Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Orvin, Muntahith Mehadil
Master of Applied Science - MASc
University of British Columbia
This thesis develops advanced modeling techniques to investigate bicycling demand and bicyclists’ user behavior including their speed choice, destination choice, and reasons to choose dockless bikeshare service (DBS). This study utilizes data from automatic bicycle counters, GPS records, and DBS user survey from the City of Kelowna. One of the major contributions of this thesis is to adopt a machine learning (ML) algorithm to improve the trip identification procedure using the GPS data. Another key feature of this thesis is to develop latent segmentation-based (LS) models to capture heterogeneity. For example, the destination choice analysis adopts a random parameter LS logit model to address multidimensional heterogeneity by assuming a discrete (i.e. inter-segment heterogeneity), and continuous (i.e. intra-segment heterogeneity) distribution of the parameters. This thesis examines the effects of weather, built environment, traffic, land use, neighborhood, socio-demographic, accessibility, and trip attributes on bicycle usage. In case of the model results, the proposed advanced models show improved goodness-of-fit measures. For instance, speed choice model validation results suggest that ML and LS modeling approach outperforms other methods in-terms of Welch’s t-test and Mean Absolute Deviation respectively. The model results provide important behavioral insights which are expected to assist in developing effective policies to promote bicycling more. For example, the model results suggest that bike index (BI) is an important target variable to increase bicycling demand and speed. Individuals are more likely to choose DBS as cheapest option while residing in bicycle-friendly neighborhoods with good transit accessibility. The model results also confirm the existence of multidimensional heterogeneity. For instance, trip destinations closer to designated dockless bike return areas (i.e. havens) show inter-segment heterogeneity. Specifically, urban trips are more likely to be destined closer to the havens; whereas, suburban trips are less likely to be destined closer to the havens. Intra-segment heterogeneity is also confirmed while this variable is tested for BI – i.e. a higher BI indicating bicycle-friendly environment increases the probability for suburban trips to be destined closer to the havens. Finally, heterogeneity captured in this research needs to be accommodated within the policies to ensure effective usage of bicycle and newer shared services.
BICYCLISTS’ TRAVEL BEHAVIOR ANALYSIS: MODELING BICYCLING DEMAND, SPEED CHOICE, DESTINATION CHOICE, AND USAGE OF SHARED MOBILITY SERVICES by Muntahith Mehadil Orvin B.Sc., Bangladesh University of Engineering and Technology (BUET), 2014 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) April 2020 © Muntahith Mehadil Orvin, 2020 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: BICYCLISTS’ TRAVEL BEHAVIOR ANALYSIS: MODELING BICYCLING DEMAND, SPEED CHOICE, DESTINATION CHOICE, AND USAGE OF SHARED MOBILITY SERVICES submitted by Muntahith Mehadil Orvin in partial fulfillment of the requirements of the degree of Master of Applied Science (MASc) in Civil Engineering. Dr. Mahmudur Fatmi, School of Engineering Supervisor Dr. Gordon Lovegrove, School of Engineering Supervisory Committee Member Dr. Zheng Liu, School of Engineering Supervisory Committee Member Dr. Anas Chaaban, School of Engineering University Examiner iii Abstract This thesis develops advanced modeling techniques to investigate bicycling demand and bicyclists’ user behavior including their speed choice, destination choice, and reasons to choose dockless bikeshare service (DBS). This study utilizes data from automatic bicycle counters, GPS records, and DBS user survey from the City of Kelowna. One of the major contributions of this thesis is to adopt a machine learning (ML) algorithm to improve the trip identification procedure using the GPS data. Another key feature of this thesis is to develop latent segmentation-based (LS) models to capture heterogeneity. For example, the destination choice analysis adopts a random parameter LS logit model to address multidimensional heterogeneity by assuming a discrete (i.e. inter-segment heterogeneity), and continuous (i.e. intra-segment heterogeneity) distribution of the parameters. This thesis examines the effects of weather, built environment, traffic, land use, neighborhood, socio-demographic, accessibility, and trip attributes on bicycle usage. In case of the model results, the proposed advanced models show improved goodness-of-fit measures. For instance, speed choice model validation results suggest that ML and LS modeling approach outperforms other methods in-terms of Welch’s t-test and Mean Absolute Deviation respectively. The model results provide important behavioral insights which are expected to assist in developing effective policies to promote bicycling more. For example, the model results suggest that bike index (BI) is an important target variable to increase bicycling demand and speed. Individuals are more likely to choose DBS as cheapest option while residing in bicycle-friendly neighborhoods with good transit accessibility. The model results also confirm the existence of multidimensional heterogeneity. For instance, trip destinations closer to designated dockless bike return areas (i.e. havens) show inter-segment heterogeneity. Specifically, urban trips are more likely to be destined closer to the havens; whereas, suburban trips are less likely to be destined closer to the havens. Intra-segment iv heterogeneity is also confirmed while this variable is tested for BI – i.e. a higher BI indicating bicycle-friendly environment increases the probability for suburban trips to be destined closer to the havens. Finally, heterogeneity captured in this research needs to be accommodated within the policies to ensure effective usage of bicycle and newer shared services. v Lay Summary Cities around the world are making significant investments in their bicycle infrastructures, as bicycling is a promising alternative travel mode that improves traffic congestion, air quality, and overall population health. For effective bicycle planning and infrastructure investment decision-making, it is critical to understand bicycling demand and bicyclists’ behavior. This thesis develops advanced econometric methods and machine learning algorithm to investigate bicycling demand, as well as bicyclists’ travel speed, destination choice, and reason for using newer shared mobility services. This thesis examines the impact of weather, built environment and traffic, land use, accessibility, neighborhood, socio-demographic, and travel characteristics on bicycle usage. The findings of this research is expected to assist in developing plans and policies to induce more bicycle trips as well as increase the usage of bikeshare services by promoting bicycle as a sustainable alternative travel mode. vi Preface Portions of this thesis have been published as peer-reviewed journal papers, and papers in fully peer-reviewed conference proceedings. This thesis is largely derived from the following papers:  Chapter 4 from “Orvin, M., Fatmi, M., & Chowdhury, S. (2020). A Tale of Two Cities: Development of Cycling Demand Models for New Zealand and Canada. To be published in the fully peer-reviewed proceedings of the 55th Canadian Transportation Research Forum (CTRF), Montreal, Canada.” (accepted)  Chapter 5 from “Orvin, M., and Fatmi, M. (2020). Developing Speed Choice Model Using GPS Data of Dockless Bikeshare Service Users. Journal of Transport and Land Use.” (under review)  Chapter 6 from “Orvin, M., and Fatmi, M. (2020). Modeling Destination Choice Behavior of the Dockless Bikesharing Services Users: Assessing Origin-destination Inter-dependency Using Their Built Environment Attributes. Transportation Research Record (TRR): Journal of Transportation Research Board (TRB).” (under final review)  Chapter 7 from “Orvin, M., and Fatmi, M. (2020). Modeling the Reasons for Choosing Dockless Bikesharing Services?: Assessing the Effects of Transportation Infrastructure, Land Use, and Neighborhood Attributes. Published in the fully peer-reviewed proceedings of the 99th Annual Meeting of the Transportation Research Board (TRB), Washington D.C., USA.” vii Table of Contents Abstract .............................................................................................................................. iii Lay Summary ....................................................................................................................... v Preface ................................................................................................................................ vi Table of Contents .............................................................................................................. vii List of Tables ..................................................................................................................... xii List of Figures .................................................................................................................. xiii List of Abbreviations .........................................................................................................xv Acknowledgements ......................................................................................................... xvii Dedication ......................................................................................................................... xix Chapter 1: Introduction ..................................................................................................... 1 1.1 Background .......................................................................................................... 1 1.2 Objectives ............................................................................................................ 5 1.3 Significance ......................................................................................................... 5 1.4 Thesis Outline ...................................................................................................... 6 Chapter 2: Literature Review ............................................................................................ 9 2.1 Introduction .......................................................................................................... 9 2.2 Bicycle Infrastructure Planning and Understanding Bicycle User Behavior ......... 10 2.3 Shared Services................................................................................................. 12 2.3.1 Station-based Bikeshare Service (SBS) .....................................................13 2.3.2 Dockless Bikeshare Service (DBS) .............................................................15 2.4 Emergence of GPS Data .................................................................................... 16 2.5 Bicycling Demand .............................................................................................. 17 viii 2.6 Bicyclists’ Speed Choice Decision ..................................................................... 19 2.7 Destination Choice Behavior of Dockless Bikeshare Service (DBS) Users......... 21 2.8 Reasons for Choosing Dockless Bikeshare Service (DBS) ................................ 24 2.9 Research Gaps in the Existing Literature ........................................................... 26 2.10 Concluding Remarks .......................................................................................... 