ease of interpretation rather than a Likert scale between 1 and 7. 12. Variables with reverse scaling was rescaled such that that the desirable scores were always the higher scores. 5.3 Descriptive Statistics 105 riders participated in the study, of which 104 test rides were completed as one participant opted out during the experiment after filling out part of the surveys. In total there were 66 male and 39 female participants with ages ranging from 19 to 80 since this research focuses on adults. Further study on youth would be valuable. Table 5-4. General Participant breaks down the gender, summer biking frequency (SmrBikFre), winter biking frequency (WinBikeFre), weekly exercise (ExerHr), employment and income of the participants. Not all totals add to the 105 total participants as some participants elected to leave parts of the survey unfilled. 79 Table 5-4. General Participant Descriptive Statistics Descriptive Frequency Percent G en de r male 66 62.9 female 39 37.1 Total 105 100.0 Sm rB ik eF re Daily 43 41.0 A few days per week 48 45.7 A few days per month 6 5.7 a few days per year 8 7.6 Total 105 100.0 W in Bi ke Fr e Daily 13 13.8 A few days per week 24 25.5 A few days per month 19 20.2 a few days per year 38 40.4 Total 94 100.0 Ex er H r less than 2.5 hours 5 4.8 between 2.5 and 5.0 hours 24 23.1 between 5.0 and 7.5 hours 32 30.8 between 7.5 and 10 hours 22 21.2 more than 10 hours 21 20.2 Total 104 100.0 In co m e less than 20000 13 13.0 between 20000 and 40000 13 13.0 between 40000 and 60000 15 15.0 between 60000 and 100000 28 28.0 above 100000 31 31.0 Total 100 100.0 Em pl oy m en t Full Time 56 73.7 Part Time 8 10.5 Student 3 3.9 Retired 9 11.8 Total 76 100.0 80 About 63% of the participants happened to be male which is comparable to 58% self- reported in statistics Canada as having cycled in the past 12 months (Statistics Canada, 2017c). Approximately 87% of the participants cycled either daily or a few days per week during the summer while approximately 40% had the same cycling habits during the winter. This indicates that the majority of our participants are avid cyclists, which is no surprise as participants were self-selecting volunteers. The vast majority of the participants who volunteered learned of the study through the annual bike to work week event in 2016 and had an interest in the study primarily because they are cyclists. Approximately 95% of participants exercise more than 2.5 hours per day and the majority worked full time earning incomes of at least $40,000 per year. Figure 1-1 visually breaks down the participantsâ age groups that were involved in this study. The average age was 45 years old with a range from 19 to 80 and good representation from each age group. It should be noted here that the majority of participants were younger adults between 20 and 30, middle-aged adults between 50 and 60 and also older adults between 60 and 70, creating bimodal distribution with an average age of 45. Figure 5-1. Participant Age Histogram 81 5.4 Accidents and Close-Calls (Group D Data) For this analysis we will use the Group D data since the laser data and DL1 Club Data logger data is not required. One of the questions we asked the participants was their history of accidents and close calls as a child and as an adult. Many of these questions were derived from the City of Calgary(City of Calgary, 2007) survey and it is useful to have a basic look at this data because it can reveal important information such as the underlying causes of accidents. The questions here were originally included to help sort the cyclist into different typologies but also partly for interest. Table 5-5. Group D Data - Cycling Accident and Close Call Rates Question Asked yes no total response total participants asked Have you been in a cycling accident as a Child 52 42 94 of 105 Have you been in a cycling accident as a n Adult 69 36 105 of 105 Have you been in a cycling accident 87 18 105 of 105 Have you been in a cycling close call as a Child 28 19 47 of 87 Have you been in a cycling close call as a n Adult 58 14 72 of 87 Have you been in a cycling close call 65 10 75 of 87 The survey was updated part way through the study to include close calls, hence the difference between 105 and 87. Some participants did not respond to certain questions and left the sections blank. Some participants responded that they did not remember and in those cases their responses were considered blank also. We can see in almost all cases over half of the participants were involved in cycling accidents and close calls that they could remember. It is important to keep in mind that those 82 that said they did not remember or left responses blank were just left as non-responses. Originally, we had the participant tell us how many accidents and close calls they were in. However, the responses were so wide ranging that in post processing we had to adjust the responses to simply be yes or no. The next series of questions were asked to all 105 participants. Participants were asked to rank different response items pertaining to certain questions regarding how often certain factors contributed to close calls and accidents. During the interview process it was found that many participants had a hard time ranking the different items from memory, and so in post processing items that were assigned a rank were given a count of 1. In other words, we did away with rankings and simply assessed how frequently different items were selected regardless of rank. In other words the frequency of their selections across all participants should reveal their ranking when assessing all the participants together. Figure 5-2 to Figure 5-5 addresses the data collected regarding accidents and collisions and the figures are self-explanatory The majority of accidents occurred when the cyclist was alone, and the second most common involved a vehicle (Figure 5-2). An overwhelming majority of accidents occur on roads then on intersections and is overwhelmingly caused by the carelessness of road users (pedestrians, cyclists and drivers) as shown in Figure 5-3 and Figure 5-4. This is very crucial as it outlines the importance of safe road design in order to prevent accidents even in cases of carelessness. For instance, cyclists often collide with opening car doors. However, if infrastructure can be designed to eliminate any interaction between cyclists and parked cars to a distance of at least 1.5 lateral meters such as with barriers or designated parking and cycling lanes, then a door incident becomes impossible. Figure 5-5 reveals an interesting observation that most accidents do not involve collisions and if they do collisions often occur with inanimate objects or motorists. The next set of questions involves close calls and were asked to 87 participants. Figure 5-6 to Figure 5-8 asks all of the same questions but within the context of a close call. The questions received the same treatment as the questions on accidents in terms of the ranking modification procedure. Interestingly while accidents mostly occurred to cyclists on their own, close calls overwhelming occurred with vehicles (Figure 5-6). This suggests that the potential for more accidents between cyclist and motorists is very high, even though the probability of an accident occurring is much lower. Figure 5-7 shows that unsurprisingly most close calls 83 occurred on roads, driveway intersections and road intersections. This is a very important finding as it outlines the focus that should be put into addressing intersection design when pertaining to cyclists and also how driveways line of site should be addressed. Similar to accidents, Figure 5-8 reveals that inattention on the part of the road user, either the motorist or the cyclist, is the cause of the vast majority of close calls. Hence, it reiterates the need for robust infrastructure designs that do not depend on road user attention. In other words, the design should be such that a slip in attention is expected of road users. It should the noted in the following figures Figure 5-2 through to Figure 5-8, the terminology cycling âaccidentâ is used to refer to cycling âcollisionsâ or âcrashesâ. âAccidentâ is an outdated terminology as it suggests that there is nothing that can be done about traffic crashes when in fact crashes are predictable events that can be reduced by better education, training, knowledge and design. Since âaccidentâ was the term utilized in the surveys the same term will be used in the figures to maintain consistency. The term âcrashâ was added in parentheses within the thesis only and not the survey. 84 Figure 5-2. Group D Data - Cycling Accident (Crash) Involvement Details 52 1 5 10 43 0 10 20 30 40 50 60 none pedestrians other cyclists cars by myself As a Child, my Cycling accidents (crashes) mostly involve 36 3 8 27 44 0 5 10 15 20 25 30 35 40 45 50 None pedestrians other cyclists cars by myself As an adult my cycling accidents (crashes) mostly involve 85 Figure 5-3. Group D Data - Cycling Accident (Crash) Locations Details Figure 5-4. Group D Data - Cycling Accident (Crash) Causes Details 15 62 11 7 31 12 10 0 10 20 30 40 50 60 70 Pathway Road Sidewalk Parking Lot Road Intersection Driveway Intersection Other Where did accidents (crashes) take place? 24 12 43 11 19 2 20 7 0 3 7 14 27 0 5 10 15 20 25 30 35 40 45 50 What Caused the Accident (Crash)? 86 Figure 5-5. Group D Data - Cycling Collision Involvement Details 27 10 27 4 3 45 13 0 5 10 15 20 25 30 35 40 45 50 What did you collide with? 87 Figure 5-6. Group D Data - Cycling Close Call Involvement Details 32 2 6 21 12 0 5 10 15 20 25 30 35 none pedestrians other cyclists cars by myself As a child my cycling close calls mostly involve 11 9 6 60 11 0 10 20 30 40 50 60 70 none pedestrians other cyclists cars by myself As an adult my cycling close calls mostly involve 88 Figure 5-7. Group D Data - Cycling Close Call Location Details Figure 5-8. Group D Data - Cycling Close Call Causes Details 21 49 12 16 44 28 1 0 10 20 30 40 50 60 Pathway Road Sidewalk Parking Lot Road Intersection Driveway Intersection Other Where did close calls take place? 52 40 20 23 19 10 3 13 8 4 4 8 2 0 10 20 30 40 50 60 What caused the Close-Calls 89 6 Analysis and Discussion This chapter features an analysis of the effect of bicycle lane width as well as BCSPM development. It is divided into five sections. The first four sections pertain to model development beginning with determining the appropriate response variable(s) to use in measuring comfort and safety. This is followed by the next section which discusses the development and utilization of the cyclist typology in this thesis. The third section discusses the input variables that will be utilized in the prediction model. The fourth section walks through the process of fitting the final model itself. Finally, there is one last section that discusses the importance of bicycle lane width. 6.1 Model Development - The Response Variables The dependent variables are the responses provided by the participants to nine questions regarding safety, safety in regards to adjacent traffic, comfort, surface roughness, enjoyment, weather, bike lane width and the presence of adjacent driveways. It is paramount that the relationship between these response variables are understood and addressed before proceeding further with model fitting. In this section, we analyse the relationship between the nine variables then narrow our focus to a single canonical variable constructed from just two of the nine variables. This section begins with a few observations from the means of each response variable in Chapter 6.1.1, followed by a discussion on the results of Pearson correlation analysis in Chapter 6.1.2. A PCA procedure is then performed on the nine variables in Chapter 6.1.3 in order to narrow down the focus on the most important response variables. Finally, a series of T-Tests are performed on the remaining response variables in section 6.1.4, leading to the development of the Safe-Comfort Index which is the canonical variable that represents the overall safety and comfort perceived by a cyclist. 6.1.1 Observations of Means (Group C Data) Table 6-1 outlines the mean and standard deviations of each response variable for 71 participants. The ratings that are reported are mean values from a Likert scale ranging from 1 to 7, however they have been re-scaled and assigned values ranging from 0 to 10. All variables 90 have also been re-scaled so that desirable values are consistently represented by larger ratings (ie 10) for ease of analysis. Figure 6-1 depicts the reported means data as a series of bar graphs. The driveway and lane variables are not included in Figure 6-1 as these variables are not applicable to all sections Figure 6-1. Group C Data - Bar plot of the mean responses variables Clear relationships between the response variables can be observed from Table 6-1 and Figure 6-1. In particular, the variables Safe, Comf, Enjoy and SafeTraffic appear to be correlated as they rise and fall together across different sections. Correlations between variables in this study suggests two possibilities: 1. First, that a participant may be interpreting different survey questions interchangeably or in a similar fashion. For example, comfort (Comf) and safety (Safe) may be seen as 0.0000 1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 10.0000 Section 1 Section 2 Section 3 Section 4 Safe SafeTraffic Comf Enjoy Weather Surf Slope 91 synonyms for some participants despite the efforts in defining them as separate aspects at the start of the test ride. 2. Second, these variables are aspects of a broader dimension. For example, the variables of, enjoyment (Enjoy), slope, weather and surface roughness (Surf) may all be different aspects of how pleasant a ride is; while the presence of adjacent driveways (Driveway), lane width (Lane) and safety in regards to traffic (SafeTraffic) are all aspects that contribute to all how exposed to danger a rider feels. Table 6-1. Group C Data - Mean response values (range: 0,10) for all 9 response variables on each road section N= 71 Section 1 Section 2 Section 3 Section 4 Variables Mean Std. Deviation Mean Std. Deviation Mean Std. Deviation Mean Std. Deviatio n Safe 7.782 2.075 6.807 2.361 9.272 1.186 9.296 1.041 SafeTraffic 7.453 2.163 6.502 2.518 10.000 0.000 8.404 2.364 Comf 7.981 1.970 6.761 2.759 8.099 2.259 9.601 0.770 Surf 9.671 0.827 8.662 2.082 5.751 3.471 9.812 1.037 Slope 8.192 2.639 7.911 2.713 8.732 2.365 9.601 1.422 Enjoy 6.244 2.369 5.563 2.886 8.873 1.430 9.014 1.339 Weather 8.052 2.456 7.981 2.534 8.967 1.699 8.944 1.809 Lane 8.169 2.393 5.822 3.356 - - - - Driveways - - 6.221 3.162 - - 7.019 3.272 Figure 6-1 and Table 6-1 reveal several observations of interest which are discussed below: Observation 1 â Safety is related to a presence of vehicular traffic The variable Safe has a mean value of 7.782 in section 1 and falls to 6.807 in section 2. Both of these sections requires that the participant ride on a bike lane adjacent to vehicular traffic. The slightly better rating for section 1 is likely indicative of a newly paved and wider 2.1 m bike lane with a double separation line between the vehicle lane and bike lane. The variable Safe then improves to a mean value of 9.272 and 9.296 in section 3 and section 4 respectively. In section 3 there was no possible interaction with motor vehicles as this path was an off road gravel path following a riverbank. Section 4 featured nearly no interaction at all with a motor vehicle being a local residential street, although the potential of vehicular traffic was still 92 present. It appears from Table 6-1 that the scores for the variable safe are related directly with the presence of traffic. Observation 2 â Traffic safety consistently performed worse than general safety The variable SafeTraffic is obtained from asking participants how safe they felt specifically in regards to adjacent motor vehicular traffic. SafeTraffic is observed to consistently perform worse than the variable Safe which asks the participant how safe they felt without specifying vehicular traffic or any other factor. This was true for all sections except section 3 which saw no possibility of interaction with vehicular traffic. Within the survey, the question pertaining to the variable Safe was always asked before the question pertaining to variable SafeTraffic. This was designed purposely to avoid a focus on vehicular traffic, in order to avoid leading the participant in their perceptions. What was expected is that the variable Safe would carry a higher score than the variable SafeTraffic. It was presumed that the variable Safe would account for the total feeling of safety while the variable SafeTraffic accounts only for safety in regards to vehicular traffic. It is likely that not all people are able to make this distinction when reporting perceptions. Traffic safety ratings consistently perform worse than the general safety rating in all sections except Section 3 which sees absolutely no encounters with motor vehicles. It should be noted that some responses in Section 3 were manually changed to 10 as some participants accounted for pedestrian and other cyclist traffic within their rating of traffic safety. With SafeTraffic always performing worse, it suggests that the participant, once asked about traffic safety, became analytically critical or aware rather than going with initial feelings of safety. It appears that participants began to think about how safe they ought to feel rather than report their feeling of safety. Since decisions are often made unconsciously, this suggests that the ratings from general safety more accurately represent the perception one would use to make decisions about cycling. In other words, the question of general safety is argued to be more relevant to our study in predicting cycle network desirability. 93 Observation 3 â Safety affects comfort In Table 6-1 we can see that the score of variable Comf is nearly identical to variable Safe in areas where there is close interaction with vehicular traffic such as in Sections 1 and Sections 2. In Section 3 where there is no vehicular traffic interaction and Section 4 where interaction to vehicular traffic was only a potential, the variable Comf carries a distinct value from the variable Safe. This suggests that participants cannot differentiate between safety and comfort unless the safety needs are met first. In other words, participants appear to feel uncomfortable if they feel unsafe. Within Section 3 and Section 4 (which experienced no traffic at all) once the source of risk (i.e. adjacent vehicular traffic) was removed the participant was able to discern feelings of comfort unique from the feeling of safety. Simply put, the data suggests that it is possible to feel uncomfortable because one feels unsafe despite a comfortable physical environment (i.e. smooth pavement, very low gradient, wide lanes). Observation 4 - Comfort does not affect safety Interestingly, it is also possible to feel uncomfortable, but safe. For instance, Section 3 sees a tremendous improvement in safety compared to Section 1 and Section 2. Recall that participants are cycling on asphalt in both Section 1 and Section 2 while they are cycling on gravel in Section 3. Due to this, we expect Section 1 and Section 2 to carry a more desirable comfort score than Section 3. This is not the case as observation 3 pointed out, the comfort score was increased due to high safety score. The variable Safe in Section 3 is 9.272 which is more desirable than the Comf score of 8.099. Interestingly, a less desirable comfort score will not affect the safety score. In other words, poor feelings of safety damages feelings of comfort. In contrast, poor feelings of comfort will not damage feelings of safety. Observation 5 â Enjoyment follows the same pattern as feeling of safety Table 6-1 and Figure 6-1 shows the variable Enjoy following an identical pattern to the variable Safe. However, the variable Enjoy always performs worse, having a lower, and therefore less desirable mean value. This is a logical observation because it indicates that safety is just one aspect of enjoyment. 