28 Chapter 3: Data ...............................................................................................................31 3.1 Introduction ........................................................................................................ 31 3.2 Study Area ......................................................................................................... 31 3.3 Sources of Data ................................................................................................. 32 3.3.1 Bicycle Count Data .....................................................................................32 3.3.2 Survey of Dockless Bikeshare Service (DBS) Users ...................................33 3.3.3 GPS Data ...................................................................................................35 3.3.4 Other Sources of Data ................................................................................35 3.4 Built Environment and Land Use Indices ............................................................ 36 Chapter 4: Bicycling Demand Modeling ...........................................................................39 4.1 Introduction ........................................................................................................ 39 4.2 Exploratory Analysis of Bicycle Usage Data ....................................................... 40 4.3 Methodology ...................................................................................................... 42 4.4 Independent Variables Tested in This Study ...................................................... 45 4.5 Results ............................................................................................................... 45 4.5.1 Goodness-of-fit Measures ..........................................................................46 4.5.2 Latent Segment Allocation Component Results ..........................................47 4.5.3 Parameter Estimation Results ....................................................................48 4.5.4 Predicted Average Daily Bicycling Demand ................................................50 4.6 Concluding Remarks .......................................................................................... 51 ix Chapter 5: Bicyclists’ Speed Choice Modeling .................................................................53 5.1 Introduction ........................................................................................................ 53 5.2 Exploratory Analysis of GPS Data ...................................................................... 54 5.3 Methodology ...................................................................................................... 56 5.3.1 Machine Learning Algorithm for GPS Data Cleaning ..................................56 5.3.2 Speed Choice Modeling Approach..............................................................58 5.4 Independent Variables Tested in This Study ...................................................... 59 5.5 Results ............................................................................................................... 60 5.5.1 GPS Data Processing Result ......................................................................60 5.5.1.1 Preliminary Filtering and Application of DBSCAN Algorithm ..................60 5.5.1.2 Map Matching ........................................................................................63 5.5.2 Model Results .............................................................................................64 5.5.2.1 Goodness-of-fit Measures .....................................................................64 5.5.2.2 Latent Segment Allocation Component Results .....................................66 5.5.2.3 Parameter Estimation Results ...............................................................67 5.5.2.4 Model Validation Results .......................................................................70 5.6 Concluding Remarks .......................................................................................... 71 Chapter 6: Destination Choice Modeling of Dockless Bikeshare Service (DBS) Users ....74 6.1 Introduction ........................................................................................................ 74 6.2 Exploratory Analysis of Trip Itinerary Information from GPS Records ................. 75 6.3 Data Processing................................................................................................. 75 6.4 Methodology ...................................................................................................... 76 6.4.1 Bicycle Analysis Zone and Choice Set Generation .....................................76 6.4.2 Destination Choice Modeling Approach ......................................................77 6.5 Independent Variables Tested in This Study ...................................................... 80 x 6.6 Results ............................................................................................................... 81 6.6.1 Goodness-of-fit Measures ..........................................................................81 6.6.2 Latent Segment Allocation Component Results ..........................................83 6.6.3 Parameter Estimation Results ....................................................................85 6.6.4 Model Validation Results ............................................................................88 6.7 Concluding Remarks .......................................................................................... 90 Chapter 7: Modeling the Reasons for Choosing Dockless Bikeshare Services (DBS) ......93 7.1 Introduction ........................................................................................................ 93 7.2 Exploratory Analysis of Dockless Bikeshare Service (DBS) Users’ Survey Data 94 7.3 Methodology ...................................................................................................... 99 7.4 Independent Variables Tested in This Study .................................................... 101 7.5 Results ............................................................................................................. 101 7.5.1 Goodness-of-fit Measures ........................................................................ 102 7.5.2 Latent Segment Allocation Component Results ........................................ 104 7.5.3 Parameter Estimation Results .................................................................. 104 7.5.4 Results of Elasticity Effects ....................................................................... 108 7.6 Concluding Remarks ........................................................................................ 110 Chapter 8: Conclusion ................................................................................................... 112 8.1 Summary of Contributions ................................................................................ 112 8.2 Concluding Remarks ........................................................................................ 118 8.3 Future Research .............................................................................................. 119 References .................................................................................................................... 121 Appendix A : Bicycling Demand Model Results ............................................................ 136 Appendix B : Bicyclists’ Speed Choice Model Results ................................................ 142 xi Appendix C : Destination Choice Model Results of DBS Users ................................... 146 Appendix D : Reasons to Choose DBS Model Results ................................................. 152 xii List of Tables Table 4.1. Descriptive Statistics of the Independent Variables Used in the LSNB Bicycling Demand Model ....................................................................................................................46 Table 4.2. Goodness-of-fit Measures of Bicycling Demand Models .....................................47 Table 4.3. Latent Segment Allocation Results of the LSNB Bicycling Demand Model .........47 Table 4.4. Parameter Estimation Results of the LSNB Bicycling Demand Model .................48 Table 4.5. Predicted and Observed Average Daily Bicycling Demand .................................50 Table 5.1. Descriptive Statistics of the Independent Variables Used in the LSLR Bicyclists’ Speed Choice Model ...........................................................................................................65 Table 5.2. Goodness-of-fit Measures of the Bicyclists’ Speed Choice Models .....................66 Table 5.3. Latent Segment Allocation Results of the LSLR Bicyclists’ Speed Choice Model 67 Table 5.4. Parameter Estimation Results of the LSLR Bicyclists’ Speed Choice Model .......69 Table 5.5. Predictive Performance Evaluation for Bicyclists’ Speed Choice Models ............70 Table 6.1. Descriptive Statistics of the Independent Variables Used in the RPLSL Destination Choice Model ....................................................................................................82 Table 6.2. Goodness-of-fit Measures of the Destination Choice Models ..............................83 Table 6.3. Latent Segment Allocation Results of the RPLSL Destination Choice Model ......84 Table 6.4. Parameter Estimation Results of the RPLSL Destination Choice Model .............86 Table 7.1. Descriptive Statistics of the Independent Variables Used in the Reasons to Choose DBS LSL Model ................................................................................................... 102 Table 7.2. Goodness-of-fit Measures of the Reasons to Choose DBS Models .................. 103 Table 7.3. Latent Segment Allocation Results of the Reasons to Choose DBS LSL Model103 Table 7.4. Parameter Estimation Results of the Reasons to Choose DBS LSL Model ....... 105 Table 7.5. Elasticity Effects ............................................................................................... 109 xiii List of Figures Figure 2.1 Bicycle Infrastructure Facilities (Source: City of Kelowna and CBC news) ..........10 Figure 2.2 Station-based Bikeshare Service (SBS) .............................................................14 Figure 2.3 Dockless Bikeshare Service (DBS) .....................................................................15 Figure 3.1. Bicycle Infrastructure and Count Locations in Kelowna .....................................33 Figure 3.2. Eco-counter Located at Gyro Beach Pathway ...................................................34 Figure 4.1. Distribution of Monthly Bicycling Demand ..........................................................41 Figure 4.2. Distribution of Daily Bicycling Demand for Different Seasons ............................41 Figure 4.3. Distribution of Monthly Bicycling Demand for Protected and Unprotected Facilities ..............................................................................................................................41 Figure 4.4. Distribution of Bicycling Demand with Temperature ...........................................42 Figure 4.5. Distribution of Bicycling Demand with Precipitation ...........................................42 Figure 5.1. Distribution of Speed for Spatial and Temporal Characteristics .........................54 Figure 5.2. Variation of Speed for Built Environment Attributes ...........................................55 Figure 5.3. Three Types of Points in DBSCAN Algorithm Considering n = 4 .......................57 Figure 5.4. Sample Results of DBSCAN Algorithm Implementation .....................................62 Figure 5.5. Trip Points After Applying DBSCAN Algorithm...................................................62 Figure 5.6. Trip Points Before Applying Map Matching Algorithm ........................................63 Figure 5.7. Trip Points After Applying Map Matching Algorithm ...........................................64 Figure 5.8. Predicted Speed Using the LSLR Model for Validation Sample .........................71 Figure 6.1. Predicted Average Probability of Choosing Destination BAZs for Weekday Peak Hour Trips ...........................................................................................................................89 Figure 6.2. Predicted Average Probability of Choosing Destination BAZs for Weekday Off-peak Hour Trips ...................................................................................................................89 Figure 7.1. Density of Haven Locations in Kelowna .............................................................95 xiv Figure 7.2. Density of DBS Trip Origin in Kelowna ..............................................................96 Figure 7.3. Reasons to Choose DBS ...................................................................................96 Figure 7.4. Gender Distribution for DBS Users ....................................................................97 Figure 7.5. Age Group Distribution for DBS Users ...............................................................97 Figure 7.6. Individual Income Distribution for DBS Users ....................................................98 Figure 7.7. Percentage Distribution of Access Time to Bicycles of DBS by Walking ............98 xv List of AbbreviationsAADT…………………………………………………………………. Annual Average Daily Traffic AIC ................................................................................................ Akaike Information Criteria BAZ ...................................................................................................... Bicycle Analysis Zone BI ........................................................................................................................... Bike Index BIC ............................................................................................ Bayesian Information Criteria CBD ................................................................................................. Central Business District DA ............................................................................................................ Dissemination Area DBI ........................................................................................................ Davies-Bouldin Index DBS ............................................................................................ Dockless Bikeshare Service DBSCAN .................................... Density-Based Spatial Clustering of Applications with Noise DMTI .............................................................................. Desktop Mapping Technologies Inc. EEA ..................................................................................... European Environmental Agency GHG ............................................................................................................. Greenhouse Gas GPS .............................................................................................. Global Positioning System IIA ............................................................................. Independence of Irrelevant Alternatives LOS ............................................................................................................... Level Of Service LS ............................................................................................... Latent Segmentation-based LSL .................................................................................... Latent Segmentation-based Logit LSLR ..............................................................Latent Segmentation-based Linear Regression LSNB.............................................................. Latent Segmentation-based Negative Binomial LSPR........................................................... Latent Segmentation-based Poisson Regression LUI ................................................................................................................. Land Use Index MAD ................................................................................................ Mean Absolute Deviation ML .............................................................................................................. Machine Learning xvi MNL ............................................................................................................. Multinomial Logit MSPE ..................................................................................... Mean Squared Prediction Error NB .............................................................................................................. Negative Binomial OLS .................................................................................................... Ordinary Least Square OLSR ............................................................................... Ordinary Least Square Regression PR ........................................................................................................... Poisson Regression RB ........................................................................................................................ Rule Based RCI ................................................................................................... Road-Connectivity Index RP………………………………………………………………………………..Random Parameter RPL ................................................................................................. Random Parameter Logit RPLR ......................................................................... Random Parameter Linear Regression RPLSL ................................................. Random Parameter Latent Segmentation-based Logit RUM ...................................................................................... Random Utility-based Modeling UNEP ........................................................................ United Nations Environmental Program WI .................................................................................................................. Wind-chill Index xvii Acknowledgements I express my sincere gratitude to a number of people for their continuous support, guidance, and inspiration throughout my tenure as MASc student at University of British Columbia, Okanagan. First and foremost, I would like to express my heartfelt thanks to my supervisor, Dr. Mahmudur Fatmi, for your tremendous support and encouragement. It is an honor for me to work under your insightful supervision. I am deeply grateful to you for your precious suggestions, for sharing your vast wisdom with me, for showing me the right way when I was stuck, for providing continuous motivation to publish, for boosting my confidence, and for supporting me in every way not only as a mentor but also as a friend. You set a high standard in my mind by your leadership and problem solving capability. You have encouraged me tremendously to raise my academic and personal standards to a great extent. I am also thankful to the funding I have received as a research assistant and teaching assistant which would not have been possible without you. I greatly appreciate my thesis supervisory committee members - Dr. Gordon Lovegrove and Dr. Zheng Liu for your thoughtful comments and recommendations at every stage of my research work. Without your valuable reviews, my thesis would not have reached to this standard. Special thanks to the university examiner Dr. Anas Chaaban for offering your precious time. Your suggestions helped me a lot to improve my thesis. I would like to thank the City of Kelowna for providing the data for analysis. Special thanks goes to Dr. Subeh Chowdhury for sharing her valuable ideas related to my research. I appreciate the support of Muhammad Ahasanuzzaman and Fazle Rabbi Masum. I would like to thank all the researchers of Center for Transportation and Land Use Research (CeTLUR) xviii and Sustainable Transport Safety lab for their ideas and support. I would like to voice my sincere appreciation to Abdul Masoud for helping me whenever I needed. I would like to thank my parents, parents-in-law, wife, and sister for their constant love and encouragement during the research. This work would not have been possible without the blessings of my family members. Special thanks to my spouse, Afia Siddika for always inspiring me and being a concrete support to me. Last but not least, I would like to thank the Almighty Allah for providing me the strength, knowledge, and sound health to accomplish my thesis. xix Dedication To My Father, Dr. Shahid Motaher Hossan, My Mother, Mrs. Dilrose Akhter, My Wife Afia Siddika Ivy, and My Sister Muntarin Tanta Detisha 1 Chapter 1: Introduction 1.1 Background Traffic congestions and pollutions induced by higher use of private vehicles have significant economic and health consequences. To tackle these challenges, government agencies are exploring ways to increase the use of active transportation as a daily transport mode. Active transportation includes any mode of transportation that requires physical activity (e.g. walking/bicycling) and can contribute significantly to a physically active lifestyle. The United Nations Environmental Program (UNEP) and the European Environmental Agency (EEA) have developed policies to encourage the use of bicycling and walking (Rojas-Rueda, De Nazelle, Teixidó, & Nieuwenhuijsen, 2012). Transportation planners are also investigating how to increase the modal share of non-motorized modes like bicycling and walking to reduce transport-related emissions, delays, and parking problems in cities during peak hours. Bicycling is an effective, efficient, and sustainable alternative travel mode; particularly for traveling shorter distances (Zhuang, Jin, Shen, & Jiang, 2019). Bicycling has emerged as a popular travel mode; primarily, due to the wide range of advantages associated with bicycling which includes increased physical exercise, reduced vehicular emissions, improved accessibility, and reduced traffic congestions and delays (Tran, Ovtracht, & D’arcier, 2015). As a result, it offers benefits to bicyclists’ health and the urban environment (Jensen, Rouquier, Ovtracht, & Robardet, 2010). One of the major benefits of bicycling is the accessibility to public transit by addressing the first and last mile connectivity (Jäppinen, Toivonen, & Salonen, 2013). The growing demand for bicycling throughout the world is the reflection of understanding the limitations of motorized travel mode dependability (Fishman, Washington, & Haworth, 2013). 