94 Observation 6 â Lane width relates to safety Section 1 features a wider 2.1 m wide bike lane Section 2 has a 1.5 m wide bike lane) and a double line (Section 2 has a single line) to separate the bike lane from the vehicle lane. It is not surprising that Section 1 scores better in the variable Lane with a mean value of 8.169 (see Table 6-1) compared to a mean value of 5.822 for Section 2. The improvement in bike lane design from Section 2 to Section 1 is likely a contributing factor to the improvement in the mean safety scores. Observation 7 â No discernable pattern between surface roughness and comfort Typically, a correlation between surface roughness and comfort is expected. However, the variable Surf and the variable Comf does not follow the same pattern. In fact, the variable Comf scored a mean value of 9.601 in Section 4, which is a more desirable value than its scores of 7.981 and 6.761 in Section 1 and Section 2 respectively. Simultaneously, the variable Surf carries a relatively equal if not slightly poorer score in Section 4 compared to Section 1 and Section 2. It is clear surface roughness does play a role in comfort, as can be seen when we compare the Section 3 and Section 4 values of the variables Surf and Comf. However, it is undeniable that other factors are affecting comfort to a high degree. The exceptional performance of Comf in Section 4 is likely attributed to both a good level of safety, surface roughness and also its pleasant residential surroundings. Observation 8 â The variable Weather changes depending on the section The score for the variable Weather is collected from a survey questions asking about how comfortable the weather was for the participant. A correlation between the variable Weather and the section is not expected since the weather remains relatively constant within the time frame of a test ride. However, Section 3 does carry better score for the variable Weather over other sections. This is likely due to the fact that the environment of Section 3 being within a forest and next to a river contributes to a very desirable riding environment. However, what is more surprising is that Section 4 also carries a better score for the variable Weather compared to the scores in Section 1 and Section 2. Section 1,2 and 4 are similar in that they do not have tree coverage and all three sections are ridden on the road. What this suggests is that when participants are pre-occupied with vehicular traffic they cannot appreciate good weather. 95 6.1.2 Observations of Correlations (Group D Data) Pearson correlation analysis was conducted on all response variables for each Section ridden in order to follow up on the observations made in Chapter 6.1.1. Group D data was utilized as data was not being compared across Sections. The range in the number of samples used in the calculations of each Pearson Correlation statistic is indicated in the N values provided for each table. Different N values are present as some response variables were added into the survey midway through the experiments, some participants left certain responses blank, and Section 4 was added as the fourth Section part way through the experiment also. Correlations highlighted in green indicate a p-value of 0.05 or better which indicates a strong likelihood of a real correlation between the variables to a 95 confidence level or better. In Table 6-2 it is clear that all response variables are highly correlated with each other, except for the variable pertaining to slope. Likely this is because the slope on Section 1 is relatively flat for all participants, and remained consistent throughout all test rides. In particular, there is correlation of 0.902 between the variables Safe and SafeTraffic, a correlation of 0.685 between Safe and Comf and a correlation of 0.508 between Safe and Enjoy indicating that these four measures are tend to rise and fall together. Table 6-2. Group D Data - Pearson Correlation of Section 1 Response Variables Pearson Correlations (N = 90 to104) Variables S1 _S af e S1 _S af et ra ffi c S1 _C om f S1 _S ur f S1 _S lo pe S1 _E nj oy S1 _W ea th er S1 _L an e S1_Safe 1 .902** .685** .277** .085 .508** .359** .446** S1_Safetraffic .902** 1 .664** .271** .054 .476** .298** .368** S1_Comf .685** .664** 1 .319** .158 .608** .490** .463** S1_Surf .277** .271** .319** 1 .103 .275** .166 .526** S1_Slope .085 .054 .158 .103 1 .001 .025 .046 S1_Enjoy .508** .476** .608** .275** .001 1 .436** .499** S1_Weather .359** .298** .490** .166 .025 .436** 1 .409** S1_Lane .446** .368** .463** .526** .046 .499** .409** 1 **. Correlation is significant at the 0.01 level (2-tailed) â highlighted green 96 Table 6-3 represents Section 2 and in a similar fashion shows significant correlation in the four main response variables. Slope is now correlated with other response variables indicating that the steeper grade of 1.4% of Section 2 compared to the 1.1% of Section 1 made a significant difference such that participants started to have varying opinions on the level of discomfort attributed to slope. As expected, Driveways were a concern and was correlated with Safe, SafeTraffic and Comf but was not correlated with all other response variables. Table 6-3. Group D Data - Pearson Correlation of Section 2 Response Variables Pearson Correlations (N = 90 to 104) Variables S2 _S af e S2 _S af eT ra ffi c S2 _C om f S2 _S ur f S2 _S lo pe S2 _E nj oy S2 _W ea th er S2 _L an e S2 _D riv ew ay s S2_Safe 1 .910** .700** .242* .272** .499** .264* .650** .300** S2_SafeTraffic .910** 1 .667** .249* .209* .479** .238* .585** .329** S2_Comf .700** .667** 1 .404** .322** .695** .445** .734** .318** S2_Surf .242* .249* .404** 1 .192 .278** .193 .497** .112 S2_Slope .272** .209* .322** .192 1 .230* .215* .324** .154 S2_Enjoy .499** .479** .695** .278** .230* 1 .422** .654** .206 S2_Weather .264* .238* .445** .193 .215* .422** 1 .333** .053 S2_Lane .650** .585** .734** .497** .324** .654** .333** 1 .318** S2_Driveways .300** .329** .318** .112 .154 .206 .053 .318** 1 **. Correlation is significant at the 0.01 level (2-tailed) â highlighted green *. Correlation is significant at the 0.05 level (2-tailed) â highlighted green Table 6-4 represents Section 3 and shows significant correlations between all response variables except Weather. It is likely that Weather performed consistently within a forested environment as previously stated in Chapter 6.1.1. Pearson correlation could not be calculated for SafeTraffic as there was no variation in its reported value. 97 Table 6-4. Group D Data - Pearson Correlation of Section 3 Response Variables Pearson Correlations (N = 92 to 104) Variables S3 _S af e S3 _S af eT ra ffi c S3 _C om f S3 _S ur f S3 _S lo pe S3 _E nj oy S3 _W ea th er S3_Safe 1 .a .397** .242* .364** .438** .327** S3_SafeTraffic .a .a .a .a .a .a .a S3_Comf .397** .a 1 .636** .356** .538** .059 S3_Surf .242* .a .636** 1 .384** .326** .003 S3_Slope .364** .a .356** .384** 1 .347** .028 S3_Enjoy .438** .a .538** .326** .347** 1 .051 S3_Weather .327** .a .059 .003 .028 .051 1 **. Correlation is significant at the 0.01 level (2-tailed) â highlighted green *. Correlation is significant at the 0.05 level (2-tailed) â highlighted green a. Cannot be computed because at least one of the variables is constant. Table 6-5. Group D Data - Pearson Correlation of Section 4 Response Variables Pearson Correlations (N = 85) Variables S4 _S af e S4 _S af eT ra ffi c S4 _C om f S4 _S ur f S4 _S lo pe S4 _E nj oy S4 _W ea th er S4 _D riv ew ay s S4_Safe 1 .385** .574** .047 .386** .214* .057 .500** S4_SafeTraffic .385** 1 .330** .010 .102 .144 .035 .557** S4_Comf .574** .330** 1 .338** .080 .365** .180 .289** S4_Surf .047 .010 .338** 1 .078 .010 -.040 .029 S4_Slope .386** .102 .080 .078 1 -.009 -.036 .323** S4_Enjoy .214* .144 .365** .010 -.009 1 .366** .086 S4_Weather .057 .035 .180 -.040 -.036 .366** 1 -.066 S4_Driveways .500** .557** .289** .029 .323** .086 -.066 1 **. Correlation is significant at the 0.01 level (2-tailed) â highlighted green *. Correlation is significant at the 0.05 level (2-tailed) â highlighted green Table 6-5 represents Section 4, and it is immediately apparent that many of the previously significant correlations no longer are in this Section. This is not surprising as Section 98 4 is a very pleasant riding environment that invites agreement in perception among cyclists. With consistent values across many samples, a variableâs correlation with other variables will decrease as it is no longer varying as significantly. The important correlations still hold true, which is that between the variable Safe, Comf and SafeTraffic. Some correlations appear erroneous such the correlation between Driveways and Slope, while others are expected such as those between Enjoy and Weather, and Surf and Comf. 6.1.3 Principle Components Analysis (Group D Data) Following the observations from Chapter 6.1.2, a Principle Components Analysis (PCA) is performed on each ride section. PCA is a method of dimension reduction that creates a smaller number of principle components from a larger data set of variables allowing more meaningful interpretation of any underlying patterns from the larger highly correlated data. In this thesis we consider the 9 response variables discussed in Chapter 6.1.2. Observations have already been made that many of these variables are correlated. PCA will allow us to reveal any underlying pattern between the responses and can help reduce the number of variables from nine to just one or two. Some response variables may relate with some but not others, so the PCA may be able to identify to which group(s) different variables belong. Group D Data was selected because the variability between the responses themselves is primarily being assessed rather than their relationship across sections. Selecting the number of principal components is a balancing act that requires knowledge about the data coupled with results of the PCA. The scree plot can be used to visually determine how many significant components that the procedure can reasonably identify. This can be done by identifying the elbow of the scree plot. The elbow method is a heuristic approach where the eigenvalues of each principal component is plotted in the vertical axis against the principle component number across the horizontal axis. A larger eigenvalue indicates that the principle component associated with it is able explain more variance in the data. As the number of principle components are increased (up to a maximum equal to the number of original variables) each additional principal component is able to explain less variance in the original data, and thus this manifests as an eigenvalue that is calculated to have a relatively lower value. Figure 6-2 shows the scree plot for the PCA of all Sections and is essentially a line graph that depicts the total variance explained by each component based on the size of the eigenvalue. 99 Figure 6-2. Group D Data - Scree plots for Section 1, Section 2, Section 3 and Section 4 (Clockwise from top left) Table 6-6 shows each component generated in PCA up to the maximum number (which is the number of original variables) and the percent of variance in the data that is explained by each component and its associated eigenvalue. Principle components eigenvalues highlighted in green indicate those which parallel analysis methodology applied (OâConnor, 2000) determined was statistically significant to keep in the analysis. Parallel analysis works by generating random datasets with the same number of variables and observations as the data set of interest and then calculating eigenvalues for the correlation matrix of the randomly generated data. A comparison between the eigenvalues generated from the random data sets can be made with the 100 eigenvalues of the PCA performed on the data set of interest. The objective is to only keep components which have eigenvalues greater than the eigenvalues from randomly generated datasets, since the results of the randomly generated datasets represent the effect of random noise. In our parallel analysis, the number of datasets generated was set at the typical standard of 100 and the percentile set at 95 for the generated eigenvalues. Overall, the results indicate that there is only one underlying component for Section 1 through 3 and two for Section 4. The meaning behind this is discussed further in this chapter. Table 6-6. Group D Data - PCA Variance explained by each component Section 1 Section 2 Component Initial Eigenvalues Component Initial Eigenvalues Total % of Variance Cumulative % Total % of Variance Cumulative % 1 3.743 46.788 46.788 1 4.387 48.746 48.746 2 1.104 13.803 60.591 2 1.051 11.678 60.424 3 1.011 12.64 73.231 3 0.848 9.426 69.85 4 0.835 10.432 83.664 4 0.833 9.256 79.106 5 0.527 6.587 90.25 5 0.775 8.611 87.717 6 0.404 5.051 95.301 6 0.545 6.054 93.771 7 0.269 3.368 98.669 7 0.275 3.056 96.827 8 0.106 1.331 100 8 0.21 2.334 99.162 9 0.075 0.838 100 Section 3 Section 4 Component Initial Eigenvalues Component Initial Eigenvalues Total % of Variance Cumulative % Total % of Variance Cumulative % 1 2.608 43.46 43.46 1 2.607 32.594 32.594 2 1.177 19.609 63.068 2 1.451 18.143 50.736 3 0.77 12.834 75.903 3 1.126 14.08 64.817 4 0.685 11.411 87.314 4 0.925 11.557 76.374 5 0.47 7.829 95.144 5 0.659 8.239 84.613 6 0.291 4.856 100 6 0.574 7.18 91.793 7 0.39 4.881 96.674 8 0.266 3.326 100 Significant components highlighted green according to parallel analysis methodology applied (OâConnor, 2000) 101 Once the appropriate number of components to extract is determined, PCA is conducted once again, however, this time the number of components extracted is set at number dictated by the previously run parallel analysis. Rotations are a part of PCA and are mathematical procedures that rotates the factor axis. The direct oblimin rotation method is utilized, so that the factors can be correlated with each other. Varimax rotation is an alternative, however it forces orthogonal components. Completely orthogonal relationships do not typically occur naturally and in order to create components that yield the greatest variability in the data the direct oblimin rotation method was chosen. Table 6-7 outlines the communalities that are outputted by the PCA based on 1 component extraction for Section 1 through 3 and two component extraction in Section 4. The extraction column indicates the percentage of variance for each variable that is being accounted for by the extracted component. The larger the percentage the more the PCA can explain the variation in that variable with the extracted component. All variables with extraction values above 0.5 for each section are highlighted in green. 102 Table 6-7. Group D Data â Communalities for 1 component extraction for Section 1 through 3 and 2 Component Extraction for Section 4. Extraction Method: Principal Component Analysis Section 1 Section 2 Variable Initial Extraction Variable Initial Extraction S1_Safe 1.000 .716 S2_Safe 1.000 .733 S1_SafeTraffic 1.000 .649 S2_SafeTraffic 1.000 .673 S1_Comf 1.000 .705 S2_Comf 1.000 .812 S1_Surf 1.000 .236 S2_Surf 1.000 .267 S1_Slope 1.000 .017 S2_Slope 1.000 .204 S1_Enjoy 1.000 .555 S2_Enjoy 1.000 .569 S1_Weather 1.000 .363 S2_Weather 1.000 .235 S1_Lane 1.000 .502 S2_Lane 1.000 .725 S2_Driveways 1.000 .169 Section 3 Section 4 Variable Initial Extraction Variable Initial Extraction S3_Safe 1.000 .424 S4_Safe 1.000 .679 S3_Comf 1.000 .670 S4_SafeTraffic 1.000 .472 S3_Surf 1.000 .516 S4_Comf 1.000 .643 S3_Slope 1.000 .410 S4_Surf 1.000 .052 S3_Enjoy 1.000 .549 S4_Slope 1.000 .350 S3_Weather 1.000 .039 S4_Enjoy 1.000 .627 S4_Weather 1.000 .563 S4_Driveways 1.000 .673 All variables with extraction values above 0.5 for each section are highlighted in green The components themselves are created by PCA to maximize variance and hence they can be interpreted as the underlying factor(s) that other variables are attempting to measure. Understood from that angle it is apparent in Section 1 and Section 2 that the variables Safe, Comf, SafeTraffic, Enjoy and Lane are all pointing to or attempting to measure some underlying factor. Given the analysis so far, it can be assumed that since vehicular traffic is present and 103 interacting with cyclists in Section 1 and Section 2, this underlying factor is the feeling of safety. Within Section 3 we see the variables that take precedent is Comf, Surf and Enjoy. The variable of SafeTraffic and Lane does not apply to Section 3 and this fact has also seemed to remove Safe from the list of variables whose variance is most explained by the component. This seems to suggest that the feeling of comfort is now taking precedent in Section 3. In Section 4 we see elements of both comfort and safety as the variable Safe and Driveways are now present along with Weather. Throughout all Sections the variables Safe and Comf consistently appear to be variables greatly explained by the PCA, suggesting that these variables best represent the principle component (ie. perception of the cyclist). Table 6-8 shows the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) and Bartlettâs Test of the data. These tests confirm that performing a PCA is appropriate. The general rule is that a KMO of 0.6 or greater indicates that a PCA is appropriate. Bartlettâs test is significant indicating that it is appropriate to perform the PCA Table 6-8. Group D Data - KMO and Barlettâs Test Section 1 Section 2 Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .776 Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .819 Bartlett's Test of Sphericity Approx. Chi- Square 329.069 Bartlett's Test of Sphericity Approx. Chi- Square 435.724 df 28 df 36 Sig. .000 Sig. .000 Section 3 Section 4 Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .686 Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .615 Bartlett's Test of Sphericity Approx. Chi- Square 132.938 Bartlett's Test of Sphericity Approx. Chi- Square 150.062 df 15 df 28 Sig. .000 Sig. .000 Finally, Table 6-9 summarizes the component loadings for each variable to the rotated extracted components for each Section and ordered from greatest to lowest in value. The greater the component loadings of each variable on the component the greater their variation in the direction of that component. In other words, assessing this table will allow variables that are measuring similar factors to be grouped together. After examining the means, Pearson 104 correlations and communalities for these variables previously, the conclusions drawn from Table 6-9 should not be surprising. The variables are highlighted according to the component they most represent in cases where more than 1 component is present. Table 6-9. Group D Data - PCA Pattern Matrix for each Section. Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization. Component Matrix Component Matrix Section 1 Component Section 2 Component 1 1 S1_Safe .846 S2_Comf .901 S1_Comf .840 S2_Safe .856 S1_Safetraffic .805 S2_Lane .852 S1_Enjoy .745 S2_SafeTraffic .820 S1_Lane .708 S2_Enjoy .754 S1_Weather .603 S2_Surf .517 S1_Surf .486 S2_Weather .485 S1_Slope .132 S2_Slope .452 S2_Driveways .411 Component Matrix Pattern Matrix Section 3 Component Section 4 Component 1 1 2 S3_Comf .818 S4_Driveways .830 -.125 S3_Enjoy .741 S4_Safe .779 .176 S3_Surf .718 S4_SafeTraffic .673 .071 S3_Safe .651 S4_Slope .576 -.248 S3_Slope .640 S4_Surf .157 .143 S3_Weather .198 S4_Enjoy .045 .784 S4_Weather -.193 .754 S4_Comf .508 .549 Rotation Method: Oblimin with Kaiser Normalization. 9 iterations converged. The component loadings are highlighted according to the component they most represent in cases where more than 1 component is present Comfort and Safety are the two variables clearly present and contributing the majority of the variation. This means they are the most important question to ask a cyclist. It seems in a situation involving potential interaction with traffic, safety is the main factor for Section 1 and 2 105 and a situation with no traffic interaction, comfort is the main question in Section 3. Finally in a situation with low traffic like Section 4, both questions can be asked. Comfort appears well represented in both components. It can be presumed that component 1 represents safety and component 2 represents comfort. This is clear when variables such as Driveways, Safe, SafeTraffic represent component 1 while Enjoy, Weather and Comf represent component 2. The definition of comfort seems to change depending on the context of the Section the cyclist is riding. PCA seems to show 1 component for safety and 1 component for comfort depending on the priority of the participant given the context. In section 1 and 2 safety is dimension 1, in section 3 comfort is dimension 1 and in section 4 both dimensions are important, however the safety remains the priority. 