2 Despite the well-understood benefits of bicycling, the percentage of bicyclists remains low in many cities. In Texas, around 27% of the driving population rode a bicycle at least once during summer (Sener, Eluru, & Bhat, 2009). The percentage of commuters using bicycle in the United Kingdom and Ireland is around 1% and 2% respectively (Goodman, Green, & Woodcock, 2014). Bicycle-friendly cities in countries such as the Netherlands, Denmark, and Germany have around 10% to 37% of commuters bicycling to work (Fishman et al., 2013). As such, cities around the world have recognized bicycling as an important alternative mode of transportation, and are making significant investments to improve the bicycle network. For example, the City of Victoria invested on developing all ages and abilities (AAA) connected bicycle network in 2015 (Winters et al., 2018). The complete network included around 60 km of bicycle infrastructures with increased accessibility from the suburban centers. City of Kelowna is investing significantly on bike lanes, multi-use paths, cycle tracks, bicycle racks throughout urban city centers, and racks on transit buses to boost active transportation (City of Kelowna, 2016). According to Lanzendorf & Busch-Geertsema (2014), bicycle infrastructure improvement is identified as a potential bicycling strategy to achieve bicycle share up to 20% by 2025 in Berlin. However, for effective investment decision-making, it is critical to understand the demand for bicycle facilities. Specifically, how bicycling demand varies spatially (i.e. built environment, traffic, land use, and accessibility characteristics), and temporally (i.e. weather, time of day, and season) by infrastructure type. To effectively promote bicycling, it is essential to understand their user behavior such as travel speed and destinations. For example, speed is a critical element to promote bicycling as an attractive alternative travel mode as well as making infrastructure investment decisions such as signal timing adjustment incorporating bicyclists. Speed is also an important level of service (LOS) measure of the urban transportation network (Yamamoto, Kobayashi, & Uesaka, 2012). 3 Although speed is a common LOS measure for most of the vehicular mode, very little is known regarding bicyclists’ speed choice decisions (Strauss & Miranda-Moreno, 2017). Since, bicycle is often preferred as a faster commute mode for shorter distances, it is essential to understand bicyclists’ speed choice behavior (Faghih-Imani, Anowar, Miller, & Eluru, 2017). However, such behavioral understanding of bicyclists was previously challenging primarily due to the unavailability of data. Majority of travel surveys collects information of motorized travel modes (Pasaoglu et al., 2014). However, technological advancements in the 21st century have introduced newer data sources such as GPS records. GPS data has been utilized extensively for travel behavior studies (Grengs, Wang, & Kostyniuk, 2008; Krause & Zhang, 2019; Ohmori, Muromachi, Harata, & Ohta, 2000). For example, Krause & Zhang (2019) examined the prediction of vehicle destination in real time using GPS data. In the case of study related to bicycle user behavior, Hood, Sall, & Charlton (2011) developed a route choice model using GPS data collected from mobile phone users in San Francisco. However, GPS data often lacks precision due to noise. Therefore, actual trip identification from GPS data is a critical concern prior to data analysis. Machine learning approach has proven to be effective in identifying the trips and eliminating noises. For example, Yazdizadeh, Patterson, & Farooq (2019) applied a machine learning method to identify trips based on the GPS data. Hence, machine learning methods need to be developed for effectively identifying the actual trips in GPS data. In recent times, bikeshare services have experienced tremendous success in many countries, including the USA, Canada, Netherlands, and China, among others (Xing, Handy, & Mokhtarian, 2010). Bikeshare provides an alternative shared transportation mode, benefiting its users and overall ensuring a healthier living environment (Tran et al., 2015). Cities around 4 the world are investing in initiatives related to the promotion of bikeshare services (Xu & Chow, 2018). Bikehsare services can be classified as: i) station-based bikeshare services (SBS), and ii) dockless bikeshare services (DBS). DBS is a popular bikeshare service – a bikeshare system that is station-less and free floating (Shen, Zhang, & Zhao, 2018). In such systems, users have the flexibility to drop-off a bicycle for the subsequent user at any location within the designated territory. Unlike traditional rental bicycles, dockless bicycles can be utilized and returned at literally anywhere (Jin, Cheng, Li, & Hu, 2018). Therefore, one of the major advantages of DBS is that users are not required to search for vacant docks to return their bicycles. In addition, DBS does not require space for docking stations. To enhance the adoption of DBS services, it is important to understand DBS user behavior, particularly why individuals choose DBS over other transportation modes. To further promote DBS services, it is essential to provide appropriate bicycling infrastructures at the users’ travel destinations. In the transportation literature, destination choice modeling can be broadly categorized into: i) destination choice by trip purposes, and ii) destination choice by travel modes. For example, several destination choice studies have focused on modeling destination choice decisions for shopping trips (Scott & He, 2012), recreational trips (Pozsgay & Bhat, 2001; Seddighi & Theocharous, 2002), and commute trips (Jonnalagadda, Freedman, Davidson, & Hunt, 2001), among others. Among the studies related to travel modes, several research focuses on destinations for walking trips (Clifton, Singleton, Muhs, & Schneider, 2016), and bicycle trips (Hyodo, Suzuki, & Takahashi, 2000), among others. However, limited studies have analyzed the destination choice of bikeshare service users (Faghih-Imani & Eluru, 2015, 2018). Specifically, destination choice behavior of DBS users has not occurred to any significant extent. Such understanding will assist in ensuring a successful and effective operation and expansion of the DBS services. This will enable the 5 system operators to plan and design more attractive DBS services, which in return is expected to improve the adoption and usage of bicycling. 1.2 Objectives The overall aim of this research is to develop methods to understand the bicycling demand and behavior of bicycle users. To achieve this goal, this thesis evolves within the following four objectives: i) To develop an advanced econometric model for estimating daily bicycling demand and examining the combined effects of spatial and temporal attributes. ii) To develop a machine learning (ML) algorithm for processing GPS data and a hybrid econometric model to examine the speed choice decisions using the GPS data. iii) To develop a state-of-the-art econometric model to understand dockless bikeshare service (DBS) users’ destination choice decisions, and assessing the inter-dependency between origin and destination of trips. iv) To develop an advanced econometric model to investigate the reasons behind individuals’ decision to choose DBS over other travel modes. 1.3 Significance This thesis involves developing advanced modeling techniques to understand bicycling demand and bicycle user behavior. For example, this thesis develops a direct demand model to estimate daily demand for bicycling. The model extensively examines the influence of 6 temporal and spatial characteristics and captures unobserved heterogeneity across the users. In the case of understanding the bicycle user behavior, this research develops advanced models to investigate the speed choice and destination choice decisions, and reasons for choosing dockless bikeshare services. For the speed choice analysis, an advanced econometric model is developed using GPS records that captures heterogenous behavior among the users. In addition, a machine learning (ML) algorithm is developed for data cleaning purposes identifying the actual trips from the GPS records. The model tests the effects of weather characteristics, built environment attributes, and accessibility measures on the speed choice behavior. For the destination choice model, a state-of-the-art econometric model is developed to address multi-dimensional heterogeneity among the users. The model examines the inter-dependencies between trip origin and destination. To determine the appropriate micro-spatial unit of analysis for this spatial choice modeling exercise, the concept of bicycle analysis zone (BAZ) is introduced and defined. Finally, this thesis investigates the reasons behind individuals’ choice to use newer shared mobility services such as dockless bikeshare service (DBS) over other transportation modes. The model captures heterogeneity and tests the influence of built environment attributes including transportation infrastructure, land use, and neighborhood characteristics on individuals’ choice. 1.4 Thesis Outline This thesis comprises of total eight chapters including the introduction. Chapter two reviews the literature regarding the bicycling infrastructure planning, and newer mobility services such as dockless bikeshare services. This chapter further describes the literature relevant to bicycling demand, speed choice, destination choice, and behavior of bikeshare users. Then, the scope for further research in the domain of bicycling demand and user behavior analysis 7 is identified. This chapter posits several research questions based on the research gaps, followed by a concluding remarks. Chapter three presents a brief description of the study area. Then this chapter describes the several sources of data utilized in this thesis. After that, generation of built environment and land use variables are described. These data and variables are used in this thesis to develop models related to bicycling demand and behavior of bicycle users, which are discussed in the subsequent chapters. Chapter four presents a bicycling demand model using bicycle count data. This chapter focuses on the exploratory analysis of the data, modeling technique, independent variables used in the model, discussion on the model estimation results, and model prediction results. Chapter five focuses on developing a speed choice model using the GPS data of dockless bikeshare service users. This chapter presents the exploratory analysis of GPS data, data processing and modeling technique, independent variables tested in the model, results of data processing, discussion on model estimation results, and model validation results. Chapter six develops a hybrid destination choice model using trip itinerary data of dockless bikeshare service users. This chapter focuses on the exploratory analysis of trip itinerary data, data processing method, method of generating the spatial unit of analysis and choice set for the model, modeling technique, independent variables tested in the model, discussion on model estimation results, and model validation results. 8 Chapter seven focuses on developing model that investigates the reasons to choose dockless bikeshare service (DBS). This chapter shows the exploratory analysis of DBS user survey data, modeling technique, independent variables used in the model, discussion on model estimation results, and elasticity effects of explanatory variables used in the model. Finally, chapter eight summarizes the findings of this research and discusses the key contributions, followed by the concluding remarks and scope of future research. 9 Chapter 2: Literature Review 2.1 Introduction This research contributes to the existing literature by investigating the demand and user behavior of bicycle users. Bicycle is an important alternative mode for traveling shorter distances. Government agencies around the world are investing in bicycle network to reduce traffic congestion and improve public health. Recently, bikeshare services have emerged as a popular shared mobility option. Such services and technological advancements have generated huge amount of data (e.g. eco-counter, GPS) which was not previously available. This puts forward the scope to investigate bicycling demand and better understand bicycle user behavior including their travel speed, origin and destination of the trips, and reasons for using bicycling and shared mobility services. Such understanding is important to improve the attractiveness of bicycle mode and assist in effective infrastructure investment decision-making. This chapter reviews relevant literatures on understanding the bicycling demand and its user behavior. First, research in the domain of bicycle infrastructure planning and newer mobility services such as bikeshare services are discussed. Then, a review of emerging data sources (e.g. GPS data) are described. The subsequent sections discuss the modeling of bicycling demand, speed choice, destination choice, and reasons for using bikeshare services. After that, the existing research gaps in bicycle planning are described along with the research questions. Finally, this chapter concludes with a concluding remarks. 