6.1.4 Safe-Comfort Index (Group C Data) It was clear that variables of Safe and Comf held the most influence and in the following analysis were examined more closely with paired sample T-Tests and Pearson Correlations using Group C Data. The Pearson Correlation results shown in Table 6-10 show a positive correlation between Safe and Comf in all sections with Section 1,2 and 4 showing sig. values of less than 0.001. The same story is told in the paired sample T-Test results in where means are shown to not be statistically different in Sections 1, 2 and 4. These results confirm that in Sections 1, 2 and 4 Safe and Comf are collinear and can be represented by the either variable. In Section 3 the results indicate that Safe and Comf are measuring different factors. Both Safe and Comf should be used together to measure the desirability of cycling infrastructure, and so their average is taken to be a measure of the perceived âwellnessâ of the cyclist. At this point forward all the response variable utilized in the study will be the âWellâ canonical Safe-Comfort index constructed using the average of Safe and Comf ratings. 106 Table 6-10. Group C Data â Descriptive Statistics and Pearson Correlation between Safe and Comf for each Section Paired Groups Mean N Std. Deviati on Std. Error Mean Pearson Correlation Correlatio n Sig Pair 1 S1_Safe 7.7817 71 2.07466 0.24622 0.660 0.000 S1_Comf 7.9812 71 1.97027 0.23383 Pair 2 S2_Safe 6.8075 71 2.3611 0.28021 0.717 0.000 S2_Comf 6.7606 71 2.75857 0.32738 Pair 3 S3_Safe 9.2722 71 1.18635 0.14079 0.202 0.091 S3_Comf 8.0985 71 2.2589 0.26808 Pair 4 S4_Safe 9.2957 71 1.04132 0.12358 0.585 0.000 S4_Comf 9.6009 71 0.7698 0.09136 Table 6-11. Group C Data â Paired Sample T-Test between Safe and Comf for each Section Paired Differences t df Sig. (2- tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 S1_Safe - S1_Comf -.19949 1.66937 .19812 -.59463 .19564 -1.007 70 .317 Pair 2 S2_Safe - S2_Comf .04693 1.96125 .23276 -.41729 .51115 .202 70 .841 Pair 3 S3_Safe - S3_Comf 1.17370 2.32980 .27650 .62225 1.72516 4.245 70 .000 Pair 4 S4_Safe - S4_Comf -.30517 .85961 .10202 -.50863 -.10170 -2.991 70 .004 6.2 Model Development - The Cyclist Typology Cluster analysis allows us to organize a data set into groups based on similarities in how each data point scores on a number of chosen variables within the data set. In this thesis, survey questions regarding participant demographics, riding frequency, bicycle accident frequency, parental encouragement in cycling, cycling perceptions and opinions on cycling infrastructure were collected for the very purpose of grouping participants into different cyclist typologies. The next several sections of this chapter detail the initial variable screening that took place to remove certain variables from the cyclist grouping procedure, the secondary screening 107 which utilized PCA and the K-Means clustering algorithm utilized to sort cyclists into different typologies. Finally, a section is included which describes how a new cyclist not from this dataset can be assigned to a cyclist typology constructed from this dataset. 6.2.1 Initial Variable Screening using Two-Step Cluster Analysis (Group D Data) Many different combinations of variables were explored. Initial clustering attempts utilized Two-Step Clustering Analysis and through trial and error it was found that a participantâs opinions on cycling infrastructure variables yielded the best results in terms of grouping participants for our purpose of analyzing comfort and safety perception. Participantsâ responses to perception questions were not included in the clustering analysis as these questions were found to be confusing to the participants. It was observed that many participants asked for clarification when answering questions in the survey. Demographic data such as gender and age was also found to be a poor variable for grouping participants for the purpose of analyzing comfort and safety perception. This may seem counterintuitive at first; for instance one would expect that younger men be more tolerant of dangerous situations than older women. However, while this is true in the general population as a whole, the participants in this study is largely a self-selected group of cyclists comfortable enough to ride on the road to participate in the study. The further implications of having a self-selected cyclist sample group are discussed later in more detail in other sections of the thesis. Interestingly, bicycle accident data, riding frequency and the amount of parental encouragement were all poor determiners of a cyclistâs perception of comfort and safety. The reason bicycle accident data is a poor indicator in this dataset is that regardless of having an accident or not, this is a group of participants that are currently comfortable enough to cycle that they would participate in this study. Additionally any lasting significant impacts from a cycling accident would manifest itself in the participantâs opinions on cycling infrastructure. In regards to riding frequency and parental encouragement, the frequency and reason for cycling does not seem to affect ones perception of comfort and safety for this group of participants. It is logical that there can be very experienced riders who do not ride frequently and that riders can be nervous or fearless regardless of encouragement from parents. 108 Many of the survey questions were originally taken from Damant-Sirois et al. (2014) who used his survey to construct a more holistic type of cyclist typology, one that incorporates the full demographic background and riding pattern behind each rider. Part of the aim of Damant- Sirois et al. (2014) was to create a cyclist typology that could help predict what sort of programs would best encourage more cycling from the different cyclist typologies. In contrast, this thesis focuses more specifically on how a cyclists feels when riding a particular section of bicycle infrastructure. With such a focus, the cyclist typology in this thesis is much more similar to the four cyclist types Geller (2009) introduces. 6.2.2 Secondary Variable Screening using Principle Component Analysis (Group D Data) Once it has been determined that it is a cyclistsâ opinions on cycling infrastructure that best groups them into different cyclist typologies, a PCA was conducted in similar fashion to Chapter 6.1.3, this time using the survey questions are variables. The 7 opinion questions analysed from the survey are listed below for reference. How important are the following factors in making a good bicycle route? Low number of cars driving Low speed of cars Low number of parked cars Continuous bicycle route Very unimportant Unimportant Neither important nor unimportant Important Very Important ï¡ ï¡ ï¡ ï¡ ï¡ Very unimportant Unimportant Neither important nor unimportant Important Very Important ï¡ ï¡ ï¡ ï¡ ï¡ Very unimportant Unimportant Neither important nor unimportant Important Very Important ï¡ ï¡ ï¡ ï¡ ï¡ Very unimportant Unimportant Neither important nor unimportant Important Very Important ï¡ ï¡ ï¡ ï¡ ï¡ 109 Presence of bicycle path with a physical barrier Bicycle specific signage Flat route To narrow down which opinion questions are the most important to ask the results of the PCA are analyzed. Figure 6-3 shows the scree plot results and Table 6-12 shows the variance explained by each component extracted by the PCA. Using parallel analysis it was determined that the first two components were most significant. Table 6-13 reveals the component loadings for each Opinion question on the first two components. Figure 6-4 presents the component loadings visually on a 2D plot. Based on the component loadings the top 5 Opinion questions, Opinion 1,2,4,5 and 6 chosen for final clustering analysis. Very unimportant Unimportant Neither important nor unimportant Important Very Important ï¡ ï¡ ï¡ ï¡ ï¡ Very unimportant Unimportant Neither important nor unimportant Important Very Important ï¡ ï¡ ï¡ ï¡ ï¡ Very unimportant Unimportant Neither important nor unimportant Important Very Important ï¡ ï¡ ï¡ ï¡ ï¡ 110 Figure 6-3. Group D Data - Scree plots for Opinion Questions Table 6-12. Group D Data - PCA Variance explained by each component for Opinion Questions Component Initial Eigenvalues Total % of Variance Cumulative % 1 2.141 30.583 30.583 2 1.251 17.869 48.451 3 0.966 13.797 62.248 4 0.875 12.506 74.754 5 0.785 11.219 85.973 6 0.587 8.379 94.352 7 0.395 5.648 100 Extraction Method: Principal Component Analysis. a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance. 111 Table 6-13. Group D Data - PCA Pattern Matrix of Opinion Questions Component 1 2 Opinion1 .911 -.124 Opinion2 .841 .007 Opinion5 .373 .353 Opinion6 -.023 .770 Opinion4 -.168 .716 Opinion3 .268 .428 Opinion7 .015 .425 Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization. a. Rotation converged in 8 iterations. The component loadings are highlighted in different colours according to the component they most represent 112 Figure 6-4. Group D Data â Component plots 6.2.3 K-Means Clustering Analysis Results (Group C and D Data) Through elimination and trial and error, many combinations of Opinion 1, 2, 4, 5 and 6 were utilized within the K-Means Clustering procedure in several trials. The final cluster memberships were calculated using K-means cluster analysis following the technique utilized in a previously successful study (Damant-Sirois et al., 2014). The clustering procedure was conducted in SPSS and was set to terminate when there is no longer any changes in cluster centers from one iteration to the next. The results of the trials are summarized in Table 6-14. Three clusters were first attempted so as to split the cyclists into three distinct groups. It could be assumed that the upper 3 levels of cyclist from Geller (2009) are present in the participant population of this thesis. Final selection was validated on minimizing the number iterations required in completing the K-Means clustering procedure. The less iterations required indicates that groups are more 113 distinct from one another. More importantly, the resulting groups created from K-Means clustering should significantly differ in their Opinions of cycling infrastructure; this was tested in a One-Way ANOVA. Finally, the resulting groups should also differ significantly in the response variable âWellâ, and this also was tested in a One-Way ANOVA. The results of the One-Way ANOVA are included for each trial. A red highlight indicates there was no statistical difference between the groups for the indicated variable (either Opinion or Well) and a green highlight indicates that a significant (sig. value less than 0.05) difference was present between the groups for the indicated variable. Trials 1, 2, and 3 of the K-Means clustering procedure set the number of clusters at 3. However, this showed poor results as the groups that were created showed no statistical difference in their opinions. In other words, these were poor groupings and cyclists in these groups barely differed in their responses to Opinion questions on which the clustering is based. Opinion questions were removed in an elimination procedure. The Opinion question with the largest resulting Sig. Value was removed. From Trial 1 this meant removing Opinion1 for the next trial. From Trial 2, Opinion5 was removed. Finally in Trial 3, Opinion2 and Opinion4 still revealed very insignificant differences, removing these would result in a grouping based only on one Opinion question. It was clear that there were not three viable clusters but two. Trial 4 included all Opinion questions but with conducting K-Means clustering with only two clusters as the objective. In trial 5, Opinion4 was removed. All Opinions in Trial 5 differed significantly between groups and so One-Way ANOVA was conducted on the Well variable for each Section with the results showing no significant differences between the groups. With some trial and error it was found that removing Opinion6 created groups where there was no significant difference in Opinion5. Removing Opinion5 and including Opinion6 in Trial 7 yielded the best results, with significant differences between groups for all Opinion questions utilized in clustering and also for the variable Well in Section 1 and Section 2. Understandably, perceptions in Section 3 and Section 4 do not differ significantly between groups as these Sections are relatively pleasant and agreement between different cyclist groups is expected. 114 Table 6-14. Group C and D Data â K-Means Clustering Trials for Cyclist Typology Trial 1 2 3 4 5 6 7 Number of Clusters 3 3 3 2 2 2 2 D es ig n: O pi ni on Q ue st io ns In cl ud ed Opinion1 yes no no yes yes yes yes Opinion2 yes yes yes yes yes yes yes Opinion4 yes yes yes yes no no no Opinion5 yes yes no yes yes yes no Opinion6 yes yes yes yes yes no yes Iterations 9 6 6 9 6 4 4 G ro up D D at a (N = 10 5) M ax S ig . V al ue B et w ee n G ro up s Opinion1 1.000 - - 0.000 0.000 0.000 0.000 Opinion2 0.099 0.676 1.000 0.000 0.000 0.000 0.000 Opinion4 0.889 0.643 1.000 0.297 - - - Opinion5 0.004 1.000 - 0.000 0.000 0.059 - Opinion6 0.060 0.100 0.000 0.002 0.000 - 0.000 G ro up C D at a (N = 71 ) M ax S ig . V al ue B et w ee n G ro up s S1_Well - - - - 0.066 - 0.037 S2_Well - - - - 0.064 - 0.017 S3_Well - - - - 0.481 - 0.167 S4_Well - - - - 0.677 - 0.598 115 Table 6-15 outlines the details of trial 7 K-Mean clustering. There is a nearly even split between cluster 1 and cluster 2 and the final cluster centers shown can be used to assign new participants to the cyclist typology. Two clusters seemed reasonable, as it is conceivable that the majority of participants would be from the two most confident types of cyclists from Geller (2009) and Damant-Sirois et al. (2014) typologies since they are self-selected volunteers. For instance, it seems plausible that the âinterested but concernedâ group from Gellerâs typology and the âFairweather Utilitarianâ from Damant-Siroisâ typology would not be interested to volunteer in this study. Table 6-15. Group D Data - Trial 7 - K-Means Clustering Summary of Results Initial Cluster Centers Cluster 1 2 Opinion1 5 2 Opinion2 4 2 Opinion6 1 4 Iteration Historya Iteration Change in Cluster Centers 1 2 1 2.259 2.244 2 0.076 0.092 3 0.068 0.076 4 0 0 Final Cluster Centers Cluster 1 2 Opinion1 4.63 3.55 Opinion2 4.46 3.59 Opinion6 3.21 4.04 Number of Cases in each Cluster Cluster 1 56 2 49 Total 105 116 6.2.4 Assigning New Participants to Existing Cyclist Typology The beauty of the K-means clustering procedure is that once cluster centers have been calculated, additional participants can be assigned to a cluster based on their response to the same three questions. The new participant is assigned his/her membership to either cluster 1 or cluster 2 based on the relative distance their responses are to each cluster center in ânâ dimensions, calculated using simple linear geometry formulas. In this thesis three questions were used, Opinion1, Opinion2 and Opinion6 and the distances to cluster centers can be calculated in the equation below. The shorter of the two distances is taken, for instance if a new participant has a response of 4 for Opinion1, Opinion2 and Opinion6, the calculated distance to cluster 1 would be 1.1102 and for cluster 2, 0.61008. Since the responses yields a distance closer to cluster 2 than cluster 1, the participant would be assigned membership to cluster 2. Equation 6-1. Cluster Assignment of new participants ï¿½(ðððððððððððððð1 â 4.63)2 + (ðððððððððððððð2 â 4.46)2 + (ðððððððððððððð5 â 3.21)22 = ð·ð·ððð·ð·ð·ð·ð·ð·ððð·ð·ð·ð· ð·ð·ðð ð¶ð¶ð¶ð¶ð¶ð¶ð·ð·ð·ð·ð·ð·ð¶ð¶ 1 ï¿½(ðððððððððððððð1 â 3.55)2 + (ðððððððððððððð2 â 3.59)2 + (ðððððððððððððð5 â 4.04)22 = ð·ð·ððð·ð·ð·ð·ð·ð·ððð·ð·ð·ð· ð·ð·ðð ð¶ð¶ð¶ð¶ð¶ð¶ð·ð·ð·ð·ð·ð·ð¶ð¶ 2 While this is a straightforward approach, we should be cautious that although we can ask a new participant these three questions to determine their cyclist type, anyone who would have been in the "interested but concerned" category or the "no way no how" category, would likely fall into a cluster which they would not belong. In theory they should belong to a third cluster. For instance, a response of 1 for all opinions would still yield a membership for cluster 2, however, in reality such a response is so far away from both cluster centers that it should belong to a third cluster all together. This is the limitation of the clustering we have with the data available, because the clustering was conducted using 105 volunteer participants who are likely cyclists who are quite comfortable already with the idea of riding on the road. Thus, the clustering assignment set out in will only apply to those cyclists who naturally fall into cluster 1 or 2. Cyclists who fall outside of cluster 1 and 2 would be mistakenly assigned to the nearest cluster, even though they may not belong there. In order to have an assignment process that applies to the entire population, we would need to create cluster centers for all types of cyclists and that would require participants of all typologies to be involved in the study. 117 6.2.5 Analysis of the Cyclist Typology (Group D Data) A final assessment of the cyclist groups was conducted using Group D Data and the results are provided in this chapter. The clusters were assigned the names âLevel 1â and âLevel 2â with the higher level indicating the more confident group of cyclists. One-Way ANOVA was conducted in order to test differences between groups in their perceptions to comfort and safety measured by the Safe-Comfort Index variable âWellâ. ANOVA was performed to assess the difference between the two levels of cyclists against the reported Safe-Comfort Index variable âWellâ for Section 1, Section 2, Section 3 and Section 4. From the Table 6-16, it is apparent that Level 2 cyclists report a higher Safe-Comfort Index compared to Level 1 cyclists in all Sections, suggesting that they are more confident cyclists. A higher Safe-Comfor Index variable âWellâ corresponds to greater feeling of comfort, safety and enjoyment. Table 6-16. Group D Data - Descriptive Statistics of Level 1 and Level 2 Cyclists against Safe- Comfort Index Variable "Well" for all Sections N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimu m Maximu m Lower Bound Upper Bound S1 _W el l Level 1 56 7.1949 2.06648 .27615 6.6415 7.7483 1.67 10.00 Level 2 48 8.4289 1.59700 .23051 7.9651 8.8926 4.17 10.00 Total 104 7.7644 1.95605 .19181 7.3840 8.1448 1.67 10.00 S2 _W el l Level 1 56 6.2947 2.17982 .29129 5.7109 6.8785 1.67 10.00 Level 2 48 7.8039 2.18756 .31575 7.1687 8.4391 1.25 10.00 Total 104 6.9912 2.30053 .22559 6.5438 7.4386 1.25 10.00 S3 _W el l Level 1 56 8.5863 1.42952 .19103 8.2035 8.9691 5.00 10.00 Level 2 48 8.8021 1.54629 .22319 8.3531 9.2511 2.50 10.00 Total 104 8.6859 1.48119 .14524 8.3978 8.9740 2.50 10.00 S4 _W el l Level 1 44 9.4318 .75776 .11424 9.2014 9.6622 7.50 10.00 Level 2 41 9.4513 .84593 .13211 9.1843 9.7183 6.67 10.00 Total 85 9.4412 .79674 .08642 9.2693 9.6130 6.67 10.00 118 The Levene statistic tests in Table 6-17 describes whether or not the dependent variable exhibits equal variances across the different groups. The Null hypothesis states that the dependent variable has equal variances across all groups. Since the Sig. value or P-value is more than 0.05 for S1_Well, S2_Well, S3_Well, and S4_Well, the null hypothesis is not rejected, hence the groups likely have equal variances. This is significant as it validates the ANOVA in Table 6-18. Table 6-17. Group D Data - Test of Homogeneity of Variances of Level 1 and Level 2 Cyclists against Safe-Comfort Index Variable "Well" for all Sections Levene Statistic df1 df2 Sig. S1_Well 3.161 1 102 .078 S2_Well .380 1 102 .539 S3_Well .015 1 102 .904 S4_Well .001 1 83 .980 Table 6-18. Group D Data - ANOVA of Level 1 and Level 2 Cyclists against Safe-Comfort Index Variable "Well" for all Sections Sum of Squares df Mean Square F Sig. S1_Well Between Groups 39.353 1 39.353 11.315 .001 Within Groups 354.739 102 3.478 Total 394.091 103 S2_Well Between Groups 58.866 1 58.866 12.348 .001 Within Groups 486.254 102 4.767 Total 545.120 103 S3_Well Between Groups 1.203 1 1.203 .546 .462 Within Groups 224.772 102 2.204 Total 225.976 103 S4_Well Between Groups .008 1 .008 .013 .911 Within Groups 53.315 83 .642 Total 53.323 84 The ANOVA results in Table 6-18 reveal that there is a significant difference between the groups in Section1 and Section 2 as expected. The Null hypothesis of ANOVA is that there is no difference in the means. In other words, the Null hypothesis for ANOVA signifies that means are the same which is soundly rejected by the sig value of 0.001 for S1_Well and 0.001 119 for S2_Well which is well below 0.05. However, we do see that the null hypothesis is rejected for S3_Well and S4_Well as they have Sig. values of 0.462 and 0.911 respectively. This indicates that there is no significant difference between the two levels of cyclists in their opinions towards Section 3 and Section 4. 6.3 Model Development - The Input Variables Chapter 6.3 reviews the screening process for the input variables that will be used for BCSPM model development. In Chapter 6.3.1 variables pertaining to adjacent vehicular traffic is assessed for Section 1 and Section 2. In Chapter 6.3.2 the effect of the different sections themselves is assessed. 6.3.1 Vehicular Traffic (Group A and B Data) Individual univariate GLMs were conducted for each variable to determine if variation in the variable had any measurable effect on the Safe-Comfort Index for Section 1 and Section 2. The variables screened are all related to motor vehicle traffic volume, passing speed, length, and passing lateral distance. A full description of all variables are provided in Appendix B â Variable Definitions. Table 6-19 and Table 6-20 outlines the results of univariate GLM screening. In Section 1 it is observed that S1_CarAct80Spd significantly affects S1_Well and in Section 2, S2_CarRelAvgSpd, S2_CarRel50Spd, and S2_Car80PerLength significantly affects S2_Well. Consistently, it is vehicle size and passing speed variables that affected the Safe-Comfort Index, however, there is a lack of consistency in the type of measure used. S1_CarAct80Spd measures the 80th percentile actual passing speed of the vehicle. S2_CarRelAvgSpd measures the average relative passing speed of vehicles. S2_CarRel50Spd measures the 50th percentile relative passing speed of the vehicle. Finally, S2_Car80PerLength measures the 80th percentile passing vehicle length. The effect size as measured by Eta Square report values ranging from 0.073 to 0.078 indicating that these variables account for approximately 7 to 8 percent of the variation in S1_Well and S2_Well each. 120 Table 6-19. Group A Data - Univariate GLM Results for Section 1 and Section 2 Dependent Variable Independent Variable ANOVA Mean N Std. Deviation F Sig. Eta Square d S1_Well (Group A Data) S1_CarVol .507 .480 .010 230.196 53 95.69263 S1_CarRelMaxSpd .134 .715 .003 38.6509 53 5.93603 S1_CarRelAvgSpd .025 .876 .000 27.9949 53 2.92472 S1_CarRelMinSpd .618 .435 .012 16.4717 53 5.86746 S1_CarRel80Spd .084 .773 .002 32.5943 53 2.93955 S1_CarRel50Spd .813 .372 .016 28.2689 53 3.30131 S1_CarActAvgSpd 1.882 .176 .036 51.6408 53 3.30856 S1_CarActMaxSpd 2.042 .159 .039 62.6004 53 4.20040 S1_CarActMinSpd .172 .681 .003 37.1306 53 8.73476 S1_CarAct80Spd 4.046 .050 .073 57.1770 53 2.85525 S1_CarAct50Spd 1.224 .274 .023 52.3344 53 3.30647 S1_CarAvgLength .442 .509 .009 4.2542 53 .38355 S1_CarMaxLength .491 .487 .010 6.7943 53 2.92740 S1_CarMinLength .081 .777 .002 3.0274 53 .56027 S1_Car80PerLength 1.184 .282 .023 4.6251 53 .38683 S1_Car50PerLength .390 .535 .008 4.0443 53 .26754 S1_CarAvgPassDist .646 .425 .013 2.1559 53 .20272 S1_CarMaxPassDist 1.273 .264 .024 2.8255 53 .36369 S1_CarMinPassDist .341 .562 .007 1.6991 53 .26917 S1_Car80PerPassDist 2.107 .153 .040 2.3613 53 .21596 S1_Car50PerPassDist .632 .430 .012 2.1245 53 .21171 S2_Car80PerPassDist 1.326 .254 .019 2.2751 72 .22822 S2_Car50PerPassDist 1.173 .283 .016 1.9479 72 .15318 Sig. values less than 0.05 are highlighted As a cyclist will tend to remember the worst situation encountered during a ride and base judgments on those experiences (Furth et al., 2016), it is logical that 50th and 80th percentile variables yield more significance over mean variables. 50th and 80th percentile variables account for the higher range of values to be assessed. What was surprising is that passing distance was not a significant factor and this may be due to having either small variation in passing distances recorded or the group of cyclists in this study may be more sensitive to vehicle speed and size rather than proximity. 121 Table 6-20. Group B Data - Univariate GLM Results for Section 2 Dependent Variable Independent Variable ANOVA Mean N Std. Deviation F Sig. Eta Square d S2_Well (Group B Data) S2_CarVol 2.159 .146 .030 166.954 72 60.89094 S2_CarRelMaxSpd 2.765 .101 .038 41.2431 72 8.19953 S2_CarRelAvgSpd 5.488 .022 .073 27.0892 72 2.79363 S2_CarRelMinSpd .065 .800 .001 15.2222 72 4.81553 S2_CarRel80Spd 2.783 .100 .038 31.4417 72 3.36409 S2_CarRel50Spd 5.863 .018 .077 26.8056 72 3.16484 S2_CarActAvgSpd 2.137 .148 .030 51.2138 72 2.12842 S2_CarActMaxSpd .049 .825 .001 63.7196 72 6.77421 S2_CarActMinSpd 2.086 .153 .029 35.3656 72 9.16251 S2_CarAct80Spd 1.053 .308 .015 55.3686 72 2.23717 S2_CarAct50Spd .378 .541 .005 51.4411 72 2.31338 S2_CarAvgLength 1.185 .280 .017 4.4099 72 .80290 S2_CarMaxLength .684 .411 .010 9.6375 72 11.10523 S2_CarMinLength .349 .557 .005 2.6660 72 .83989 S2_Car80PerLength 5.920 .018 .078 4.7588 72 .37773 S2_Car50PerLength 3.103 .083 .042 4.0663 72 .17594 S2_CarAvgPassDist 2.093 .152 .029 2.0277 72 .13195 S2_CarMaxPassDist .746 .391 .011 3.1938 72 .51798 S2_CarMinPassDist .812 .371 .011 1.3868 72 .24380 S2_Car80PerPassDist 1.326 .254 .019 2.2751 72 .22822 S2_Car50PerPassDist 1.173 .283 .016 1.9479 72 .15318 Sig. values less than 0.05 are highlighted 6.3.2 Infrastructure (Group C Data) In order to measure the effect that different Sections had we used Group C Data to ensure that only the completed Section 1 is included. Paired sample T-Tests and Pearson Correlations were conducted between the Safe-Comfort Index means from each section. It was expected that from the Pearson Correlation statistics in Table 6-21 that the Safe-Comfort Index was well correlated. This was no surprise as the same participants were measured across all Sections. Sig. values for paired sample T-Test shown in Table 6-22 confirmed that the means of all pairs are statistically distinct. This confirms that there is a significant measurable difference in the perception that cyclists hold between the different sections, as expected. 122 Table 6-21. Group C Data - Paired Sample Statistics and Pearson Correlations of Safe-Comfort Index for Section 1 through 4 Paired Samples Statistics Mean N Std. Deviation Std. Error Mean Pearson Correlation Sig. Pair 1 S1_Well 7.8815 71 1.84291 0.21871 0.776 0.000 S2_Well 6.7841 71 2.37293 0.28161 Pair 2 S1_Well 7.8815 71 1.84291 0.21871 0.202 0.091 S3_Well 8.6855 71 1.37775 0.16351 Pair 3 S1_Well 7.8815 71 1.84291 0.21871 0.37 0.002 S4_Well 9.4484 71 0.80853 0.09596 Pair 4 S2_Well 6.7841 71 2.37293 0.28161 0.303 0.01 S3_Well 8.6855 71 1.37775 0.16351 Pair 5 S2_Well 6.7841 71 2.37293 0.28161 0.44 0.000 S4_Well 9.4484 71 0.80853 0.09596 Pair 6 S3_Well 8.6855 71 1.37775 0.16351 0.31 0.008 S4_Well 9.4484 71 0.80853 0.09596 Table 6-22. Group C Data - Paired Sample Test Statistics of Safe-Comfort Index for Section 1 through 4 Paired Differences t df Sig. (2- tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 S1_Well - S2_Well 1.09739 1.49603 .17755 .74329 1.45150 6.181 70 .000 Pair 2 S1_Well - S3_Well -.80396 2.06571 .24515 -1.29290 -.31501 -3.279 70 .002 Pair 3 S1_Well - S4_Well -1.56686 1.71715 .20379 -1.97330 -1.16042 -7.689 70 .000 Pair 4 S2_Well - S3_Well -1.90135 2.35563 .27956 -2.45892 -1.34378 -6.801 70 .000 Pair 5 S2_Well - S4_Well -2.66425 2.14373 .25441 -3.17167 -2.15684 -10.472 70 .000 Pair 6 S3_Well - S4_Well -.76290 1.36391 .16187 -1.08573 -.44007 -4.713 70 .000 123 6.4 Model Development - The Prediction Model In Chapter 6.4 the BCSPMs are developed using CyclistTypology, S1_CarAct80Spd, S2_CarRelAvgSpd, S2_CarRel50Spd, S2_Car80PerLength and the Sections themselves as input variables to predict the Safe-Comfort Index S1_Well, S2_Well, S3_Well and S4_Well. Three separate models will be constructed. Chapter 6.4.1 details the development of a model for Section 1 (predicting S1_Well), Chapter 6.4.2 details the development of a model for Section 2 (predicting S2_Well) and Chapter 6.4.3 details the development of a model for Sections 1 through 4 simultaneously. As stated before, there does seem to be a lack of consistency pertaining to the traffic variable inputs for Section 1 and Section 2. Section 2 sees three available input variables, S2_CarRelAvgSpd, S2_CarRel50Spd, and S2_Car80PerLength while Section 1 only has S1_CarAct80Spd. This could be due to the 2.1 m bicycle lane on Section 1 compared to the 1.5 m bicycle lane on Section 2. In Addition to a wider bicycle lane Section 1 featured a double demarcation lines separating the bicycle lane from the motor vehicle traffic lane. This extra feature may have removed the influence of passing vehicular size on the perception of safety and comfort for Section 2. The extra width and double lines may also have changed which type of vehicular speed variable was most effective for predicting the perception of safety and comfort. It was decided that specific models for Section1 and Section 2 would be constructed for completeness utilizing section specific variables, followed by a complete model that would serve all four sections. 6.4.1 Section 1 Specific Univariate GLM (Group A Data) To complete the model for Section 1, cyclist typology and S1_CarAct80Spd were utilized as independent variables and S1_Well was utilized as the dependent variable. The model was fit using elimination procedure. The first model revealed that S1_CarAct80Spd had a sig. value of 0.97 (larger than 0.05) indicating that it was not a significant variable to include in the model. Removing this variable meant that the Section 1 model would only rely on cyclist typology making a Section 1 specific model no different from one of the four models that the 124 repeated measures GLM would yield later in Chapter 6.4.3. That being said, the Section 1 specific model results are included below for completeness. Table 6-23. Group A Data â Section 1 Specific Model Development Descriptive Statistics Dependent Variable: S1_Well CyclistTypology Mean Std. Deviation N level 1 7.2271 2.17433 29 level 2 8.5243 1.48448 24 Total 7.8145 1.98630 53 Table 6-24. Group A Data â Section 1 Specific Model Development Levene's Test of Equality of Error Variances Dependent Variable: S1_Well F df1 df2 Sig. 2.637 1 51 .111 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. Design: Intercept + CyclistTypology Table 6-25. Group A Data â Section 1 Specific Model Development Tests of Between-Subjects Effects Dependent Variable: S1_Well Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Corrected Model 22.100a 1 22.100 6.157 .016 .108 Intercept 3258.156 1 3258.156 907.709 .000 .947 CyclistTypology 22.100 1 22.100 6.157 .016 .108 Error 183.061 51 3.589 Total 3441.688 53 Corrected Total 205.161 52 a. R Squared = .108 (Adjusted R Squared = .090) 125 Table 6-26. Group A Data â Section 1 Specific Model Development Parameter Estimates Dependent Variable: S1_Well Parameter B Std. Error t Sig. 95% Confidence Interval Partial Eta Squared Lower Bound Upper Bound Intercept 8.524 .387 22.042 .000 7.748 9.301 .905 [CyclistTypology=1.00] -1.297 .523 -2.481 .016 -2.347 -.248 .108 [CyclistTypology=2.00] 0a . . . . . . a. This parameter is set to zero because it is redundant. 6.4.2 Section 2 Specific Univariate GLM (Group B Data) To complete the model for Section 2, cyclist typology and S2_CarRel50Spd, S2_CarRelAvgSpd, and S2_Car80PerLength were utilized as independent variables and S2_Well was utilized as the dependent variable. The model was fit using elimination procedure. The first model that was fitted revealed that S1_CarRelAvgSpd yielded a sig. value of 0.856 (larger than 0.05) and the largest Sig.value of all four variables. Additionally, S1_CarRelAvgSpd also yielded a partial eta squared value of 0.000 indicating that in the model that was fitted not only was its effect not significant, it also contributed nothing in terms of explaining the variability of S2_Well. S1_CarRelAvgSpd was removed prior to fitting the second model. The second model that was fitted revealed that S1_CarRel50Spd yielded a sig. value of 0.63 (larger than 0.05) and the largest Sig.value of all three variables utilized. Additionally, S1_CarRel50Spd also yielded a partial eta squared value of 0.05 indicating that in the model that was fitted, not only was its effect not significant, it was estimated that the variable would explain only 5% of the variability of S2_Well. S1_CarRel50Spd was removed prior to fitting the third model which saw all variables possessing sig. values less than 0.05. The Section 2 specific model results are included below. 126 Table 6-27. Group B Data â Section 2 Specific Model Development Descriptive Statistics Dependent Variable: S2_Well CyclistTypology Mean Std. Deviation N 1.00 6.3142 2.29916 39 2.00 7.9546 2.22775 33 Total 7.0660 2.39655 72 Table 6-28. Group B Data â Section 2 Specific Model Development Levene's Test of Equality of Error Variances Dependent Variable: S2_Well F df1 df2 Sig. .968 1 70 .329 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. Design: Intercept + S2_Car80PerLength + CyclistTypology Table 6-29. Group B Data â Section 2 Specific Model Development Tests of Between-Subjects Effects Dependent Variable: S2_Well Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Corrected Model 76.731a 2 38.366 7.996 .001 .188 Intercept 101.767 1 101.767 21.211 .000 .235 S2_Car80PerLength 28.630 1 28.630 5.967 .017 .080 CyclistTypology 44.932 1 44.932 9.365 .003 .120 Error 331.055 69 4.798 Total 4002.643 72 Corrected Total 407.787 71 a. R Squared = .188 (Adjusted R Squared = .165) 127 Table 6-30. Group B Data â Section 2 Specific Model Development Parameter Estimates Dependent Variable: S2_Well Parameter B Std. Error t Sig. 95% Confidence Interval Partial Eta Squared Lower Bound Upper Bound Intercept 15.933 3.288 4.845 .000 9.373 22.493 .254 S2_Car80PerLength -1.683 .689 -2.443 .017 -3.057 -.308 .080 [CyclistTypology=1.00] -1.587 .519 -3.060 .003 -2.621 -.552 .120 [CyclistTypology=2.00] 0a . . . . . . a. This parameter is set to zero because it is redundant. S2_Car80PerLength describes the 80th percentile vehicle length for test ride and appears to be the only motor vehicle related variable that had a measurable significant effect on the S2_Well. This does not mean that passing speed or proximity is not important as both has been proven to be a factor for cyclists in other studies. It is likely that in this study, either participants were consistent in their perceptions towards speed and proximity, and/or the variation in the encountered motor vehicle passing speed and proximity was too small to have a measurable effect on the variation of the S2_Well. It can be seen from the parameter estimates for S2_ Car80PerLength that larger vehicles lead to lower S2_Well rating. It is interesting that in Section 1, S1_ Car80PerLength was not a significant variable, and this may be explained by the wider 2.1 m bicycle lane. 6.4.3 Section 1 to Section 4 - Repeated Measures GLM (Group C Data) The ultimate goal of this entire project is to accurately measure the desirability of bicycle infrastructure by developing a comfort and safety perception prediction model. In the process it was discovered that comfort and safety can be combined into an overall variable called âwellnessâ which is represented by the variable label âWellâ. When developing the BCSPMs, the goal is to predict a cyclistâs comfort and safety perception of a particular route based on data that can be collected about the cyclist and the route without them having to ride the route itself. Therefore it is data regarding the route (ie. Traffic volumes, slope, lane widths etc.) and preliminary survey data (demographics) that is used to predict participant perception. 128 In analyzing the data collected that the physical infrastructure and the cyclistsâ typology are the two most important factors when predicting a cyclistâs perceived wellness on a piece of infrastructure. Additionally, we have been able to discover the most important demographical questions that determine cyclist typology. While, we do not have control over a personâs typology it is re-assuring that we have control over infrastructure which is one of the most crucial factors for predicting a cyclists perceived wellness. The first variable utilized is CyclistTypology, which has been seen to be significant in all previous cases and in screening. The other independent variable is the separate comfort and safety ratings provided by each participant for Section 1 through Section 4. These separate reports serve as a variable that describes the path type. For instance, Section 1 represents the 2.1 m bike lane, Section 2 represents the 1.5 m bike lane, Section 3 represents the off road gravel path, and Section 4 represent sharing the road. Additionally, Section 1 and Section 2 represents cycling infrastructure located adjacent to a collector road, Section 3 represents a separated bicycle path and Section 4 represents sharing the road on a local street. Since each participant reported their perceptions regarding Section 1 through Section 4, the different reported perceptions themselves can be treated repeated measures similar in the way studies may measure the same variable twice before and after a certain treatment or after a certain time has passed. The section specific variables S2_Car80PerLength is not utilized in this model for simplicity. The Section 2 specific model can simply be used replace the Section 2 model in the repeated measures GLM if desired. Repeated measures general linear model is used to construct the model as each participant rode all four sections, the four dependent variables of S1_Well, S2_ Well, S3_ Well and S4_ Well make up the within-subject factors. CyclistTypology serves as a between subject factor, as each participant is assigned to one of two cyclist typologies. Table 6-31 outlines the mean wellness broken down by combinations of both the Section and CyclistTypology factors. 129 Table 6-31. Group C Data - Descriptive Statistics of Section and CyclistTypology Section CyclistTypology Mean Std. Deviation N S1_Well Level 1 7.4786 1.95179 39 Level 2 8.3725 1.59576 32 Total 7.8815 1.84291 71 S2_Well Level 1 6.1753 2.21275 39 Level 2 7.5261 2.38291 32 Total 6.7841 2.37293 71 S3_Well Level 1 8.4829 1.45441 39 Level 2 8.9323 1.25648 32 Total 8.6855 1.37775 71 S4_Well Level 1 9.4017 0.78717 39 Level 2 9.5053 0.84292 32 Total 9.4484 0.80853 71 6.4.3.1 Within Subject Effects â Section (Bicycle Infrastructure Type) One of the assumptions that needs to be tested for repeated measures ANOVA is compound symmetry. This means that variances and covariances are equal between the different repeated measures. The Mauchlyâs test of Sphericity is indirectly testing the assumption by testing that the differences in the differences in variances are equal. Table 6-32 displays the results of Mauchly's Test of Sphericity. Table 6-32. Group C Data - Mauchly's Test of Sphericitya Measure: MEASURE_1 Within Subjects Effect Mauchly's W Approx. Chi- Square df Sig. Epsilonb Greenhouse- Geisser Huynh- Feldt Lower- bound Section .532 42.743 5 .000 .695 .727 .333 Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is proportional to an identity matrix. a. Design: Intercept + CyclistTypology Within Subjects Design: Section b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests of Within-Subjects Effects table. 130 The sig value listed in Table 6-32 is less than 0.001. This means that the null hypothesis of the Mauchly's Test of Sphericitya is rejected. As the Mauchly's Test of Sphericitya is rejected we can we can look to results of multivariate tests as an alternative. However, we can also utilize the Epsilon correction factors in Table 6-32 for further analysis. In practice, Mauchlyâs test is usually rejected in repeated measures, because in most repeated measures experiments, variances and covariances do change from one measurement to the next. Using the correction factors some conclusions are made in the main ANOVA results in Table 6-33. Result can be read either from the Greenhouse-Geisser, Huynh-Feldt or Lower- bound. Greenhouse-Geisser is the most commonly used while Lower bound is likely most robust. However, in this case all three and even the sphericity assumed option provide the same results. The sig. values for the Section factor show values less than 0.001 with partial eta squared values of 42.1%. Eta is a measure of association when an independent nominal variable is used with a scale dependent variable. Eta values range from 0 which is no association to 1 which is perfect association. Eta square values will tell us how much variation is explained in the dependent variable by variation in the independent variable. In Table 6-33 we have partial Eta square since we have other independent variables present. The effect size of the Section is at 0.421 which means that 42.1% of the variation in Well can be attributed to the factor Section. The interaction of Section and CyclistTypology shows significant with sig values less than 0.05 only with Hyunh-Feldt and with an effect size of 4.1%. This indicates that different levels of cyclists likely behaved statistically similar in different sections. Not enough evidence is present to suggest a significant difference between cyclist types when different sections are present. 