10 2.2 Bicycle Infrastructure Planning and Understanding Bicycle User Behavior Extensive research on bicycle planning has been performed in the recent years. Particularly, several studies have focused on bicycle infrastructure usage. For example, Pedroso, Angriman, Bellows, & Taylor (2016) evaluated the use of bicycle in Boston after an expansion of the bicycle network between 2007 and 2014. They found an increase in bicycle usage from 0.9% to 2.4% following the network expansion. Perception of safety has been identified as the key performance measure for the quality of any bicycling infrastructure (Hull & O’Holleran, 2014). Research has consistently demonstrated that segregated bike lanes affect the bicyclist’s perception of safety and therefore promotes bicycling in the community (Pucher, Dill, & Handy, 2010). In another study, Teschke et al. (2012) found that in the case of protected cycle track, injury risk is lower. This suggests that bicyclists’ safety increases within the protected infrastructure facilities. Ryu, Su, Chen, & Choi (2019) predicted bicycling demand for smaller communities with limited resources in Utah, USA to facilitate bicycle infrastructure investment planning and route guidance. Bicycle infrastructure can be categorized into three types: (i) protected cycle tracks, (ii) shared paths (walking and bicycling), and (iii) on-road facilities such as bike lanes (Campbell, 2012; Ministry of Transportation and Infrastructure, 2019) (Figure 2.1). Cycle track Shared path Bike lane Figure 2.1 Bicycle Infrastructure Facilities (Source: City of Kelowna and CBC news) 11 For effective bicycle planning and infrastructure investments, it is important to understand bicycle user behavior. For example, Hyodo et al. (2000) investigated the bicycle route and destination choice behavior for bicycle users in Japan. They identified several policy implications related to bicycle infrastructure, road conditions, station conditions, and bicycle parking opportunity in stations, among others. Another study examined the attributes contributing to the selection of bicycle routes in Texas, USA (Sener et al., 2009). The results showed that journey time, volume of traffic, number of stops, speed, and on-street parking have a significant impact on route choice decisions. Caulfield, Brick, & McCarthy (2012) conducted a stated-preference study to understand commuters’ mode and route choice. The volume of traffic, speed and number of junctions had a significant effect on their decision to ride a bicycle. Bicycle lane is often effective to reduce the level of traffic stress. Furth (2012) identified the criteria for categorizing road links according to the level of traffic stress. One of the important factors to understand bicycle user behavior is the travel speed choice decisions. For example, Twisk, Boele, Vlakveld, & Christoph (2013) explored the speed choice decisions for both traditional bicycles and electric bicycle users. It was found that complexity of road environment significantly influences the speed of bicyclists. Traditional bicycle speed was found to be as high as 25 km/hr on downhill areas. In contrast, lower speed was observed along the sharp curves for both traditional bicycles and electric bicycles. Speed distribution for electric bicycles are also investigated under mixed traffic flow situation (Xu, Li, Qu, & Tao, 2015). Results suggested that average speed was around 27% higher compared to the conventional bicycles. In another study, Parkin & Rotheram (2010) found that the impact of uphill topography on speed has substantial effect than that of downhill topography. Results further suggested that around 85% of cases, the speed of bicyclist was 22 km/hr on level surface. Bernardi & Rupi (2015) evaluated the impedances that contributed to lower bicycle 12 speed. They found an approximately 30% reduction in bicycle speed while sharing the path with pedestrians. In another study, El-Geneidy, Krizek, & Iacono (2007) examined the bicycle speed on off-street and on-street bicycle facilities using real-time GPS data. Recently, Lindsey & Alexander (2019) developed an active travel speed and route choice model. Their findings reveal useful policy implications for bicycle network planning and promoting newer bicycle technologies. Another critical element to understand bicycle user behaviour is their destination choice decisions. For instance, Faghih-Imani & Eluru (2015) investigated users’ decision to prefer a destination location for Chicago’s Divvy – bikeshare system in USA. They found a significant effect of travel distance, land use attributes, built environment characteristics, and accessibility features on destination location preferences. Another study developed a destination and route choice model for Providencia, Santiago (González, Melo-Riquelme, & de Grange, 2016). Results suggested that destination and route choice decisions are influenced by proximity to transit stops and presence of bicycle infrastructure. Recently, with the increase of city population and congestion, cities are investing on smarter, greener, and convenient alternative travel modes. One of such newer and popular mobility options is the shared mobility services such as bikeshare services. To effectively promote such micro mobility services, it is critical to understand their user behavior such as the reasons for choosing this shared service and the choice of destination locations. 2.3 Shared Services Bikeshare services have recently experienced significant growth around the world. It has evolved substantially over the years – from the first generation pioneered by the Netherlands 13 in the 1960s to the fourth generation of dockless bikeshare services (DBS) initiated in China in 2015 (Guo, Zhou, Wu, & Li, 2017; Shi, Si, Wu, Su, & Lan, 2018). Bikeshare has become remarkably popular in Southeast Asia and Europe. One of the major advantages of bikeshare services is the elimination of personal bicycle ownership. Furthermore, bikeshare services are inexpensive compared to transit and car rental, and convenient for occasional bicycling. Broadly, bikeshare systems can be categorized into the traditional docking bikeshare and emerging dockless bikeshare (McKenzie, 2018). 2.3.1 Station-based Bikeshare Service (SBS) Station-based bikeshare service (SBS) is a system where individuals can rent a bicycle for short term duration from a particular station using a daily/ monthly/ trip-specific subscription fee. This system requires the return of bicycles to the docks/ stations (Figure 2.2). At the docks, bicycles are securely locked. Bicycles can be unlocked and used after the payment confirmation. Several studies have focused on SBS user behavior; particularly, examining the key factors influencing the demand for SBS (Biehl, Ermagun, & Stathopoulos, 2018; Frade & Ribeiro, 2014; He, Song, Liu, & Sze, 2019; Rudloff & Lackner, 2014). Furthermore, Guo et al. (2017) investigated the factors affecting the usage of SBS and the degree of satisfaction of the users. They tested the influence of bicycle ownership, travel time, station location, user perception, and socio-demographic attributes. They argued that a significant positive correlation exists between bikeshare usage and degree of satisfaction. Besides, Bachand-Marleau, Lee, & El-Geneidy (2013) examined bikeshare usage and frequency for BIXI services in Montreal. They examined the influence of socio-demographics, travel, and spatial configuration attributes. They found that closer distance from home to docking hub and bicycle security aspects are the two most significant factors to promote bikeshare usage. Another study, Reynaud, Faghih-Imani, & Eluru (2018) focusing on the BIXI system in Montreal, 14 investigated the impact of built environment features, land use attributes, and bicycle infrastructure on bicycle availability at a station. The findings of this study suggested that unavailability of bicycles was more severe in zones with compact and higher mixed land uses. Faghih-Imani, Hampshire, Marla, & Eluru (2017) investigated bikeshare usage, and bicycle rebalacing strategies for Seville and Barcelona. They argued that station density and capacity, and land use configuration such as business, recreation, and restaurant locations have substantial impact on bikeshare. Wang, Lindsey, Schoner, & Harrison (2015) examined the effects of neighborhoods, nearby businesses, and employment opportunities on station-level demand. Tran et al. (2015) analyzed the arrival and departure flow at docking stations. They tested the effects of built environment factors including public transport, topographic, socio-economic, and network features. Xu & Chow (2018) tested the influence of bicycle infrastructures on bikeshare demand in the New York City. They confirmed that new bike lanes positively impact bicycling demand on the weekends. Figure 2.2 Station-based Bikeshare Service (SBS) 15 Figure 2.3 Dockless Bikeshare Service (DBS) 2.3.2 Dockless Bikeshare Service (DBS) Studying the previous literature, it is found that majority of the existing studies have emphasized on modeling the users’ behavior of station-based bikeshare systems (Bachand-Marleau et al., 2013). Dockless bikeshare service (DBS) has emerged as a popular micro mobility system among the users (Figure 2.3). It is the latest generation of bikeshare service, which has experienced a rapid expansion in the past 3-4 years (Shen et al., 2018). The emergence of DBS occurred from the need to tackle the limitations of the station-based bikeshare system. The free-floating feature of DBS addresses the challenges associated with the requirement of spaces for the docking stations and associated facilities of docking bikeshare services. DBS provides users the flexibility to pick up and return bicycles at literally anywhere in the city, as they do not require to search for stations. DBS users can unlock and use the bicycles by scanning the QR code via mobile-based applications. This emerging 16 system promises to revolutionalize the conventional bikeshare market (Tu, Chen, Gao, Yang, & Chen, 2019). Newer technological advancement such as DBS demands investigation of the travel behavior of dockless bikeshare users. Specifically, the speed choice decisions and destination location choices are need to be examined extensively. Because of its recent emergence, there exists very limited research that focused on the speed choice and destination choice behavior of DBS users. Majority of the studies analyzed these behavior for station-based bikeshare services users. 