131 Table 6-33. Group C Data â Tests of Within-Subjects Effects Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Section Sphericity Assumed 261.558 3 87.186 50.123 .000 .421 Greenhouse- Geisser 261.558 2.085 125.451 50.123 .000 .421 Huynh-Feldt 261.558 2.182 119.853 50.123 .000 .421 Lower-bound 261.558 1.000 261.558 50.123 .000 .421 Section * CyclistTypology Sphericity Assumed 15.460 3 5.153 2.963 .033 .041 Greenhouse- Geisser 15.460 2.085 7.415 2.963 .053 .041 Huynh-Feldt 15.460 2.182 7.084 2.963 .050 .041 Lower-bound 15.460 1.000 15.460 2.963 .090 .041 Error(Section) Sphericity Assumed 360.066 207 1.739 Greenhouse- Geisser 360.066 143.861 2.503 Huynh-Feldt 360.066 150.581 2.391 Lower-bound 360.066 69.000 5.218 Table 6-34 outlines the within-subject contrasts. This table is a trend analysis which compares a linear fit, with a quadratic fit and a cubic fit for the relationship between dependent and independent variables. Both the sig values and partial Eta squared values clearly show that a linear relationship between Well and Section and also linear relationship between Well and the Section and CyclistTypology interaction are the most proper relationship. 132 Table 6-34. Group C Data â Tests of Within-Subjects Contrasts Source Section Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Section Linear 145.829 1 145.829 65.801 .000 .488 Quadratic 58.270 1 58.270 53.409 .000 .436 Cubic 57.460 1 57.460 30.066 .000 .303 Section * CyclistTypology Linear 9.410 1 9.410 4.246 .043 .058 Quadratic 2.832 1 2.832 2.596 .112 .036 Cubic 3.219 1 3.219 1.684 .199 .024 Error(Section) Linear 152.918 69 2.216 Quadratic 75.279 69 1.091 Cubic 131.869 69 1.911 6.4.3.2 Between Subject Effects â CyclistTypology Part of the repeated measures ANOVA is to also assess the between subject effects which in this case is the CyclistTypology. Table 6-35 outlines the Levene's Test of Equality of Error Variances, an assumption of ANOVA that should be met before assessing the results. Table 6-35. Group C Data â Levene's Test of Equality of Error Variancesa F df1 df2 Sig. S1_Well 0.337 1 69 0.563 S2_Well 0.026 1 69 0.872 S3_Well 0.582 1 69 0.448 S4_Well 0.305 1 69 0.583 Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a. Design: Intercept + CyclistTypology Within Subjects Design: Section In Table 6-35 we see that the all Sections sig. value are greater than 0.05 indicating that null hypothesis is not rejected and that error variance across CyclistTypology is equal. With the Leveneâs test passed the ANOVA results in Table 6-36 can be assessed and reveals with a sig. value of 0.018 which is below 0.05 that cyclist typology is a significant affect with an effect size of 7.9%. These results are no surprise as they have been examined in detail in Chapter 6.3.2 above. 133 Table 6-36. Group C Data â Tests of Between-Subjects Effects â CyclistTypology Transformed Variable: Average Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Intercept 19069.19 1 19069.19 3284.402 0 0.979 CyclistTypology 34.392 1 34.392 5.924 0.018 0.079 Error 400.613 69 5.806 6.4.3.3 Parameter Estimates of Repeated Measures GLM Table 6-37 outlines the parameter estimates of the repeated measures GLM. Sig. values of CyclistTypology is below 0.05 in when predicting S1_Well and S2_Well, but is above 0.05 when predicting S3_Well and S4_Well, indicating that it is a significant variable in Section 1 and Section 2 only. This suggests that there is agreement in comfort and safety perception between the two cyclist types when riding on more quiet local streets or off road trails and that difficult riding condition such as the bike lane of a busy roadway is what separates the opinions of the two levels of cyclist. 134 Table 6-37. Group C Data â BCSPM Prediction Model Parameter estimates Dependent Variable Parameter B Std. Error t Sig. 95% Confidence Interval Partial Eta Squared Lower Bound Upper Bound S1_Well Intercept 8.372 .318 26.304 .000 7.737 9.007 .909 [CyclistTypology=1] -.894 .429 -2.081 .041 -1.751 -.037 .059 [CyclistTypology=2] 0a . . . . . . S2_Well Intercept 7.526 .405 18.585 .000 6.718 8.334 .833 [CyclistTypology=1] - 1.351 .546 -2.472 .016 -2.441 -.261 .081 [CyclistTypology=2] 0a . . . . . . S3_Well Intercept 8.932 .242 36.908 .000 8.450 9.415 .952 [CyclistTypology=1] -.449 .327 -1.376 .173 -1.101 .202 .027 [CyclistTypology=2] 0a . . . . . . S4_Well Intercept 9.505 .144 66.163 .000 9.219 9.792 .984 [CyclistTypology=1] -.104 .194 -.534 .595 -.490 .283 .004 [CyclistTypology=2] 0a . . . . . . a. This parameter is set to zero because it is redundant. Within Table 6-37, CylistTypology level 1 decreases the predicted Wellness variable in all cases, indicating that level 1 cyclists has a lower feeling of wellness when cycling as a baseline. The differences between the different sections are characterized by the intercept values of each individual model. It can be seen that Section 2 has the lowest score, followed by Section 1, Section 3 and Section 4 in increasing order of perceived wellness. The intercept comparison between Section 1 and Section 2 indicates the importance of a wider bike lane, while the difference between Section 3 and Section 4 indicates the importance of pavement over a gravel path. Figure 6-5 shows the estimated marginal means of all Sections with both cyclist typology and 95% confidence intervals for the mean. Substantial overlap of confidence intervals is visible for the two cyclist types in Section 3 and 4, explaining the earlier sig. values being greater than 0.05 in those sections for CyclistTypology within Table 6-37. 135 Figure 6-5. Group C Data - Estimated Means of Wellness for all Sections for both Cyclist Types 6.4.4 Utilizing the Prediction Model Based on the parameter estimates in Chapter 6.4.3.3, the following equations are derived for predicting cyclist perceived wellness depending on the cyclist typology determined as described in in Chapter 6.2.4. Table 6-38. Group C Data â BCSPM Equations Road Section ðððððððððððððððððð ðððððððððððððððððð ðððððððððððððððð (0 to 10) â Higher is better Section 1 ððð¶ð¶ð·ð·ððððð·ð·ð·ð·ð·ð·ðð ððð·ð·ð¶ð¶ð·ð·ð·ð·ððððð·ð·ðð ððð·ð·ð¶ð¶ð¶ð¶ððð·ð·ð·ð·ð·ð· = 8.372 â 0.894 (ðððð ð¶ð¶ð·ð·ððð·ð·ð¶ð¶ 1) Section 2 ððð¶ð¶ð·ð·ððððð·ð·ð·ð·ð·ð·ðð ððð·ð·ð¶ð¶ð·ð·ð·ð·ððððð·ð·ðð ððð·ð·ð¶ð¶ð¶ð¶ððð·ð·ð·ð·ð·ð· = 7.526 â 1.351(ðððð ð¶ð¶ð·ð·ððð·ð·ð¶ð¶ 1) Section 3 ððð¶ð¶ð·ð·ððððð·ð·ð·ð·ð·ð·ðð ððð·ð·ð¶ð¶ð·ð·ð·ð·ððððð·ð·ðð ððð·ð·ð¶ð¶ð¶ð¶ððð·ð·ð·ð·ð·ð· = 8.932 Section 4 ððð¶ð¶ð·ð·ððððð·ð·ð·ð·ð·ð·ðð ððð·ð·ð¶ð¶ð·ð·ð·ð·ððððð·ð·ðð ððð·ð·ð¶ð¶ð¶ð¶ððð·ð·ð·ð·ð·ð· = 9.505 136 The results of the BCSPM in this thesis account for only a portion of all possible scenarios and where the results apply in context to the rest of all possible scenarios is summarized in Table 6-39. Table 6-39 provides the predicted perceived wellness of a cyclist on a given piece of cycling infrastructure. It takes the models developed, and organizes the results into a simple reference table. Table 6-39. Group C Data â Current Desire Prediction Model Table (More data â future work is needed to fill gaps) Perceived Wellness Infrastructure Type Surface Bike Lane Cyclist Typology Width Children Level 0 Level 1 Level 2 Separated Path Gravel N/A Future work Future work 8.4829 8.9323 Paved N/A Future work Future work Future work Future work Local Gravel 0.0 m Future work Future work Future work Future work Paved 0.0 m Future work Future work 9.4017 9.5053 1.5 m* Future work Future work Future work Future work Collector Paved 1.5 m Future work Future work Future work Future work 2.1 m** Future work Future work Future work Future work Arterial Paved 1.5 m Future work Future work 6.1753 7.5261 2.1 m** Future work Future work 7.4786 8.3725 *may not be a practical option **Recommended for increased comfort and safety Table 6-39 outlines what a full prediction model table would look like. The predictions that were fitted in this research are shown. âFuture workâ indicates missing predictions due to limitations in the data of this study. The model table breaks down predictions based on CyclistTypology (Level 0, Level 1 and Level 2). Level 0 was added to represent the next level down for cyclists in the âinterested but concernedâ category. As described earlier, we expect that Level 1 and Level 2 cyclists constitute âenthused and confidentâ and âstrong and fearlessâ respectively as they would be the ones willing to volunteer for this study. It is crucial that those that are interested but concerned about cycling be accounted for in a BCSPM such as this, if the goal is promote cycling. A new 137 cyclist typology designation for children was also added. Many previous models have acknowledged that their models and assessments did not apply and are not suitable for children. Children will have greater needs than adults and can feel unsafe or uncomfortable in situations where an adult feels fine. It is crucial that children are accounted for in a BCSPM such as this, if the goal is promote cycling. The model addresses the road type as the first factor on which the cyclist is cycling. Section 1 and Section 2 were both on arterial roads, while Section 4 was on a local road. Section 3 constitutes a separated path. Highways are not included in the table as it was felt that having a wellness rating for this type of road is not appropriate as it is simply too risking to have cyclists riding on bike lanes beside highways. A bike lane next to a highway would likely be avoided even by âstrong and fearlessâ cyclists if they had alternatives. The safety of road users are paramount. Currently, it is unlikely that bike lanes even exist on highways. Often, a paved shoulder is present, and is sometimes used by cyclists; however these paved shoulders on highways are not official cycle lanes. Finally, even the safety risk of collecting data for highways put such data out of reach. Besides, there would be no need to collect such data if our design philosophy forbids us to install bike lanes on highways. The next factor is the riding surface. Section 1, Section 2 and Section 4 were all paved surfaces. Section 3 was a gravel surface. A gravel bike lane on the side of the road simply cannot exist because it cannot be painted so those options have not been included. Finally, the last factor is bike lane width. It is clear from this study that 2.1 m bike lanes are more preferable to 1.5 m bike lanes. However, many 1.5 m bike lanes exist and as we have data for both 1.5 m and 2.1 m bike lanes we have allowed for these two options in the model table. A 0.0 m bike lane indicates a situation where cyclist share the road with motor vehicles. Separated paths have been treated as a completely independent infrastructure type, regardless of the adjacent road type. This would be the necessary option to allow cyclists to reach areas only accessible by highways. The reasoning is that being a separated path, the physical barrier should eliminate all possibility of interaction between the vehicles in the adjacent street and the cycle lane. This reasoning seems sound; however, more data collection would be required to verify this as the only separated path that was tested in this experiment was 138 Section 3 which is completely removed from the road. While Safety and Comfort are expected to remain equal on a separated path regardless of adjacent road type, there is a good chance that there will be some differences in enjoyment. We know that the volume of ambient traffic noise affects cyclists. Examples of separated pathways on a local road versus a collector road can be seen in Figure 6-6 and Figure 6-7, a path like Cawston Avenue (local street) and Abbott Street (Local Street) in Kelowna BC, versus Figure 6-9 and Figure 6-8 which are Hornby Street (collector) and parts of 10th Ave W (collector) in Vancouver BC. One assumption that is made in Table 6-39 is that bike lanes at a minimum should be provided on all collector roads and arterials. There is no 0.0 m bike lane width option for a bike lane width on collectors or arterials, indicating that all collector and arterial roads should have bike lanes at a minimum. It was also noted that of the two options the 2.1 m bike lane is recommended. We also assume that having no bike lanes on local roads are acceptable due to slower vehicle speed and lower vehicle volume on local roads. An example of this is Figure 6-10, on parts of 10th Ave W in Vancouver BC. As shown in Figure 6-10 traffic calming techniques restricted thru traffic access to allow only cyclists and local traffic on parts of 10th Ave W in Vancouver BC. Ensuring low traffic volume and speed in local roads should be sufficient for most cyclists to cycle without a bike lane on the local roads. However, it should be noted that even in such a situation, as shown in Figure 6-10, a child may still feel unsafe, although a bike lane in Figure 6-10 may seem excessive for adults, for children it may be necessary to encourage cycling. The ideal situation is to have separated bicycle paths along collectors and arterials which allow cyclists the same access as vehicular arterials and collectors, while on local roads; cyclists may share the road directly with the vehicles or with bike lanes if those roads are busier. 139 Figure 6-6. Example of Separated Paved Path Way âCawston Avenue (Local Street), Kelowna BC â Google Street View Figure 6-7. Example of Separated Paved Path Way âHornby Street (Collector), Vancouver BC â Google Street View 140 Figure 6-8. Example of Separated Paved Path Way â Abbott Street (Local), Kelowna BC â Google Street View Figure 6-9. Example of Separated Paved Path Way â 10th Avenue West (Collector), Vancouver BC â Google Street View 141 Figure 6-10. Example of On-Street Paved Bike Way and Traffic Calming â 10th Avenue West (Local), Vancouver BC â Google Street View 6.5 The Importance of Bicycle Lane Width (Group A and B Data) During the study, Section 1 went through road resurfacing which also involved the widening of the bike lane from 1.5 m to 2.1 m in width. Additionally, the bike lane was painted with double lines to emphasize its presence to vehicles. This turn of events gave the opportunity to take a look at Section 1 in an unplanned before and after study to assess the effects of changing bike lane width. Table 6-40 and Figure 6-11 outline the mean reported wellness ratings for Section 1 at different stages of bike lane widening and pavement resurfacing across both cyclist typologies. It should be noted that for both the original state of Section 1 and the newly paved with marker lines state there is a very small sample size of just 10 and 9 respectively, leading to a much larger 95% confidence intervals in the mean as shown in Figure 6-11. Due to the small sample size within the first two states of Section 1, no meaningful conclusions can be drawn from this dataset regarding the effects of bicycle lane width. In order to assess bicycle lane width, a comparison between the data collected for the completed Section 1 and Section 2 is carried out. 142 Table 6-40. Group B Data - Descriptive Statistics of S1_Well with respect to the state of section 1(Sec1State) and CyclistTypology Dependent Variable: S1_Well Sec1State Cyclist Typology Mean Std. Deviation N Old 1.5 m Bike Lane (Single Lines) 1.00 5.9027 2.93928 6 2.00 7.5000 2.45308 4 Total 6.5416 2.73604 10 Newly Paved 2.1 m Bike Lanes (marker lines) 1.00 7.3610 1.96897 3 2.00 9.0972 1.63346 6 Total 8.5184 1.84131 9 Newly Paved 2.1 m Bike Lane (Double Lines) 1.00 7.2639 2.14603 30 2.00 8.6051 1.46295 23 Total 7.8459 1.98097 53 Total 1.00 7.0620 2.25986 39 2.00 8.5606 1.62741 33 Total 7.7488 2.11953 72 143 Figure 6-11. Group B Data - Mean Predicted Perceived Wellness (S1_Well) according to Cyclist Typology and state of Section 1 To compare the effect of bike lane width between the completed Section 1 and Section 2 paired sample T-tests were conducted using Group A data. Section 1 features 1.5 m bike lane and is adjacent to residential single family homes and intersecting driveways while Section 1 has 2.1 m bike lanes and is adjacent to a regional park. Table 6-41 and Table 6-42 show the details of the Correlation and paired sample t-tests for a variety of variables across Section 1 and Section 2. Green highlights in the last column indicate pairs where the null hypothesis of equal means has been rejected at an alpha value of 0.05 or better, in other words, mean pairs that were statistically different from each other. Green highlights in the third column indicate the more desirable of the two values. Finally a green highlight in the seventh column indicate a value significant correlation between the paired variables. What we observe is that Section1 which has the 2.1 m bike lane out performs Section 2 in the wellness variable âWellâ and âLaneâ and hence shows that a 2.1 m bicycle lane is measurably more desirable to cyclists of typologies on average. 144 Of particular interest are the five measures of lateral passing distance (pair 12, 13, 14, 15, and 16) which all show a significant improvement with a wider bike lane except for the pair 13. Pair 13 reveals that the maximum lateral passing distance of a motor vehicle was smaller with the wider bike lane. This possible is due to some motorists giving extra room when cyclists had a narrower bike lane. Of particular interest is the minimum passing distances as people will often remember the best and worst experiences but not the average. This phenomenon is seen in a larger scale as well as the importance of low stress bicycling connectivity was emphasized for a given cycling network (Furth et al., 2016). Simultaneously, Section 1 is observed to have faster traffic speed in three of the four measures that proved to be significant (pair 4, 5, and 9) and higher traffic volume (pair 11), both of which are undesirable attributes. Therefore, it is reasonable that we could attribute overall more desirable performance of Section 1 over section 2 to the presence of a 2.1 m bike lane in Section 1, which is ensuring an increase in the lateral passing distance overall. 145 Table 6-41. Group A Data â Pair 1 to Pair 10 Sample Mean T-Tests and Correlation of Car Passing Speed, Car Length, Car Passing Distance, and Car Volume comparing a 2.1 m bike lane Section 1 with a 1.5 m bike lane Section 2. Paired Samples Statistics Pearson Correlation Paired Sample T- Test Variables Mean N Std. Deviation Correlation Sig. t Sig. (2- tailed) Pair 1 S1_CarRelMaxSpd 38.6509 53 5.93603 0.274 0.047 -2.490 0.016 S2_CarRelMaxSpd 41.717 53 8.53641 Pair 2 S1_CarRelAvgSpd 27.9949 53 2.92472 0.150 0.283 1.420 0.161 S2_CarRelAvgSpd 27.2516 53 2.92044 Pair 3 S1_CarRelMinSpd 16.4717 53 5.86746 -0.059 0.675 0.840 0.405 S2_CarRelMinSpd 15.5755 53 4.74989 Pair 4 S1_CarRel80Spd 32.5943 53 2.93955 0.261 0.059 1.838 0.072 S2_CarRel80Spd 31.6132 53 3.42463 Pair 5 S1_CarRel50Spd 28.2689 53 3.30131 0.334 0.014 2.587 0.013 S2_CarRel50Spd 26.9104 53 3.32281 Pair 6 S1_CarActAvgSpd 51.6408 53 3.30856 0.078 0.578 0.512 0.611 S2_CarActAvgSpd 51.3721 53 2.18342 Pair 7 S1_CarActMaxSpd 62.6004 53 4.2004 0.192 0.169 -1.327 0.190 S2_CarActMaxSpd 63.9149 53 6.72397 Pair 8 S1_CarActMinSpd 37.1306 53 8.73476 0.138 0.325 1.286 0.204 S2_CarActMinSpd 35.0735 53 8.99768 Pair 9 S1_CarAct80Spd 57.177 53 2.85525 0.114 0.418 3.543 0.001 S2_CarAct80Spd 55.5312 53 2.16464 Pair 10 S1_CarAct50Spd 52.3344 53 3.30647 0.174 0.214 1.401 0.167 S2_CarAct50Spd 51.6231 53 2.32465 Green highlights in the last column indicate pairs where the null hypothesis of equal means has been rejected at an alpha value of 0.05 or better, in other words, mean pairs that were statistically different from each other. Green highlights in the third column indicate the more desirable of the two values. Finally a green highlight in the seventh column indicate a value significant correlation between the paired variables. 146 Table 6-42. Group A Data â Pair 11 to Pair 17 Sample Mean T-Tests and Correlation of Car Passing Speed, Car Length, Car Passing Distance, and Car Volume comparing a 2.1 m bike lane Section 1 with a 1.5 m bike lane Section 2. Paired Samples Statistics Pearson Correlation Paired Sample T- Test Variables Mean N Std. Deviation Correlation Sig. t Sig. (2- tailed) Pair 11 S1_CarVol 230.197 53 95.69263 0.474 0.000 5.365 0.000 S2_CarVol 167.871 53 52.53838 Pair 12 S1_CarAvgPassDist 2.1559 53 0.20272 0.110 0.431 4.011 0.000 S2_CarAvgPassDist 2.0295 53 0.13212 Pair 13 S1_CarMaxPassDist 2.8255 53 0.36369 0.091 0.516 -4.589 0.000 S2_CarMaxPassDist 3.216 53 0.53581 Pair 14 S1_CarMinPassDist 1.6991 53 0.26917 -0.010 0.943 6.773 0.000 S2_CarMinPassDist 1.3972 53 0.17851 Pair 15 S1_Car80PerPassDist 2.3613 53 0.21596 0.031 0.823 2.504 0.015 S2_Car80PerPassDist 2.2677 53 0.17242 Pair 16 S1_Car50PerPassDist 2.1245 53 0.21171 0.240 0.083 5.413 0.000 S2_Car50PerPassDist 1.9533 53 0.15481 Pair 17 S1_Well 7.8145 53 1.9863 0.817 0.000 4.812 0.000 S2_Well 6.9026 53 2.39287 Pair 18 S1_Lane 8.0817 53 2.32037 0.599 0.000 5.379 0.000 S2_Lane 6.1949 53 3.14152 Green highlights in the last column indicate pairs where the null hypothesis of equal means has been rejected at an alpha value of 0.05 or better, in other words, mean pairs that were statistically different from each other. Green highlights in the third column indicate the more desirable of the two values. Finally a green highlight in the seventh column indicate a value significant correlation between the paired variables. In conclusion, wider bike lanes as shown in Table 6-41 and Table 6-42 can significantly reduce overtaking lateral distance. Bike lane widths can be a way to ensure the minimum mandated/advised overtaking lateral distances set in many countries between 0.9 m to 1.8 m (Road Safety Research and Driver Education, 2018) are met. Table 6-43 and Table 6-44 outline the distribution of minimum passing distances in within the Group A dataset. In Group A, of the 53 Section 1 rides only 6 instances (11.3%) of minimum vehicle overtaking lateral distances are below 1.5 m while in the 53 rides in Section 2, 32 out of 53 rides (60.4%) recorded minimum overtaking instances at below 1.5 m. 1.5 m is overall the most commonly agreed upon minimum 147 passing distance mandated/advised for different countries including New Zealand, France, Chile and Austria (Road Safety Research and Driver Education, 2018). In Canada, the City of Calgary, and Montreal have both introduced minimum passing distance laws in September 2019 and May 2018 enforced with fines in both cities (upwards of $200 in Calgary). Minimum passing distance was set at 1.0 m for vehicles driving 50 km/hr or less and 1.5 m for vehicles travelling faster in Montreal. In Calgary, the same distances are enforced but with a faster threshold speed of 60 km/h for vehicles (City of Calgary, 2019; Le Gouvernement du Quebec, 2019). Table 6-43. Group A Data â S1_CarMinPassDist Distribution Frequency Percent Valid Percent Cumulative Percent Valid 1.15 1 1.9 1.9 1.9 1.30 1 1.9 1.9 3.8 1.35 2 3.8 3.8 7.5 1.40 1 1.9 1.9 9.4 1.45 1 1.9 1.9 11.3 1.50 3 5.7 5.7 17.0 1.55 7 13.2 13.2 30.2 1.60 9 17.0 17.0 47.2 1.65 6 11.3 11.3 58.5 1.70 3 5.7 5.7 64.2 1.75 2 3.8 3.8 67.9 1.80 1 1.9 1.9 69.8 1.85 5 9.4 9.4 79.2 1.90 3 5.7 5.7 84.9 1.95 1 1.9 1.9 86.8 2.00 5 9.4 9.4 96.2 2.15 1 1.9 1.9 98.1 2.95 1 1.9 1.9 100.0 Total 53 100.0 100.0 148 Table 6-44. Group A Data â S2_CarMinPassDist Distribution Frequency Percent Valid Percent Cumulative Percent Valid 1.00 1 1.9 1.9 1.9 1.10 3 5.7 5.7 7.5 1.20 11 20.8 20.8 28.3 1.30 1 1.9 1.9 30.2 1.35 1 1.9 1.9 32.1 1.40 14 26.4 26.4 58.5 1.45 1 1.9 1.9 60.4 1.50 14 26.4 26.4 86.8 1.60 2 3.8 3.8 90.6 1.70 3 5.7 5.7 96.2 1.75 1 1.9 1.9 98.1 1.80 1 1.9 1.9 100.0 Total 53 100.0 100.0 Although not as desirable as a separating physical barrier, a wider bike lane can be a more cost effective way to offset the discouragement to walking and cycling that high traffic volumes and speed are known to cause as stated by Jacobsen et al., 2009. Even if vehicle lane widths are sacrificed where possible for wider bike lanes this has the potential to help reduce vehicle speeds (Charlton et al., 2010) and thus further encourage cycling (Furth et al., 2016; Jacobsen et al., 2009). The wider bike lanes significantly improved the desirability of the built environment as shown in Table 6-41 where the perceived wellness for cyclists on Section 1 with the wider bike lanes was 7.8145 and Section 2 with 1.5 m bike lanes was 6.9026 (across both cyclist types) despite Section 1 having higher traffic volumes and speeds. Both Section 1 and Section 2 bike lanes were adjacent to two-lane collector roads. Wider bike lanes allow cyclists more control of how close they want to be to traffic or the curb as it gives them more leeway to manoeuvre within their own lane. When over taking cyclists vehicles will sometimes change into the left lane if left side traffic is not present or cross the center dashed line if left side travel lane in the same direction is available (Feng et al., 2018). 149 Wider bike lanes may reduce instances of lane changes and crossing of center line for vehicles. This may also facilitate not just bicycle safety but driver safety as well. One difference between Section 1and Section 2 that cannot be fully reconciled is the driveway presence; there is drive way presence in Section 2 but not section 1. This could be a contributing reason to the lower perceived wellness of cyclists riding on Section 2. However, this should not detract from the obvious improvement in minimum passing distance that was obtained by Section 1 with a 2.1 m bike lane. The effect of driveways will need additional research and testing to fully quantify its effects. 150 7 Conclusions In this chapter, key findings are discussed in Chapter 7.1 and limitations and future work is discussed in Chapter 7.2. Conclusions discussed in the analysis and discussion sections are reiterated here for completeness. 7.1 Key Findings 1. Cyclist typology (and therefore perception and desirability) can be predicted from asking just three simple questions. The best questions for separating cyclists into different typologies for our purposes of assessing their perceived wellness when cycling was simply their response to three questions in the surveys. I. Opinion 1 - How important is low number of cars II. Opinion 2 - How important is low speed of cars III. Opinion 6 - How important is the presence of a bicycle path Using opinions to sort participants into rider typologies was found to be the most effective because their opinions are the most direct and accurate way to gauge their beliefs and thus perceptions. Data such as riding frequency is not as useful because riding frequency may not coincide with tolerance to adverse road conditions. Additionally, some participants who are not an experienced cyclist may have a natural higher tolerance for danger and feel safer with vehicles. Additionally, opinion questions take into account participants who may not have a lot of experience but have knowledge, and it is thus their knowledge that increases their tolerance to increased exposure to passing motor vehicles. For instance, rear end collisions of a passing car hitting a cyclist are rare, and someone with this knowledge may be less afraid than those without this knowledge, regardless of their riding experience. Gender and age, which is usually a good factor for determining perceptions or behaviour in many behavioural studies proved to be unnecessary in this study. Earlier preliminary analysis were conducted with variables of age and gender 151 included with the three opinion questions and it was found that these combinations yielded clusters or groups that did not yield significant differences in their ratings of reported safety. The clusters that involved age and gender was less able to group cyclists into typologies that show a distinction in reported safety. It can be assumed the same would arise for comfort, and enjoyment, since all of these metrics have been shown to be so highly correlated. 2. Table 6-18 revealed that there were observed differences in how the cyclist typologies responded in section 1 and section 2 where there is exposure to motor vehicles (higher stress environment) , but not in section 3 or section 4 (lower stress environment) 3. Traffic Safety and Safety are measuring different things, but are very similar. Cyclists do not usually think about traffic consciously as a separate safety issue until you bring it to their attention and then seem to over-report its effect. 4. Some cyclists do not think about driveways consciously until asked. However, driveways can be a significant concern for cyclist safety and proper site lines should be ensured in road design. We may assume that unconsciously the perception of traffic and driveways are taken into consideration automatically when participants report general safety. However with driveways this needs to be verified in future experiments. 5. Cyclists must feel safe to feel comfortable and cyclists must feel both safe and comfortable to feel enjoyment. Traffic safety affected enjoyment the most. 6. Safety and comfort can be combined into a single variable called wellness. A cyclistâs perceived wellness measures the desirability of a route if other factors such as travel time and logistics (distance, cost of travel, luggage needs, practicality etc.) are equal. 152 7. Bike lane width and/or the double line bike lane painted buffer had greater effect on safety, comfort and enjoyment than vehicle volumes and vehicle speed. 8. A cyclistâs mindset controls how they feel to a large extent. Expectations play a significant role â we can see this from the CyclistTypology. 9. The infrastructure type and CyclistTypology have an overwhelming effect on a cyclistâs perceived wellness and thus the desirability of cycling infrastructure. 7.2 Limitations and Future Work 1. Further study on children and youth would be crucial, as it has been shown that habits and lifestyles created at childhood have significant momentum to carry forward into adulthood. Participants in this study were limited to age 19 or older as such these models do not apply to youth or children. The inapplicability of studies on adults to children has been mentioned by earlier researchers such as Harkey et al. (1998). Understanding children and youth perceptions are crucial if our intention is to promote cycling. There does not seem to be a lot of research geared towards children. If cycling networks were designed with children in mind, then in 10 short years a significant switch to active transportation could be observed from the younger generation. 2. It can be seen from the parameter estimates for S2_ Car80PerLength that larger vehicles lead to lower S2_Well rating. It is interesting that in Section 1, S1_ Car80PerLength was not a significant variable, and this may be explained by the wider 2.1 m bicycle lane. A wider bicycle lane forces minimum separation and could reduce the effects vehicular size. 3. S2_Car80PerLength describes the 80th percentile vehicle length for test ride and appears to be the only motor vehicle related variable that had a measurable significant effect on the S2_Well. This does not mean that passing speed or proximity is not important. Research by Llorca et al. (2014) has confirmed the 153 significance that passing motor vehicle speed and proximity has on cyclist perceptions of safety. 4. Recruiting less experienced cyclists into the study would benefit future studies. These would be cyclists that constitute the âinterested but concernedâ category according to Geller (2009), otherwise called level 0 in this study. This would be required to complete the model (Table 6-39) for the entire cyclist population. Additionally, in order to have a cyclist typology assignment process that applies to the entire population, we would need to create cluster centers for all types of cyclists and that would require participants of all typologies to be involved in the study. Further study with a wider range of cyclist typologies is required. 5. Future work would benefit from more data focused on paved separated path ways with adjacent collector roads and local roads in order to assess whether adjacent roads had an effect at all on separated paths or if the separation removed the effect of vehicular traffic all together. This would also be required to complete the model proposed in this study (Table 6-39). 6. No turn manoeuvres or intersections were considered in this study. Future studies would benefit from a focus on how turns and intersections affects perception. 7. More research is required to determine how much of subjective perception reflects real objective safety. This is research on subjective perceptions of safety and comfort. Perceived safety and real safety are two different things. They are related, as safety increases, perceived safety is likely going to increase. However, in some cases perceived safety gives a false sense of security. A prime example of this is cycling on pedestrian sidewalks; it feels safer but can often be more dangerous in reality. The pilot study (A. Lee et al., 2014) found that some participants took notice of the risk of adjacent driveways and some participants did not. The same observation occurred in this study. This illustrates a clear difference in the perceived safety of different participants riding on the same route which would have the same objectively safety. A clear distinction has to be 154 made. In order to directly study objective safety utilizing crash data will be necessary as carried out by researchers such as Allen-Munley et al. (2004) and Petritsch et al. (2006). 8. Insights on the effect of weather are limited in this study (winter weather and rain would be useful to determine which cities can have large ridership) as most conditions were dry and sunny or dry and cloudy. 9. Interaction with slopes was limited in this study. Grades were relatively flat in this study. To test grades we need varying grades while keeping other factors constant. Naturalistic studies by Elizabeth Sall (Hood et al., 2011) addresses this. 10. Limited pedestrian interactions are in this study. A more in depth study is warranted between cyclist and pedestrian interactions. Participants reported not liking pedestrians sharing the path in Section 3, but quantifying the pedestrian effects on comfort and safety would be a good study to determine the utility and value of having shared pathways. In this study we simply did not have the pedestrian density or variation in density to quantify the effect of pedestrians. 11. Limited cyclist interactions with other cyclists. In similar fashion to the above statement, we did not have the density or variation in density of other cyclists to be able quantify the effects that other cyclists might have on the desirability of a given piece of infrastructure. 12. Effects of obstructions and debris were not explored in this study. 13. The space beyond the bike lane was not addressed directly in this study. Bike lanes in Section 1 were adjacent to a borrow ditch, open space, or a forest and within Section 2 bike lanes were adjacent to a sidewalk. Further research is required to determine how these spaces to the right of the bicycle lane affect comfort and safety. 155 14. Further research is required to determine how the lane width or number of vehicle lanes itself affects cyclist perception. Additionally, it would be interesting to see how many motorists change lanes when there is the option to do so on with the presence of both a 1.5 m and 2.1 m bicycle lane. 15. Further research is required to determine how curves in the route not addressed. The routes in this study were straight midblock sections. 16. Parking on the side of the street is not addressed in this studyâ Different kinds of parking should also be addressed such as parallel versus angle parking. Parked vehicles beside cyclist have major effect on their comfort and safety as reported from other studies. 17. Route choice studies using GPS equipped bicycle share programs such as the one conducted by Hood et al. (2011) were reviewed in the literature review. More future studies of this sort are valuable. They yield the most data and have the least bias, and in many ways are easier to implement. They can be used to assess other factors affecting mode choice such is route directness, trip purpose, time, and distance. These factors cannot be tested in a study design such as this one but they can be in a naturalistic GPS study using bicycle share programs or even with volunteers who agree to having a GPS unit installed on their bicycle for a period of the study. 18. Further research is required to determine how route choice is affected by habit and prior knowledge. This is a good follow-up study to the route choice GPS studies. Often commuters take the same routes due to habit, GPS instruction or ignorance of alternatives, rather than any real decision making. This is in fact one main limitation of the route choice GPS based naturalistic studies. 19. Further research is required to determine whether relative vehicle overtaking speed or actual vehicle overtaking speed is the important variable to consider. A 156 cyclist travelling quickly will perceive passing vehicles as a slower than a cyclist traveling slowly. 20. Experienced cyclists or simply faster cyclists may encounter less traffic overall in this experiment due to the fact that less cars pass the laser sensor. The faster the cyclist travels, the less perceived traffic volume the cyclist will perceive. More research is required to determine whether there is significance in this effect. 21. A faster cyclists will experience less exposure time to traffic because they spend less time riding the route. More research is needed to determine how much exposure time affects safety and safety. 22. In this study we analyze the percentiles, maximums, minimums and means of vehicle passing distance, speed and size. Further research into how each individual passing interaction affects a cyclist will yield more conclusions. 23. Further studies on traffic noise, vehicle type and vehicle size would be beneficial. More data will help develop an understanding of how the length, height, size and noise of a passing vehicle affects and influences a cyclist. 24. The position of the cyclist in their own lane was not a variable controlled for in this study. Controlling this variable will allow better testing of vehicle behaviours when overtaking the cyclistsâ such as in the studies conducted by Llorca et al. (2014) and Walker (2007). 25. Questionnaire needs to be redesigned in future studies to remove any source of ambiguity and be more specific. A lot of questions were removed from the study after collecting the data as it was discovered that participants had alternative interpretations of the questions. 26. This research collected data based on one bicycle that all participants rode. The bicycle could not be adjusted to fit all participants perfectly. If participants rode 157 their own bicycle, results may be different. This can be accounted for in future studies. 27. This research collected data on cyclists mainly from Kelowna, BC, Canada. If the study is conducted in a different city or country, results may be different. Perceptions are cultural as well as contextual. This can be accounted for in future studies. 