2.4 Emergence of GPS Data One of the common techniques to extract data for bicycling planning research is to utilize video cameras. For instance, Figliozzi, Wheeler, & Monsere (2013) developed mathematical formulation to determine the bicyclists’ acceleration and travel speed for signal time modification using video data collection method. This method has limitations such as susceptibility to ambient conditions, exhaustive labor work for data coding and aggregation, and privacy concern of people (Turner, 1997). These limitations are eliminated with the emergence of mobile app-based GPS data in recent years. For instance, Clarry et al. (2019) developed models for bicyclists’ speed analysis using smartphone app-based GPS data for Toronto. GPS data is also used to develop route choice models for bicyclists in Portland, USA (Broach, Dill, & Gliebe, 2012), and destination choice models for Chicago Divvy and New York Citibike bicycle users (Faghih-Imani & Eluru, 2015, 2018). Lue & Miller (2019) developed a route choice model for pedestrians in Montreal using smartphone based GPS data. Another source of data is the GPS device equipped bicycles. The recent technological advancements have enabled newer bikeshare services such as the DBS to include GPS equipped bicycles 17 in their fleet. The GPS technology generates enormous amount of information regarding users’ travel trajectories. GPS log comprises of series of points illustrating the longitude, latitude, and corresponding timestamp. Although GPS data facilitates receiving tremendous amount of travel information, it also posits some critical challenges. For example, GPS records lack in precision due to the signal noise generating jumps around the user’s exact location (Berjisian & Bigazzi, 2019). Therefore, the accuracy of the modeling exercises using GPS data largely depends on the data filtering and processing techniques. The majority of the previous studies have used a rule based (RB) method to identify the erroneous GPS records (Bohte & Maat, 2009). One of the limitations of the RB method is to generate arbitrary rules to filter GPS data. This results in the elimination of a high percentage of trip points during the filtering process, which in many cases are not erroneous. For instance, Khatri et al. (2016) implemented RB method for GPS data cleaning and found a reduction of almost 60% trip points. Hence, the RB method poses a high probability for eliminating the actual trip points. There is a need to develop methods to improve GPS data cleaning procedures. 2.5 Bicycling Demand Bicycling demand estimates the ridership due to the existence of certain bicycle facilities. The purpose of modeling bicycling demand is to forecast bicycle trips traveled in bicycle infrastructures (BikeWalkKC, 2016). It is an important critical input that identifies effective bicycle infrastructure investment policies. Demand modeling of bicycling infrastructures has direct policy implications. The majority of these demand models are developed using count information from automated counters and focused on examining the effects of spatial attributes on bicycling demand (Fagnant & Kockelman, 2016; Osama, Sayed, & Bigazzi, 18 2017). For example, Fagnant & Kockelman (2016) developed alternative regression models such as Poisson regression and negative binomial models to analyze the direct demand for bicycling in Seattle, USA. They tested the influence of traffic volumes, pavement conditions, speed of vehicles, job densities, and proximity to recreational areas, and residential land uses, among others. Osama et al. (2017) utilized a fully Bayesian approach to estimate bicycle kilometers traveled for Vancouver, Canada. They examined the effects of network indicators, land use attributes, and road facility characteristics. They argued that connected, dense, flat, continuous, and off-street bicycle networks yield a greater kilometer traveled by bicyclists. Many studies examined the effects of temporal factors on bicycling demand. For instance, Le, Buehler, & Hankey (2019) utilized a multi-level mixed effect model to examine the temporal trends for bicycling and walking using automated counts from 13 metropolitan cities in USA. Thomas, Jaarsma, & Tutert (2013) developed a bi-level weather model to investigate bicycling demand in Gouda and Ede, Netherlands. They found that in the case of weather, bicycling demand of recreational users were extremely sensitive compared to the demand of utilitarian users. They also concluded that 80% of daily bicycle flow fluctuations can be explained by weather-related attributes. Miranda-moreno & Nosal (2011) examined the temporal factors and effect of weather on bicycling in Montreal, Canada. Results suggested substantial impact of precipitation, temperature, and humidity on bicycling demand. For example, they found that temperature greater than 28°C and humidity higher than 60% influence bicycling demand negatively. Another study examined the impact of precipitation and temperature on commuting and non-commuting bicycle trips in Charlottesville, USA (Gosse & Clarens, 2014). They developed a multi-level temporal model that includes hourly, weather-related, and commute-day attributes using sparse count observations. 19 A limited number of studies have examined the combined temporal and spatial effects on bicycling demand. Among the few, Chen, Zhou, & Sun (2017) developed a generalized mixed model to investigate the combined effects of spatial and temporal factors. They used a 5-year count dataset from Seattle, USA. The study found that non-winter seasons, peak hours in weekends, mixed land use, and a higher percentage of water bodies, among others, are correlated with an increase in bicycle counts. Lu, Mondschein, Buehler, & Hankey (2018) utilized a regression modeling technique to investigate the hourly bicycling demand. They tested the impacts of spatial variables (i.e. population density, land use, and transportation network), and temporal variables (i.e. time of day). The majority of the previous studies regarding bicycling demand analysis have adopted a count regression modeling technique. For example, Tabeshian & Kattan (2014) developed Poisson regression and multiple linear regression models for Calgary, Canada. Hankey, Lu, Mondschein, & Buehler (2017) developed a stepwise linear regression model to investigate bicycling and walking demand in Virginia, USA. Fagnant & Kockelman (2016) developed alternative conventional regression models, including Poisson regression and negative binomial models. However, count regression model such as negative binomial model is incapable of capturing unobserved heterogeneity. There is a homogeneity assumption on the unobserved components of the regression modeling framework. Further research is required to develop a modeling technique to capture unobserved heterogeneity. 2.6 Bicyclists’ Speed Choice Decision Travel speed is an important factor to understand individuals’ travel behavior (Lindsey & Alexander, 2019), as well as an appealing factor to increase the attractiveness of a travel mode (Clarry et al., 2019). As a result, speed is an essential attribute to estimate bicyclists’ 20 demand in an urban region. It is also crucial to design multi-modal transportation network such as traffic signal phasing considering bicycle traffic (Taylor & Mahmassani, 2000). Traditionally, studies in the domain of bicycle infrastructure planning have attempted to understand bicycling demand and route choice decisions (Khatri et al., 2016; Lue & Miller, 2019; Rowangould & Tayarani, 2016), neglecting bicyclists’ speed analysis. Bicycling speed analysis is more complex in nature compared to the vehicular traffic in terms of lateral movement, movement within the lane, and overtaking behavior (Pu, Li, Wang, Ye, & Fan, 2017). Recently, researchers have invested efforts to examine the factors influencing bicyclists’ speed choice behavior. For instance, El-Geneidy et al. (2007) examined how average speed varies by bicycling facility types (e.g. protected and unprotected cycle facility) and surrounding environment along the road network for Minneapolis, USA. They found that mean speed of bicyclists’ is positively associated with protected bicycle facilities, and trip length; whereas number of intersections influences mean speed negatively. Parkin & Rotheram (2010) also developed models for mean speed and acceleration analysis. They used downhill and uphill gradient as explanatory factors. They found that an additional 1% downhill slope increases speed by 0.86 km/hr. On the other hand, every additional 1% uphill slope contributes to a decline in speed of 1.44 km/hr. Another study investigated bicyclists’ speed in on-street and off-street facility due to the variation of motorized vehicle and pedestrian volume (Bernardi & Rupi, 2015). They found that a higher number of pedestrians have negative impact on bicyclists’ average speed. They also found a maximum 31.7% reduction in average speed when bicyclists travel in mixed traffic. Lindsey & Alexander (2019) implemented a utility-based modeling technique to analyze speed fluctuations of electric bicycle users. In another study, Jensen et al. (2010) found that bicyclists’ average speed is higher than that of cars during the morning peak in the downtown area of Lyon. Furthermore, Xu et al. (2015) explored the speed 21 distribution of bicyclists in heterogeneous traffic conditions in Hangzhou, China. They revealed that a higher proportion of electric bicycle is positively associated with mean speed. Clarry et al. (2019) analyzed bicyclists’ speed choice behavior for the Toronto area in aggregate level. In the case of speed choice decisions, regression modeling techniques have been utilized largely in previous studies (Bernardi & Rupi, 2015; Parkin & Rotheram, 2010; Strauss & Miranda-Moreno, 2017; C. Xu et al., 2015). For example, Strauss & Miranda-Moreno (2017) utilized a regression model to evaluate the effects of geometric and built environment features on bicyclists’ speed. Bernardi & Rupi (2015) developed a linear regression model to examine the average speed of bicyclists. Xu et al. (2015) adopted a linear regression model to understand the relationship between average speed and proportions of electric bicycles. Parkin & Rotheram (2010) also developed linear regression models for mean speed and acceleration analysis. Recently, Clarry et al. (2019) extended the linear regression model to a multi-level linear mixed model to examine bicyclists’ speed choice decisions. One of the major limitations of the linear regression modeling techniques is their inability to capture unobserved heterogeneity. There is a homogeneity assumption on the unobserved components of the linear regression modeling framework. Further research is required to develop a modeling technique to capture unobserved heterogeneity. 