28. Definitions of both Safety and Comfort may need to be re-defined more specifically to minimize any overlap between interpretations of comfort and safety. Currently, the definitions provided to the participants in the most up to date survey reads as follows: a. Safety- Your feeling of safety is defined as the feeling of vulnerability and risk of injury while cycling a specific section of the bike route â (i.e. how exposed to danger do you feel?) b. Comfort- Your feeling of comfort is defined as the physical and mental exertion you experienced while cycling a specific section of the bike route. (i.e. how exposed to nuisances were you?) The definitions may be re-defined as follows: a. Safety- in order to gauge your perceived level of safety, assess the level of risk, vulnerability and/or danger that you feel you are inâ (i.e. how exposed to danger were you?) b. Comfort- To gauge your perceived level of comfort, assess the level of discomfort or displeasure that you felt. (i.e. how exposed to discomfort were you?) 158 Bibliography Abraham, B., & Ledolter, J. (2006). Introduction to Regression Modelling. In Brooks/Cole, Cengage Learning. Brooks/Cole, Cengage Learning. Allen-Munley, C., Daniel, J., & Dhar, S. (2004). Logistic Model for Rating Urban Bicycle Route Safety. Transportation Research Record, 1878(1), 107â115. https://doi.org/10.3141/1878- 13 Ayres, T. J. (2014). Bicycle Promotion as a Response to Climate Change. Ergonomics in Design: The Quarterly of Human Factors Applications, 22(2), 30â32. https://doi.org/10.1177/1064804614526203 Bergström, A., & Magnusson, R. (2003). Potential of transferring car trips to bicycle during winter. Transportation Research Part A: Policy and Practice, 37(8), 649â666. https://doi.org/10.1016/S0965-8564(03)00012-0 Bil, M., Andrášik, R., & KubeÄek, J. (2015). How comfortable are your cycling Tracks? A new method for objective cycling comfort measurement. Transportation Research Part C, Manuscript Draft. Blanca, M. J., Alarcón, R., Bendayan, R., Arnau, J., & Bono, R. (2017). Non-normal data: Is ANOVA still a valid option? Psicothema, 29(4), 552â557. https://doi.org/10.7334/psicothema2016.383 Boriboonsomsin, B., & Barth, M. (2008). Real-World CO 2 Impacts of Traffic Congestion. TRB 2008 Annual Meeting, January 2008, 1â18. Charlton, S. G., Mackie, H. W., Baas, P. H., Hay, K., Menezes, M., & Dixon, C. (2010). Using endemic road features to create self-explaining roads and reduce vehicle speeds. Accident Analysis and Prevention, 42(6), 1989â1998. https://doi.org/10.1016/j.aap.2010.06.006 City of Calgary. (2007). Calgary Downtown Commuter Cyclist Survey Report. City of Calgary. (2019). Cycling information for drivers. https://www.calgary.ca/Transportation/TP/Pages/Cycling/Cycling-Education-and- Safety/cycling-information-for-drivers.aspx Daley, M., & Rissel, C. (2011). Perspectives and images of cycling as a barrier or facilitator of cycling. Transport Policy, 18(1), 211â216. https://doi.org/10.1016/j.tranpol.2010.08.004 Damant-Sirois, G., Grimsrud, M., & El-Geneidy, A. M. (2014). Whatâs your type: a multidimensional cyclist typology. Transportation, April, 1153â1169. 159 https://doi.org/10.1007/s11116-014-9523-8 Dawes, J. (2018). Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5-Point, 7-Point and 10-Point Scales. International Journal of Market Research, 50(1), 61â104. https://doi.org/10.1177/147078530805000106 De Vos, J., & Witlox, F. (2013). Transportation policy as spatial planning tool; reducing urban sprawl by increasing travel costs and clustering infrastructure and public transportation. Journal of Transport Geography, 33, 117â125. https://doi.org/10.1016/j.jtrangeo.2013.09.014 Delucchi, M., & Kurani, K. S. (2014). How to Have Sustainable Transportation without Making People Drive Less or Give Up Suburban Living. Journal of Urban Planning and Development, 140(4), 04014008. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000172 Dill, J., & McNeil, N. (2013). Four types of cyclists? Transportation Research Record, 2387, 129â138. https://doi.org/10.3141/2387-15 Dozza, M, & Piccinini, G. F. B. (2014). Do cyclists on e â bikes behave differently than cyclists on traditional. November. Dozza, Marco, & Fernandez, A. (2013). Understanding Bicycle Dynamics and Cyclist Behavior From Naturalistic Field Data. IEEE Transactions on Intelligent Transportation Systems, 1â 9. https://doi.org/10.1109/TITS.2013.2279687 Dozza, Marco, & Fernandez, A. (2014). Understanding bicycle dynamics and cyclist behavior from naturalistic field data (November 2012). IEEE Transactions on Intelligent Transportation Systems, 15, 376â384. https://doi.org/10.1109/TITS.2013.2279687 Dunn, J., Creatore, M., Peterson, E., Weyman, J., & Glazier, R. (2009). Final Report Peel Healthy Development Index. Feleke, R., Scholes, S., Wardlaw, M., & Mindell, J. S. (2018). Comparative fatality risk for different travel modes by age, sex, and deprivation. Journal of Transport and Health, 8, 307â320. https://doi.org/10.1016/j.jth.2017.08.007 Feng, F., Bao, S., Hampshire, R. C., & Delp, M. (2018). Drivers overtaking bicyclists â An examination using naturalistic driving data. Accident Analysis and Prevention, 115(January), 98â109. https://doi.org/10.1016/j.aap.2018.03.010 Furth, P. G., Mekuria, M. C., & Nixon, H. (2016). Network Connectivity for Low-Stress Bicycling. 2587, 41â49. https://doi.org/10.3141/2587-06 Garder, P. (1994). Bicycle Accidents in Maine: an Analysis. Transportation Research Record, 160 1438, 34â41. 00668956 Gatersleben, B., & Haddad, H. (2010). Who is the typical bicyclist? Transportation Research Part F: Traffic Psychology and Behaviour, 13(1), 41â48. https://doi.org/10.1016/j.trf.2009.10.003 Geller, R. (2009). Four Types of Cyclists. Portland Office of Transportation. https://www.portlandoregon.gov/transportation/44597?a=237507 Gignac, G. (2019). Bivariate Correlation. In How2statsbook (Online Edition 1) (1st ed., p. C3.1- C3.41). Gignac, G. E. (2019a). Difference Between Two-Means. In How2statsbook (Online Edition 1) (p. C6.1-C6.59). Gignac, G. E. (2019b). One-Way Between-Subjects ANOVA. In How2statsbook (Online Edition 1) (pp. C7-1-C7-46). Grammenos, F., Craig, B., Pollard, D., & Guerrera, C. (2008). Hippodamus Rides to Radburn: A New Model for the 21st Century. Journal of Urban Design, 13(2), 163â176. https://doi.org/10.1080/13574800801965643 Hall, P. (2002). Urban and Regional Planning (4th ed.). Routledge. Handy, S., Cao, X., & Mokhtarian, P. (2005). Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transportation Research Part D: Transport and Environment, 10(6), 427â444. https://doi.org/10.1016/j.trd.2005.05.002 Harkey, D. L., Reinfurt, D. W., Knuiman, M., Stewart, J. R., & Sorton, A. (1998). Development of the Bicycle Compatibility Index: A Level of Service Concept (Issue December 1998). https://doi.org/No. FHWA-RD-98-072 He, Q., Fan, X., & Ma, D. (2005). Full Bicycle Dynamic Model for Interactive Bicycle Simulator. Journal of Computing and Information Science in Engineering, 5(4), 373. https://doi.org/10.1115/1.2121749 Hood, J., Sall, E., & Charlton, B. (2011). A GPS-based bicycle route choice model for San Francisco, California. Transportation Letters: The International Journal of Transportation Research, 3, 63â75. https://doi.org/10.3328/TL.2011.03.01.63-75 International Energy Agency. (2012). CO2 Emissions form Fuel Combustion. Jacobsen, P. L., Racioppi, F., & Rutter, H. (2009). Who owns the roadsâ¯? How motorised traffic discourages walking and bicycling. 369â373. https://doi.org/10.1136/ip.2009.022566 161 Jensen, M. (1999). Passion and heart in transport - a sociological analysis on transport behaviour. Transport Policy, 6(1), 19â33. https://doi.org/10.1016/S0967-070X(98)00029-8 Johan de Hartog, J., Boogaard, H., Nijland, H., & Hoek, G. (2010). Do the health benefits of cycling outweigh the risks? Environmental Health Perspectives, 118(8), 1109â1116. https://doi.org/10.1289/ehp.0901747 Joo, S., & Oh, C. (2013). A novel method to monitor bicycling environments. Transportation Research Part A: Policy and Practice, 54, 1â13. https://doi.org/10.1016/j.tra.2013.07.001 Kahn Ribeiro, S., S. K., Beuthe, M., Gasca, J., Greene, D., Lee, D. S., Muromachi, Y., Newton, P. J., Plotkin, S., Sperling, D., Wit, R., & Zhou, P. J. (2007). Transport and its infrastructure. Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (Eds)], 334â380. Krenn, P. J., Oja, P., & Titze, S. (2014). Route choices of transport bicyclists: a comparison of actually used and shortest routes. The International Journal of Behavioral Nutrition and Physical Activity, 11(1), 31. https://doi.org/10.1186/1479-5868-11-31 Kutzbach, M. J. (2009). Motorization in developing countries: Causes, consequences, and effectiveness of policy options. Journal of Urban Economics, 65(2), 154â166. https://doi.org/10.1016/j.jue.2008.10.002 Kwon, D., Yang, G., Lee, C., Shin, J., Park, Y., Jung, B., Lee, D. Y., Lee, K., Han, S., Yoo, B., Wohn, K., & Ahn, J. (2001). KAIST interactive bicycle simulator. Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), 3, 2313â2318. https://doi.org/10.1109/ROBOT.2001.932967 Le Gouvernement du Quebec. (2019). New Highway Safety Code Measures. https://www.transports.gouv.qc.ca/en/code-securite-routiere/Pages/csr.aspx Leden, L., Gårder, P., & Pulkkinen, U. (2000). An expert judgment model applied to estimating the safety effect of a bicycle facility. Accident; Analysis and Prevention, 32(4), 589â599. Lee, A., Dias, L., Mohanty, S., Carvalho, T., Commandeur, J., & Lovegrove, G. (2014). Using Instrumented Probe Bicycles to Develop Bicycle Safety and Comfort Prediction Models. International Cycling Safety Conference 2014, November, 1â20. Lee, C. (2004). Physical Activity and Environment Research in the Health Field: Implications for Urban and Transportation Planning Practice and Research. Journal of Planning Literature, 19(2), 147â181. https://doi.org/10.1177/0885412204267680 162 Llorca, C., Ferrer, V., & Garcia, A. (2014). Motor vehicles overtaking cyclists on two-lane rural roadsâ¯: analysis on speed and lateral clearance. November, 1â17. Lyngby, K., & Tel, D. (2009). Economic Evaluation of Cycle Projects - Methodology and Unit Prices (Issue December). Mohanty, S., Lee, A., Carvalho, T., Dias, L., & Lovegrove, G. (2014). A Global Review of Current Instrumented Probe Bicycle ( IPB ) Technology and Research. International Cycling Safety Conference 2014, November, 1â17. Moore, S. C., Patel, A. V., Matthewsm, C. E., A. Berrington de Conzales, W. P., Katki, H. A., Linet, M. S., Weiderpass, E., Visvanathan, K., Helzlsouer, K. J., Thun, M., Gapstur, S. M., Hartge, P., & Lee, I. (2012). Leisure Time Physical Activity of Moderate to Vigorous Intensity and Mortality: A Large Pooled Cohort Analysis. PLOS Medicine, 9(11), 1â14. Nechyba, T. J., & Walsh, R. P. (2004). Urban Sprawl. Journal of Economic Perspectives, 18(Fall), 177â200. OâConnor, B. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicerâs MAP test. Behavior Research Methods, Instruments, and Computers, 32(3), 396â402. https://doi.org/10.3758/bf03200807 OECD/International Transport Forum. (2013). Cycling, Health and Safety. http://dx.doi.org/10.1787/9789282105955-en Oja, P., Titze, S., Bauman, A., de Geus, B., Krenn, P., Reger-Nash, B., & Kohlberger, T. (2011). Health benefits of cycling: A systematic review. Scandinavian Journal of Medicine and Science in Sports, 21(4), 496â509. https://doi.org/10.1111/j.1600-0838.2011.01299.x Petritsch, T., Landis, B., Huang, H., & Challa, S. (2006). Sidepath Safety Model: Bicycle Sidepath Design Factors Affecting Crash Rates. Transportation Research Record, 1982(1), 194â201. Pirlone, F., & Candia, S. (2015). Cycle Sustainability. TeMA Journal of Land Use, Mobility and Environment, 8(1), 83â101. http://dx.doi.org/10.6092/1970-9870/2921 Pucher, J., & Buehler, R. (2010). Walking and Cycling for Healthy Cities. Built Environment, 36(4), 391â414. https://doi.org/10.2148/benv.36.4.391 Road Safety Research and Driver Education. (2018). Examining the International Research Evidence in relation to Minimum Passing Distances for Cyclists . A pre-legislative scrutiny. Sælensminde, K. (2004). Costâbenefit analyses of walking and cycling track networks taking into account insecurity, health effects and external costs of motorized traffic. 163 Transportation Research Part A: Policy and Practice, 38(8), 593â606. https://doi.org/10.1016/j.tra.2004.04.003 Schrank, D., & Lomax, T. (2003). The 2003 annual urban mobility report. In Texas Transportation Institute (Issue September). Sener, I. N., Eluru, N., & Bhat, C. R. (2009). An analysis of bicycle route choice preferences in Texas, US. Transportation, 36, 511â539. https://doi.org/10.1007/s11116-009-9201-4 Statistics Canada. (2016). Focus on Geography Series, 2016 Census - Kelowna. https://www12.statcan.gc.ca/census-recensement/2016/as-sa/fogs-spg/Facts-cma- eng.cfm?GC=915&GK=CMA&LANG=Eng Statistics Canada. (2017a). Canadian Motor Vehicle Traffic Collision Statistics 2017. https://www.tc.gc.ca/eng/motorvehiclesafety/canadian-motor-vehicle-traffic-collision- statistics-2017.html Statistics Canada. (2017b). Leading causes of death, total population, by age group. Table 13- 10-0394-01. https://doi.org/10.25318/1310039401-eng Statistics Canada. (2017c). Percentage who reported cycling in past 12 months, by sex and selected characteristics, household population aged 12 or older, Canada, 2013/2014. https://www150.statcan.gc.ca/n1/pub/82-003-x/2017004/article/14788/tbl/tbl01-eng.htm Turner, S. M., Shafer, C. S., & Stewart, W. P. (1997). Bicycle suitability criteria for state roadways in Texas (Issue June 1997). Twisk, D. a M., Boele, M. J., Vlakveld, W. P., Christoph, M., Sikkema, R. K., & Remij, R. (2013). Preliminary results from a field experiment on e-bike safetyâ¯: speed choice and mental workload for middle-aged and elderly cyclists. November, 1â15. United States Environmental Protection Agency. (2006). Greenhouse Gas Emissions from the U . S . Transportation Sector (Issue March). Vlakveld, W. P., Twisk, D., Christoph, M., Boele, M., Sikkema, R., Remy, R., & Schwab, A. L. (2015). Speed choice and mental workload of elderly cyclists on e-bikes in simple and complex traffic situations: A field experiment. Accident Analysis & Prevention, 74, 97â106. https://doi.org/10.1016/j.aap.2014.10.018 Walker, I. (2007). Drivers overtaking bicyclists: objective data on the effects of riding position, helmet use, vehicle type and apparent gender. Accident; Analysis and Prevention, 39(2), 417â425. https://doi.org/10.1016/j.aap.2006.08.010 Werneke, J., & Dozza, M. (2014). Integrating Road Safety Data for Single-Bicycle Crash 164 Causation. International Research Council on Biometrics of Injury 2014, 46(0), 442â443. https://doi.org/IRC-14-52 Winter, J. C. F. De, & Dodou, D. (2012). Five-Point Likert Itemsâ¯: t test versus Mann-Whitney- Wilcoxon. 15(11). Winters, M., Davidson, G., Kao, D., & Teschke, K. (2011). Motivators and deterrents of bicycling: Comparing influences on decisions to ride. Transportation, 38(1), 153â168. https://doi.org/10.1007/s11116-010-9284-y Winters, M., Teschke, K., Grant, M., Setton, E. M., & Brauer, M. (2010). How Far Out of the Way Will We Travel? Transportation Research Record: Journal of the Transportation Research Board, 2190(1), 1â10. https://doi.org/10.3141/2190-01 World Health Organization. (2013). Global Status Report on Road Safety 2013: Supporting a Decade of Action. World Health Organization. (2018a). Global status report on road safety 2018: summary. In (WHO/NMH/NVI/18.20). Licence: CC BY-NC-SA 3.0 IGO. https://apps.who.int/iris/bitstream/handle/10665/277370/WHO-NMH-NVI-18.20- eng.pdf?ua=1 World Health Organization. (2018b). Top 10 Causes of Deaths. https://www.who.int/news- room/fact-sheets/detail/the-top-10-causes-of-death Yamanaka, H., Xiaodong, P., & Sanada, J. (2013). Evaluation Models for Cyclists â Perception Using Probe Bicycle System. Proceedings of the Eastern Asia Society for Transportation Studies, 9. Yao, M., Fu, Q., Gao, L., & Li, Z. (2011). Exploring influencing factors to the riding comfort of bicyclists on physically separated bicycle roadways in China using proportional odds model. International Conference of Chinese Transportation Professionals & American Society of Civil Engineers, 718â727. Zhang, Y., Liu, F., Trkov, M., & Yi, J. (2012). Rider / Bicycle Pose Estimation with IMU / Seat Force Measurements. The 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 1, 604â609. 165 Appendices Appendix A â Summary of 105 trial runs and final treatment of data Run # A B C D E F G AUG036 AUG035 AUG034 1 AUG033 AUG032 1 AUG031 1 AUG030 1 AUG029 AUG028 1 AUG027 AUG026 1 1 AUG025 1 1 AUG024 1 1 AUG023 AUG022 1 AUG021 1 1 AUG020 1 AUG019 1 AUG018 1 AUG017 AUG016 1 AUG015 1 AUG014 1 AUG013 1 AUG012 1 AUG011 AUG010 1 AUG009 1 AUG008 1 166 Run # A B C D E F G AUG007 1 AUG006 1 AUG005 1 AUG004 1 AUG003 1 AUG002 1 AUG001 1 1 JUN050 1 JUN049 1 Jun048 1 Jun047 1 Jun046 1 Jun045 1 Jun044 1 Jun043 1 Jun042 1 Jun041 1 JUN040 1 JUN039 1 JUN038 1 JUN037 JUN036 1 JUN035 1 JUN034 1 JUN033 1 JUN032 JUN031 1 JUN030 1 JUN029 1 JUN028 1 JUN027 1 Jun026 1 167 Run # A B C D E F G Jun025 1 Jun024 1 Jun023 Jun022 1 Jun021 1 Jun020 1 Jun019 Jun018 1 Jun017 1 Jun016 1 Jun015 1 Jun014 1 Jun013 Jun012 1 Jun011 Jun010 1 1 Jun009 1 Jun008 1 Jun007 1 Jun006 Jun005 1 Jun004 1 Jun003 1 Jun002 1 Jun001 1 A019 1 1 A018 1 A017 1 A016 1 A015 A014 A013 168 Run # A B C D E F G A012 1 A011 1 A010 A009 A008 1 A007 A006 1 A005 1 A004 1 A003 1 A002 1 A001 1 Total 5 3 57 22 1 2 1 169 Appendix B â Variable Definitions # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 1 Vest no Experimenter Recorded Vest Nominal 1=yes 2=no na 2 Weight no Survey Weight Scale na kg 3 Height no Survey Height Scale na m 4 BMI no Survey BMI Scale na BMI 5 Age no Survey Age Scale na Years 6 Gender no Survey Gender Nominal 1=male 2=female na 7 SmrBikeFre no Survey How often do you bike in the Summer Ordinal 1=daily 2=a few days per week 3=a few days per month 4=a few days per year na 8 WinBikeFre no Survey How often do you bike in the Winter Ordinal 1=daily 2=a few days per week 3=a few days per month 4=a few days per year na 9 RecPerc no Survey What Percentage of you trips is Recreational Scale Percentage Percent 10 WorkPerc no Survey What Percentage of you trips is for work, school or errands Scale Percentage Percent 11 Income no Survey Household Income Ordinal 1=less than 20000 2=between 20000 and 40000 3=between 40000 and 60000 5=above 100000 na 12 ExerHr no Survey Hours of Weekly Exercise Ordinal 1=less than 2.5 hours 2= between 2.5 and 5.0 hours na 170 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 3=between 5.0 and 7.5 hours 4=between 7.5 and 10 hours 5=more than 10 hours 13 Percept1 no Survey I don't cycle when its too hot or humid Ordinal 1=strongly disagree 2=disagree 3=neither 4=agree 5=strongly agree na 14 Percept2 no Survey I don't cycle when its too cold Ordinal 1=strongly disagree 2=disagree 3=neither 4=agree 5=strongly agree na 15 Percept3 no Survey I don't cycle when There is snow, because of the additional effort Ordinal 1=strongly disagree 2=disagree 3=neither 4=agree 5=strongly agree na 16 Percept4 no Survey I donât cycle when There is ice or snow because of the danger of slipping Ordinal 1=strongly disagree 2=disagree 3=neither 4=agree 5=strongly agree na 17 Percept5 no Survey I donât cycle when Its Raining Ordinal 1=strongly disagree 2=disagree 3=neither 4=agree 5=strongly agree na 18 Percept6 no Survey I donât cycle when I have to carry bags or heavy loads Ordinal 1=strongly disagree 2=disagree na 171 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 3=neither 4=agree 5=strongly agree 19 Percept7 no Survey I donât cycle when The route I take is too steep (uphill climb) Ordinal 1=strongly disagree 2=disagree 3=neither 4=agree 5=strongly agree na 20 Opinion1 no Survey How important are the following factors in making a good bicycle route? Low number of cars driving Ordinal 1=very unimportant 2=unimportant 3=neither 4=important 5=very important na 21 Opinion2 no Survey How important are the following factors in making a good bicycle route? Low speed of cars Ordinal 1=very unimportant 2=unimportant 3=neither 4=important 5=very important na 22 Opinion3 no Survey How important are the following factors in making a good bicycle route? Low number of parked cars Ordinal 1=very unimportant 2=unimportant 3=neither 4=important 5=very important na 23 Opinion4 no Survey How important are the following factors in making a good bicycle route? Continuous bicycle route Ordinal 1=very unimportant 2=unimportant 3=neither 4=important 5=very important na 24 Opinion5 no Survey How important are the following factors in making a good bicycle route? Presence of bicycle path with Ordinal 1=very unimportant 2=unimportant 3=neither 4=important na 172 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units a physical barrier 5=very important 25 Opinion6 no Survey How important are the following factors in making a good bicycle route? Bicycle specific signage Ordinal 1=very unimportant 2=unimportant 3=neither 4=important 5=very important na 26 Opinion7 no Survey How important are the following factors in making a good bicycle route? Flat route Ordinal 1=very unimportant 2=unimportant 3=neither 4=important 5=very important na 27 ParEncor1 no Survey To what extent your parent(s) or guardian(s) encourage you to cycle? As a way to reach destinations? Ordinal 1=actively discouraged 2=somewhat discouraged 3=neither 4=somewhat encouraged 5=actively encouraged na 28 ParEncor2 no Survey To what extent your parent(s) or guardian(s) encourage you to cycle? As a sport or recreational activity Ordinal 1=actively discouraged 2=somewhat discouraged 3=neither 4=somewhat encouraged 5=actively encouraged na 29 ChildStart no Survey Did you start cycling as a child? Nominal 1=yes 2=no na 30 ChildAccid no Survey How many cycling accidents have you been in as a child Ordinal 1=none 2=a few 3=several 4=many na 31 AdultAccid no Survey How many cycling accidents have you been in as an adult Ordinal 1=none 2=a few 3=several 4=many na 32 ChildClose no Survey How many cycling close Ordinal 1=none na 173 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units calls have you been in (on road) as a child? 