2.7 Destination Choice Behavior of Dockless Bikeshare Service (DBS) Users Destination choice decision is another critical factor to understand individuals’ travel behavior (Hasnat, Faghih-Imani, Eluru, & Hasan, 2019). Therefore, number of studies investigated the destination choice behavior in several transportation perspectives. For example, Kitamura, Chen, & Narayanan (1998) developed home-based and non-home based destination choice 22 models assessing the influence of time of day, activity duration, and residing location. In another study, modeling framework for non-work activity location choice was proposed examining the heterogeneity in choice behavior and spatial interaction (Sivakumar & Bhat, 2007). Researchers further investigated the decision process in station choice for the commuter railway users in Montreal region (Chakour & Eluru, 2014), recreational activity location choice for the visitors in Seoul, Korea (Hong, Kim, Jang, & Lee, 2006), and residential location choice for the residents in Seattle, USA (Waddell, Bhat, Eluru, Wang, & Pendyala, 2007). Few researchers explored the destination choice behavior of bicycle users. For example, Hyodo et al. (2000) developed a train station choice model for the bicycle users in the city of Utsunomiya and Kurume in Japan. Most of the studies in the domain of destination choice modeling of bikeshare service users have focused on the station-based bikeshare service (SBS). For example, Faghih-Imani & Eluru (2015) developed a multinomial logit model to investigate the choice of destination locations of the SBS users in Chicago. They argued that the characteristics of the bicyclist, destination, and trip significantly influence the destination preferences. In another study, Faghih-Imani & Eluru (2018) developed a finite mixture of multinomial logit (FMMNL) model to examine bikeshare users’ destination preferences in the context of SBS in New York City. They accommodated heterogeneity within the FMMNL framework by assigning the trips into different segments, and consequently estimated the segment-specific destination choice models. They revealed that station capacity, distance to station, population density, and access to transit stations, among others significantly influence destination choice decisions. González et al. (2016) developed a hierarchical logit model to jointly investigate the effects of supply attributes (e.g. presence of bikeways) on destination and route preferences of SBS 23 users in Santiago, Chile. They found that proximity to transit stops and the existence of bikeways affect destination and route choice decisions. Unit of analysis is an important factor to be considered during spatial choice modeling such as destination choice. Particularly, destination choice modeling of DBS users posits a unique challenge in generating the spatial choice alternative. In the case of the SBS user behavior, the majority of the studies have considered stations as the spatial unit of analysis (Faghih-Imani & Eluru, 2015, 2018). DBS is a station-less system, relaxing the need to return bicycles at the designated stations. This puts forward a unique challenge to determine the unit for spatial analysis. One approach might be to consider traffic analysis zones (TAZ) as the spatial unit, which is commonly used as the unit for vehicular trips (Khan, Kockelman, & Xiong, 2014; Lee, Abdel-aty, & Jiang, 2014). However, TAZ is not a suitable spatial unit for bicycle trips. Since bicycle trips tend to be shorter, implying a significant number of intra-zonal trips in the TAZ system. This need calls for defining the unit of analysis for such spatial choice modeling of the dockless bikeshare system. This spatial unit requires to be at an adequate micro-scale resolution to tackle the large share of intra-zonal bicycle trips in case of a TAZ-level analysis. In the case of discrete choice decisions such as the destination choice, random utility-based discrete choice modeling techniques have been predominantly utilized. For example, one of the most common methods for destination choice modeling is the traditional multinomial logit models (Clifton et al., 2016; Faghih-Imani & Eluru, 2015; Mei, 2013). For example, Clifton et al. (2016) developed destination choice multinomial logit (MNL) models to understand the pedestrian travel behavior for different trip purposes in Portand, USA. The utility equations of developed MNL models include traveler characteristics, pedestrian barriers, and pedestrian support measures, among others. Faghih-Imani & Eluru (2015) also developed destination 24 choice model for Chicago’s Divvy bikeshare system in USA. The model was based on multinomial logit modeling technique that included a random error term. This term was presumed to be independently and identically Gumbel-distributed across the dataset. Similar modeling approach was used to develop the destination choice model for commercial vehicles in North Carolina, USA (Mei, 2013). One of the major limitations of conventional multinomial logit modeling techniques is their inability to capture unobserved heterogeneity. For example, the MNL model holds the independence from irrelevant alternative (IIA) assumption, which imposes a homogeneity assumption on the unobserved components of the utility function (Fatmi & Habib, 2016b). Since bicyclists exhibit heterogeneous behavior (Sener et al., 2009), modeling frameworks need to be flexible to accommodate such dynamics. Unless such heterogeneity is captured within the modeling framework, the model estimation might be biased and inconsistent, resulting in poor forecasting accuracy (Fatmi, Chowdhury, & Habib, 2017). This restrictive homogeneity assumption can be relaxed by extending the traditional models into a random parameter (RP) model and latent segmentation-based (LS) model. The RP family of models capture heterogeneity by assuming a continuous distribution of the parameters over the sample individuals (Fatmi & Habib, 2016a; Revelt & Train, 1998). The LS family of models accommodate heterogeneity by assuming a discrete distribution of the parameters (Fatmi, Habib, & Salloum, 2014; Greene & Hensher, 2003). However, both the RP and LS models capture heterogeneity in one dimension, i.e. either using a continuous distribution or a discrete distribution of the parameters. Further research is required to develop a hybrid modeling technique to capture multi-dimensional heterogeneity. 2.8 Reasons for Choosing Dockless Bikeshare Service (DBS) Due to the fairly recent adoption of dockless bikeshare service, limited studies have focused on analyzing this newer technology. Among the few, Shi et al. (2018) employed a social 25 network analysis technique to understand the primary challenges with DBS and identify the counter-measures; particularly, focusing on the sustainability performance of DBS from a network perspective. They explored several stakeholder-associated factors, such as sharing transport schemes and product lifecycle management, among others. Jia & Fu (2019) performed a statistical analysis to examine the correlation between dockless bikeshare programs, and travel mode choice in Shanghai. Jin et al. (2018) explored the impacts of dockless bikeshare on public transportation. They argued that DBS serves as a compliment rather than a substitute for mass transportation. They also found that increased usage of DBS leads to less congestion and emission. Shen et al. (2018) explored the spatio-temporal patterns of dockless bikeshare usage in Singapore. They developed a spatial autoregressive model utilizing the GPS data. The model identified the influence of bicycle fleet size, surrounding built environment, access to public transport, bicycle infrastructure, and weather conditions on the usage of dockless bikeshare services. Their findings suggested that heterogeneous land use mix, access to public transportation, and improved bicycle facilities positively impact on the usage of DBS. Li, Guo, Zhang, & Lv (2019) investigated the usage pattern of DBS as a feeder mode to rail transit across the urban morphology in Beijing. They suggested strategies for DBS planning, parking, rebalancing, and managing around the rail transit. Furthermore, Ma, Ji, Yuan, Oort, & Hoogendoorn (2019) developed a spatially and temporally weighted regression model to analyze the factors influencing the demand for DBS. They revealed that higher density of recreational locations promotes DBS usage during the weekends; whereas, higher density of roads and transit stations impedes DBS usage during the peak hours on weekdays. However, research related to DBS user behavior analysis has not occurred to any significant extent. For example, it is important to understand the reasons for the users to choose dockless bikeshare services over other modes of transportation. 26 2.9 Research Gaps in the Existing Literature Above discussion identifies the scope for further research in several aspects of bicycle planning research. Firstly, there is a limited number of studies that examined the combined effects of temporal and spatial factors on bicycling demand. Therefore, there is a gap in investigating the combined influence of spatial (e.g. built environment and land use attributes), and temporal (e.g. weather and time) factors on bicycling demand. Secondly, limited studies have investigated bicyclists’ speed choice decisions. Among the few, the majority of the studies have adopted an aggregate approach by considering average speed of the bicyclists. There is a scope to examine bicyclists’ speed choice decisions at the disaggregate level. Further emphasis is required to extensively test built environment attributes such as road infrastructure and bicycle facilities, land use characteristics, weather attributes, and accessibility features on bicyclists’ speed choice. Thirdly, there is a research gap in modeling destination choice behavior of DBS users, since majority of previous studies have focused on the SBS. Moreover, it is imperative to examine the inter-dependencies between the origin and destination of a trip. The spatial unit of analysis needs to be defined for modeling destination choice of DBS users. The spatial unit needs to be sensitive to the relatively shorter bicycle trips compared to other travel modes – allowing minimal intra-zonal trips. Fourthly, limited studies have focused on understanding the bikeshare services. Further research on DBS user behavior is required for effective and successful operation of this service. Particularly, modeling the reasons for choosing DBS over other travel modes have not occurred to any significant extent. It is critical to understand why individuals use this service, what factors influence the choice. Specifically, it is important to understand how built 27 environment attributes impact the reason for choosing dockless bikeshare services, which is expected to offer important behavioral insights for shared mobility-friendly policy-making. Finally, there are scopes for methodological innovations in: i) developing methods to improve the GPS data processing techniques, and ii) developing econometric methods to capture unobserved heterogeneity. In the case of GPS data processing techniques, state-of-the-art methods are required to tackle the drawbacks of the existing models such as RB method (Berjisian & Bigazzi, 2019). For capturing unobserved heterogeneity, advanced modeling techniques need to be developed to address the limitations of the traditional regression models such as linear regression and Poisson regression, and conventional discrete choice models such as multinomial logit model. Based on the above research gaps, this thesis will address the following research questions: i) How to assess the influence of both spatial and temporal factors on bicycling demand? How to tackle the limitations of traditional regression modeling techniques for bicycling demand models? ii) How bicyclists’ speed choice behavior is influenced by built environment and land use characteristics? How to develop an advanced method to improve the data processing techniques of the GPS records? iii) Where the bicyclists are traveling? Particularly how the destination choice decisions vary with trip origin for DBS users? How to generate an appropriate spatial unit of analysis for examining DBS users’ destination choice decisions? 28 iv) Why individuals choose the newer shared mobility options such as dockless bikeshare services over other travel modes? v) How to develop advanced modeling techniques to capture unobserved heterogeneity among the individuals? 