2=a few 3=several 4=many 33 AdultClose no Survey How many cycling close calls have you been in (on road) as an adult? Ordinal 1=none 2=a few 3=several 4=many na 34 OrderofExp no Experimenter Recorded Greenway first Nominal 1=yes 2=no na 35 Weather no Experimenter Recorded Weather Ordinal 1=Sunny/Clear 2=Cloudy 3=Light Rain 4=Rain na 36 Sec4exists no Experimenter Recorded Section 4 Present Ordinal 1=yes 2=no na 37 S1_Comf Section 1 Survey Overall did you feel Riding this section was comfortable Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 38 S1_Surf Section 1 Survey Did you feel roughness of the riding surface? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 39 S1_Slope Section 1 Survey Did you feel discomfort due to the slope(s) of the route? Ordinal 1=Absolutely 2=yes 3=somewhat na 174 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 4=neutral 5=not really 6=no 7=not at all 40 S1_Enjoy Section 1 Survey Overall did you feel riding this section was enjoyable? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 41 S1_Weather Section 1 Survey Overall did you feel the weather was comfortable to ride in? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 42 S1_Lane Section 1 Survey Overall did you feel the bike lane width was adequate? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 43 S1_State Section 1 Experimenter Recorded State of Section 1 Ordinal 1=old 2=newly paved with marker lines 3=newly paved with lines painted na 44 S1in_Safe Section 1 Inbound Survey Overall Did you feel Riding this section was Safe Ordinal 1=Absolutely 2=yes na 175 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 3=somewhat 4=neutral 5=not really 6=no 7=not at all 45 S1in_Safetraffic Section 1 Inbound Survey Did you feel safe in regards to adjacent vehicle traffic Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 46 S1in_CarRelAvgSpd Section 1 Inbound Laser Sensor Car relative average speed Scale na kph 47 S1in_CarRelMaxSpd Section 1 Inbound Laser Sensor Car relative max speed Scale na kph 48 S1in_CarRelMinSpd Section 1 Inbound Laser Sensor Car relative min speed Scale na kph 49 S1in_CarRel80Spd Section 1 Inbound Laser Sensor Car 80th percentile relative speed Scale na kph 50 S1in_CarRel50Spd Section 1 Inbound Laser Sensor Car 50th percentile relative speed Scale na kph 51 S1in_CarActAvgSpd Section 1 Inbound Laser Sensor / DL1 Club Data Logger Car actual average speed Scale na kph 52 S1in_CarActMaxSpd Section 1 Inbound Laser Sensor / DL1 Club Data Logger Car actual max speed Scale na kph 53 S1in_CarActMinSpd Section 1 Inbound Laser Sensor / DL1 Club Data Logger Car actual min speed Scale na kph 54 S1in_CarAct80Spd Section 1 Laser Sensor / Car 80th percentile actual Scale na kph 176 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units Inbound DL1 Club Data Logger speed 55 S1in_CarAct50Spd Section 1 Inbound Laser Sensor / DL1 Club Data Logger Car 50th percentile actual speed Scale na kph 56 S1in_CarAvgLength Section 1 Inbound Laser Sensor Car average length Scale na m 57 S1in_CarMaxLength Section 1 Inbound Laser Sensor Car max length Scale na m 58 S1in_CarMinLength Section 1 Inbound Laser Sensor Car min length Scale na m 59 S1in_Car80PerLength Section 1 Inbound Laser Sensor Car 80th percentile length Scale na m 60 S1in_Car50PerLength Section 1 Inbound Laser Sensor Car 50th percentile length Scale na m 61 S1in_CarAvgPassDist Section 1 Inbound Laser Sensor Car average passing distance Scale na m 62 S1in_CarMaxPassDist Section 1 Inbound Laser Sensor Car max passing distance Scale na m 63 S1in_CarMinPassDist Section 1 Inbound Laser Sensor Car min passing distance Scale na m 64 S1in_Car80PerPassDist Section 1 Inbound Laser Sensor Car 80th percentile passing distance Scale na m 65 S1in_Car50PerPassDist Section 1 Inbound Laser Sensor Car 50th percentile passing distance Scale na m 66 S1in_BikeAvgSpd Section 1 Inbound DL1 Club Data Logger Bike average speed Scale na kph 67 S1in_BikeMaxSpd Section 1 Inbound DL1 Club Data Logger Bike max speed Scale na kph 68 S1in_BikeMinSpd Section 1 Inbound DL1 Club Data Logger Bike min speed Scale na kph 69 S1in_VertAccelVar Section 1 DL1 Club Data Vertical acceleration variance Scale na (m/s^2)^2 177 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units Inbound Logger 70 S1in_ComfortIndex Section 1 Inbound DL1 Club Data Logger / Calculated Vertical acceleration comfort index Scale na comfort index 71 S1in_LatAccelVar Section 1 Inbound DL1 Club Data Logger Lateral acceleration variance Scale na (m/s^2)^2 72 S1in_LongAccelVar Section 1 Inbound DL1 Club Data Logger Longitudinal acceleration variance Scale na (m/s^2)^2 73 S1in_SteeringAngleVar Section 1 Inbound Potentiometer Steering angle variance Scale na volt^2 74 S1in_CarVol Section 1 Inbound Laser Sensor Car volume Scale na veh/hr 75 S1in_Temp Section 1 Inbound Temperature Sensor Average temperature Scale na Celsius 76 S1out_Safe Section 1 Outbound Survey Overall Did you feel Riding this section was Safe Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 77 S1out_Safetraffic Section 1 Outbound Survey Did you feel safe in regards to adjacent vehicle traffic Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 78 S1out_CarRelAvgSpd Section 1 Outbound Laser Sensor Car relative average speed Scale na kph 79 S1out_CarRelMaxSpd Section 1 Outbound Laser Sensor Car relative max speed Scale na kph 178 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 80 S1out_CarRelMinSpd Section 1 Outbound Laser Sensor Car relative min speed Scale na kph 81 S1out_CarRel80Spd Section 1 Outbound Laser Sensor Car 80th percentile relative speed Scale na kph 82 S1out_CarRel50Spd Section 1 Outbound Laser Sensor Car 50th percentile relative speed Scale na kph 83 S1out_CarActAvgSpd Section 1 Outbound Laser Sensor / DL1 Club Data Logger Car actual average speed Scale na kph 84 S1out_CarActMaxSpd Section 1 Outbound Laser Sensor / DL1 Club Data Logger Car actual max speed Scale na kph 85 S1out_CarActMinSpd Section 1 Outbound Laser Sensor / DL1 Club Data Logger Car actual min speed Scale na kph 86 S1out_CarAct80Spd Section 1 Outbound Laser Sensor / DL1 Club Data Logger Car 80th percentile actual speed Scale na kph 87 S1out_CarAct50Spd Section 1 Outbound Laser Sensor / DL1 Club Data Logger Car 50th percentile actual speed Scale na kph 88 S1out_CarAvgLength Section 1 Outbound Laser Sensor Car average length Scale na m 89 S1out_CarMaxLength Section 1 Outbound Laser Sensor Car max length Scale na m 90 S1out_CarMinLength Section 1 Outbound Laser Sensor Car min length Scale na m 91 S1out_Car80PerLength Section 1 Outbound Laser Sensor Car 80th percentile length Scale na m 92 S1out_Car50PerLength Section 1 Outbound Laser Sensor Car 50th percentile length Scale na m 93 S1out_CarAvgPassDist Section 1 Laser Sensor Car average passing distance Scale na m 179 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units Outbound 94 S1out_CarMaxPassDist Section 1 Outbound Laser Sensor Car max passing distance Scale na m 95 S1out_CarMinPassDist Section 1 Outbound Laser Sensor Car min passing distance Scale na m 96 S1out_Car80PerPassDist Section 1 Outbound Laser Sensor Car 80th percentile passing distance Scale na m 97 S1out_Car50PerPassDist Section 1 Outbound Laser Sensor Car 50th percentile passing distance Scale na m 98 S1out_BikeAvgSpd Section 1 Outbound DL1 Club Data Logger Bike average speed Scale na kph 99 S1out_BikeMaxSpd Section 1 Outbound DL1 Club Data Logger Bike max speed Scale na kph 100 S1out_BikeMinSpd Section 1 Outbound DL1 Club Data Logger Bike min speed Scale na kph 101 S1out_VertAccelVar Section 1 Outbound DL1 Club Data Logger Vertical acceleration variance Scale na (m/s^2)^2 102 S1out_ComfortIndex Section 1 Outbound DL1 Club Data Logger Vertical acceleration comfort index Scale na comfort index 103 S1out_LatAccelVar Section 1 Outbound DL1 Club Data Logger Lateral acceleration variance Scale na (m/s^2)^2 104 S1out_LongAccelVar Section 1 Outbound DL1 Club Data Logger Longitudinal acceleration variance Scale na (m/s^2)^2 105 S1out_SteeringAngleVar Section 1 Outbound Potentiometer Steering angle variance Scale na volt^2 106 S1out_CarVol Section 1 Outbound Laser Sensor Car volume Scale na veh/hr 107 S1out_Temp Section 1 Outbound Temperature Sensor Average temperature Scale na Celsius 108 S2_Safe Section 2 Survey Overall Did you feel Riding this section was Safe Ordinal 1=Absolutely 2=yes 3=somewhat na 180 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 4=neutral 5=not really 6=no 7=not at all 109 S2_SafeTraffic Section 2 Survey Did you feel safe in regards to adjacent vehicle traffic Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 110 S2_Comf Section 2 Survey Overall did you feel Riding this section was comfortable Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 111 S2_Surf Section 2 Survey Did you feel discomfort due to the roughness of the riding surface? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 112 S2_Slope Section 2 Survey Did you feel discomfort due to the slope(s) of the route? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 181 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 113 S2_Enjoy Section 2 Survey Overall did you feel riding this section was enjoyable? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 114 S2_Weather Section 2 Survey Overall did you feel the weather was comfortable to ride in? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 115 S2_Lane Section 2 Survey Overall did you feel the bike lane width was adequate? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 116 S2_Driveways Section 2 Survey Overall did you feel the adjacent driveways were a concern? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 117 S2_CarRelAvgSpd Section 2 Laser Sensor Car relative average speed Scale na kph 118 S2_CarRelMaxSpd Section 2 Laser Sensor Car relative max speed Scale na kph 119 S2_CarRelMinSpd Section 2 Laser Sensor Car relative min speed Scale na kph 120 S2_CarRel80Spd Section 2 Laser Sensor Car 80th percentile relative Scale na kph 182 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units speed 121 S2_CarRel50Spd Section 2 Laser Sensor Car 50th percentile relative speed Scale na kph 122 S2_CarActAvgSpd Section 2 Laser Sensor / DL1 Club Data Logger Car actual average speed Scale na kph 123 S2_CarActMaxSpd Section 2 Laser Sensor / DL1 Club Data Logger Car actual max speed Scale na kph 124 S2_CarActMinSpd Section 2 Laser Sensor / DL1 Club Data Logger Car actual min speed Scale na kph 125 S2_CarAct80Spd Section 2 Laser Sensor / DL1 Club Data Logger Car 80th percentile actual speed Scale na kph 126 S2_CarAct50Spd Section 2 Laser Sensor / DL1 Club Data Logger Car 50th percentile actual speed Scale na kph 127 S2_CarAvgLength Section 2 Laser Sensor Car average length Scale na m 128 S2_CarMaxLength Section 2 Laser Sensor Car max length Scale na m 129 S2_CarMinLength Section 2 Laser Sensor Car min length Scale na m 130 S2_Car80PerLength Section 2 Laser Sensor Car 80th percentile length Scale na m 131 S2_Car50PerLength Section 2 Laser Sensor Car 50th percentile length Scale na m 132 S2_CarAvgPassDist Section 2 Laser Sensor Car average passing distance Scale na m 133 S2_CarMaxPassDist Section 2 Laser Sensor Car max passing distance Scale na m 134 S2_CarMinPassDist Section 2 Laser Sensor Car min passing distance Scale na m 135 S2_Car80PerPassDist Section 2 Laser Sensor Car 80th percentile passing distance Scale na m 136 S2_Car50PerPassDist Section 2 Laser Sensor Car 50th percentile passing distance Scale na m 137 S2_BikeAvgSpd Section 2 DL1 Club Data Bike average speed Scale na kph 183 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units Logger 138 S2_BikeMaxSpd Section 2 DL1 Club Data Logger Bike max speed Scale na kph 139 S2_BikeMinSpd Section 2 DL1 Club Data Logger Bike min speed Scale na kph 140 S2_VertAccelVar Section 2 DL1 Club Data Logger Vertical acceleration variance Scale na (m/s^2)^2 141 S2_ComfortIndex Section 2 DL1 Club Data Logger Vertical acceleration comfort index Scale na comfort index 142 S2_LatAccelVar Section 2 DL1 Club Data Logger Lateral acceleration variance Scale na (m/s^2)^2 143 S2_LongAccelVar Section 2 DL1 Club Data Logger Longitudinal acceleration variance Scale na (m/s^2)^2 144 S2_SteeringAngleVar Section 2 Potentiometer Steering angle variance Scale na volt^2 145 S2_CarVol Section 2 Laser Sensor Car volume Scale na veh/hr 146 S2_Temp Section 2 Temperature Sensor Average temperature Scale na Celsius 147 S3_Safe Section 3 Survey Overall Did you feel Riding this section was Safe Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 148 S3_Comf Section 3 Survey Overall did you feel Riding this section was comfortable Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 149 S3_Surf Section 3 Survey Did you feel discomfort due Ordinal 1=Absolutely na 184 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units to the roughness of the riding surface? 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all 150 S3_Slope Section 3 Survey Did you feel discomfort due to the slope(s) of the route? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 151 S3_SafeTraffic Section 3 Survey Did you feel safe in regards to adjacent vehicle traffic Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 152 S3_Enjoy Section 3 Survey Overall did you feel riding this section was enjoyable? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 153 S3_Weather Section 3 Survey Overall did you feel the weather was comfortable to ride in? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really na 185 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 6=no 7=not at all 154 S3_BikeAvgSpd Section 3 DL1 Club Data Logger Bike average speed Scale na kph 155 S3_BikeMaxSpd Section 3 DL1 Club Data Logger Bike max speed Scale na kph 156 S3_BikeMinSpd Section 3 DL1 Club Data Logger Bike min speed Scale na kph 157 S3_VertAccelVar Section 3 DL1 Club Data Logger Vertical acceleration variance Scale na (m/s^2)^2 158 S3_ComfortIndex Section 3 DL1 Club Data Logger Vertical acceleration comfort index Scale na comfort index 159 S3_LatAccelVar Section 3 DL1 Club Data Logger Lateral acceleration variance Scale na (m/s^2)^2 160 S3_LongAccelVar Section 3 DL1 Club Data Logger Longitudinal acceleration variance Scale na (m/s^2)^2 161 S3_SteeringAngleVar Section 3 Potentiometer Steering angle variance Scale na volt^2 162 S3_Temp Section 3 Temperature Sensor Average temperature Scale na Celsius 163 S4_Safe Section 4 Survey Overall Did you feel Riding this section was Safe Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 164 S4_Comf Section 4 Survey Overall did you feel Riding this section was comfortable Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no na 186 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 7=not at all 165 S4_Surf Section 4 Survey Did you feel discomfort due to the roughness of the riding surface? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 166 S4_Slope Section 4 Survey Did you feel discomfort due to the slope(s) of the route? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 167 S4_SafeTraffic Section 4 Survey Did you feel safe in regards to adjacent vehicle traffic Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 168 S4_Enjoy Section 4 Survey Overall did you feel riding this section was enjoyable? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 169 S4_Weather Section 4 Survey Overall did you feel the weather was comfortable to ride in? Ordinal 1=Absolutely 2=yes 3=somewhat na 187 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 4=neutral 5=not really 6=no 7=not at all 170 S4_Driveways Section 4 Survey Overall did you feel the adjacent driveways were a concern? Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 171 S4_BikeAvgSpd Section 4 DL1 Club Data Logger Bike average speed Scale na kph 172 S4_BikeMaxSpd Section 4 DL1 Club Data Logger Bike max speed Scale na kph 173 S4_BikeMinSpd Section 4 DL1 Club Data Logger Bike min speed Scale na kph 174 S4_VertAccelVar Section 4 DL1 Club Data Logger Vertical acceleration variance Scale na (m/s^2)^2 175 S4_ComfortIndex Section 4 DL1 Club Data Logger Vertical acceleration comfort index Scale na comfort index 176 S4_LatAccelVar Section 4 DL1 Club Data Logger Lateral acceleration variance Scale na (m/s^2)^2 177 S4_LongAccelVar Section 4 DL1 Club Data Logger Longitudinal acceleration variance Scale na (m/s^2)^2 178 S4_SteeringAngleVar Section 4 Potentiometer Steering angle variance Scale na volt^2 179 S4_Temp Section 4 Temperature Sensor Average temperature Scale na Celsius 180 S1_Safe Section 1 Survey Overall Did you feel Riding this section was Safe Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral na 188 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 5=not really 6=no 7=not at all 181 S1_Safetraffic Section 1 Survey Did you feel safe in regards to adjacent vehicle traffic Ordinal 1=Absolutely 2=yes 3=somewhat 4=neutral 5=not really 6=no 7=not at all na 182 S1_CarRelAvgSpd Section 1 Laser Sensor Car relative average speed Scale na kph 183 S1_CarRelMaxSpd Section 1 Laser Sensor Car relative max speed Scale na kph 184 S1_CarRelMinSpd Section 1 Laser Sensor Car relative min speed Scale na kph 185 S1_CarRel80Spd Section 1 Laser Sensor Car 80th percentile relative speed Scale na kph 186 S1_CarRel50Spd Section 1 Laser Sensor Car 50th percentile relative speed Scale na kph 187 S1_CarActAvgSpd Section 1 Laser Sensor / DL1 Club Data Logger Car actual average speed Scale na kph 188 S1_CarActMaxSpd Section 1 Laser Sensor / DL1 Club Data Logger Car actual max speed Scale na kph 189 S1_CarActMinSpd Section 1 Laser Sensor / DL1 Club Data Logger Car actual min speed Scale na kph 190 S1_CarAct80Spd Section 1 Laser Sensor / DL1 Club Data Logger Car 80th percentile actual speed Scale na kph 191 S1_CarAct50Spd Section 1 Laser Sensor / DL1 Club Data Logger Car 50th percentile actual speed Scale na kph 189 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units 192 S1_CarAvgLength Section 1 Laser Sensor Car average length Scale na m 193 S1_CarMaxLength Section 1 Laser Sensor Car max length Scale na m 194 S1_CarMinLength Section 1 Laser Sensor Car min length Scale na m 195 S1_Car80PerLength Section 1 Laser Sensor Car 80th percentile length Scale na m 196 S1_Car50PerLength Section 1 Laser Sensor Car 50th percentile length Scale na m 197 S1_CarAvgPassDist Section 1 Laser Sensor Car average passing distance Scale na m 198 S1_CarMaxPassDist Section 1 Laser Sensor Car max passing distance Scale na m 199 S1_CarMinPassDist Section 1 Laser Sensor Car min passing distance Scale na m 200 S1_Car80PerPassDist Section 1 Laser Sensor Car 80th percentile passing distance Scale na m 201 S1_Car50PerPassDist Section 1 Laser Sensor Car 50th percentile passing distance Scale na m 202 S1_BikeAvgSpd Section 1 DL1 Club Data Logger Bike average speed Scale na kph 203 S1_BikeMaxSpd Section 1 DL1 Club Data Logger Bike max speed Scale na kph 204 S1_BikeMinSpd Section 1 DL1 Club Data Logger Bike min speed Scale na kph 205 S1_VertAccelVar Section 1 DL1 Club Data Logger Vertical acceleration variance Scale na (m/s^2)^2 206 S1_ComfortIndex Section 1 DL1 Club Data Logger Vertical acceleration comfort index Scale na comfort index 207 S1_LatAccelVar Section 1 DL1 Club Data Logger Lateral acceleration variance Scale na (m/s^2)^2 208 S1_LongAccelVar Section 1 DL1 Club Data Logger Longitudinal acceleration variance Scale na (m/s^2)^2 209 S1_SteeringAngleVar Section 1 Potentiometer Steering angle variance Scale na volt^2 210 S1_CarVol Section 1 Laser Sensor Car volume Scale na veh/hr 211 S1_Temp Section 1 Temperature Sensor Average temperature Scale na Celsius 212 GenderandVests no Calculated Sorted all participants into 4 Nominal 1=MaleNoVest na 190 # Variable Section Specific Data Collected From Description/Question Asked Type Data Representation Units bins (male wearing vests, male not wearing vests, female wearing vests, female not wearing vests) 2=FemaleVest 3=MaleVest 4=FemaleNoVest 213 S1_Well Section1 Calculated Mean of S1_Safe and S1_Comf Scale na na 214 S2_Well Section 2 Calculated Mean of S2_Safe and S2_Comf Scale na na 215 S3_Well Section 3 Calculated Mean of S3_Safe and S3_Comf Scale na na 216 S4_Well Section 4 Calculated Mean of S4_Safe and S4_Comf Scale na na 217 CyclistTypology no Calculated K-Means Clustering Based on Opinion1, Opinion2 and Opinion6 1= Level 1 2=Level 2 Ordinal na na
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Sustainable transport safety : a study on predicting cyclists' perception of safety and comfort
Text