2.10 Concluding Remarks This thesis contributes to the existing literature by addressing the above-mentioned research questions. For example, this thesis develops a direct demand model to estimate daily demand for bicycling. This study extensively examines the influence of weather (i.e. temperature, precipitation, and snowfall), time of day, week, and season as temporal factors along with spatial factors such as bicycle infrastructure, accessibility measures, road infrastructure, and traffic characteristics. Traditional regression model is extended to a latent segmentation-based regression model; specifically, a latent segmentation-based negative binomial (LSNB) model is developed to capture heterogeneity by allocating bicycle infrastructure into discrete latent segments. This thesis investigates bicylists’ speed choice behavior by developing a latent segmentation-based linear regression (LSLR) model. The LSLR model captures heterogeneity by distributing bicyclists’ trips into discrete latent segments. The model is developed using GPS data. A machine learning technique; particularly, Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is developed to improve the data cleaning accuracy of GPS records. Finally, the effects of weather, built environment, and accessibility attributes on speed choice behavior are tested. 29 The research further contributes by investigating the destination choice decisions of the dockless bikeshare service users. To tackle the challenge associated with defining the spatial unit of analysis, bicycle analysis zones (BAZs) are generated. In the case of capturing unobserved heterogeneity, a hybrid of the LS and RP model is developed. Specifically, a random parameter latent segmentation-based logit (RPLSL) model is developed to capture multi-dimensional heterogeneity- i.e. the RPLSL model utilizes both the continuous and discrete distribution of the parameters. For example, the model assumes a discrete distribution to allocate the DBS users into discrete latent segments. Furthermore, to capture heterogeneity within the segments, a continuous distribution of parameters is assumed within the segments. This study methodologically disentangles the inter-dependencies between the origin and destination characteristics of a trip by testing origin attributes as the segment-membership parameters, and destination attributes as the segment-specific parameters. Origin and destination attributes include transportation infrastructure, land use, accessibility, and neighborhood characteristics. Finally, this thesis contributes to the existing literature by exploring the reasons behind individuals’ decisions to use DBS over other transportation modes. A latent segmentation-based logit (LSL) model is developed to capture unobserved heterogeneity among the DBS users. A flexible segment allocation model is formulated within the LSL framework that allocates DBS users into discrete latent segments. One of the key features of this study is to extensively test the influence of built environment attributes such as transportation infrastructure, land use, and neighborhood characteristics. Overall, this thesis investigates advanced modeling techniques to understand the demand and user behavior of bicyclists such as speed choice and destination choice decisions, and reasons to use newer shared 30 mobility services assessing the built environment, land use, neighborhood, weather, accessibility, socio-demographic, and trip characteristics. 31 Chapter 3: Data 3.1 Introduction Emergence of shared mobility services and technological advancements have introduced newer data sources such as automatic bicycle counters and GPS data. Furthermore, the opportunity to develop web-based surveys has enhanced the flexibility to conduct surveys and collect behavioral data. These data sources put forward the opportunity to understand the behavior of bicycle users such as speed choice and destination choice behavior, which was previously challenging. This thesis utilizes several such newer data sources to analyze bicyclists’ behavior. The outline of this chapter is as follows: section 3.2 presents a brief description of the study area. Section 3.3 describes all the data sources utilized in this thesis. Finally, section 3.4 shows the concept of several built environment and land use indices. 3.2 Study Area This study area for this thesis is the City of Kelowna, a city in the south of the province of British Columbia (BC) in Canada. It is one of the most densely populated cities in the Interior BC, with a density of around 601.3 people/square kilometers. Private vehicle is the predominant mode of transportation for Kelowna residents, as more than 80% of the commute trips are made using cars (Statistics Canada, 2019). Around 4% of the commute trips in Kelowna are made using a bicycle, which is above average compared to other cities in BC (Statistics Canada, 2016). The City of Kelowna’s active transportation master plan targets to increase bicycle modal share such that 25% of all trips under 5 km in length are made by bicycling and walking (City of Kelowna, 2016). As a result, the city has prioritized investments 32 in bicycle infrastructure. For example, currently, around $3.2 million to date is invested for the protected bike lane infrastructure near the downtown area. The existing bicycle facilities in Kelowna include more than 70 km pathways separated from vehicular traffic and approximately 280 km of bike lanes (City of Kelowna, 2019a). Bike lanes are usually painted using color or marking on the existing roads. Cycle tracks are separated from the traffic and pedestrian using bollards, curbs, or parked vehicles. On the other hand, shared path also known as multi-use trails, either paved or unpaved is used by both bicyclists and pedestrians (City of Kelowna, 2019b) 3.3 Sources of Data The thesis uses data from several sources such as bicycle count data, user survey data, and GPS data. Bicycle count data are collected using eco-counter. On the other hand, user survey and GPS data is extracted from the dockless bikeshare services (DBS) – operated in Kelowna. All data are collected from the City of Kelowna. 3.3.1 Bicycle Count Data Automatic counting is popular throughout the world to estimate the demand for bicycling facilities. Eco-counters count bicycles using the following two techniques: i) by a pressure system (e.g. tubes), and ii) by identifying the metal of bicycle wheels (e.g. zelt). Tube counters count bicyclists by identifying the pressure exerted by the bicycle wheels on tubes. On the other hand, zelt counter counts the bicyclists using induction loop technology. The City has installed eco-counters at several bicycle facilities in the city (Eco-Visio, 2019). This database includes daily bicycle counts for 12 sites in Kelowna from the months of January to December of 2018. These sites include Abbott street corridor, Cawston street corridor, City park multi cyclist facility, Clifton cycle track and sidewalk, Ethel street, Gyro beach pathway, Rails with 33 trails, Sunset drive, Waterfront walkway, and Rail trail at Bernard overpass. Bicycle facilities for the above count sites include shared paths, cycle tracks, bike lanes, and on-road facilities with traffic lanes. Sites are distributed around the city center and suburban areas (Figure 3.1). Figure 3.2 shows the eco-counter set up in one of the selected sites - Gyro beach pathway. The bicycle count data is used to develop the bicycling demand model. Figure 3.1. Bicycle Infrastructure and Count Locations in Kelowna 3.3.2 Survey of Dockless Bikeshare Service (DBS) Users The dockless bikeshare service (DBS) in Kelowna was operated by Dropbike, in partnership with the City of Kelowna during the summer and fall of 2018 (City of Kelowna, 2018). A DBS user could drop-off their bicycles anywhere near their final destination, attaching them to 34 racks, rings, and poles, as required by the local by-laws. Furthermore, designated parking locations for bicycles were established referred as ‘havens’. A penalty fee was charged to user’s account if a user ended trip outside the haven. On the other hand, individual returning their bicycle to a haven earned a credit on his/her account. To understand DBS users’ behavior, the City of Kelowna administered a user survey in 2018. The survey asked the respondents to identify the primary reasons for choosing DBS over other modes of transportation in their most recent bikeshare trip. The reasons can be thematically aggregated into the following six categories: cheapest option, fastest option, exercise purpose, recreational purpose, parking constraint, and unavailability of other modes (e.g. car) or other reasons. In addition, the survey collected information regarding respondents’ home location, socio-demographics such as age, gender, and income, and travel characteristics such as personal vehicle ownership, bicycle ownership, and bicycle usage frequency, and attitudinal statements such as perception towards the affordability and availability of the DBS, among others. After cleaning the data for missing information, the final sample includes complete responses from 282 DBS users. The user survey data is used to investigate the reasons for choosing DBS over other travel modes. Figure 3.2. Eco-counter Located at Gyro Beach Pathway 35 3.3.3 GPS Data The bicycles in the City of Kelowna’s DBS fleet were equipped with GPS devices. This GPS database provides trip itinerary information which includes the coordinates of the trip origin and destination, trip start time and end time, and trip duration, among others. This database is used to model the destination choice behavior of DBS users. The GPS database also includes trip trajectory information which refers to the coordinates and timestamps of the points along bicyclists’ route for each trip. Speed corresponding to each trip point is determined using the timestamp and location data. This trip trajectory data is used to develop speed choice model for DBS users. It is to be noted that DBS fleet did not have e-bikes. 3.3.4 Other Sources of Data This thesis collects data from other sources. For instance, weather information including temperature, precipitation, relative humidity, wind speed, and snowfall, among others is collected from the Environment Canada. Neighborhood attributes such as population density, average age, dwelling type, dwelling density, median income, employment density, commute mode, and commute time, among others, at the dissemination area (DA) – level are collected from the Statistics Canada. Transportation infrastructure and land use attributes are extracted from the Kelowna Open Data. Transportation infrastructure-related variables involve number of bus stops, bicycle network (i.e. cycle track, shared path, and bike lane), sidewalk, and road network, among others. Land use attributes include the percentage of residential, commercial, park, and institutional areas, among others. Location of activity points are collected from the Desktop Mapping Technologies Inc. (DMTI). This information includes the location of the 36 Central Business District (CBD), midtown city center, food stores, schools, retail stores, parks, lakes, and recreational centers, among others. 3.4 Built Environment and Land Use Indices Several important indices are generated using the above mentioned built environment and land use attributes. One of such indices is the land use index (LUI), a measure reflecting the diversity in land use pattern (Equation 3-1). LUI is determined using residential, commercial, park and aquatic, government, recreational, forest, and miscellaneous land type information (Mavoa et al., 2018). The index value ranges from 0 to 1. A value closer to 1 indicates a relatively heterogeneous land use mix; whereas, a value closer to 0 indicates a relatively homogenous land use. Since, diverse land mix encourages people to bike and walk more (Xu, Wang, Fu, & Kosmyna, 2017), this is an important index having policy implications. 𝐿𝑎𝑛𝑑 𝑈𝑠𝑒 𝐼𝑛𝑑𝑒𝑥 (𝐿𝑈𝐼) = (−1) ∗ ∑ 𝐿𝑡 ∗ 𝑙𝑛(𝐿𝑡𝑀𝑚=1 )/𝑙𝑛(𝑀) (3-1) Here, Lt = proportion of land use type t, and M = number of land mix categories Another important variable is the bike index (BI), measuring the overall bicycle friendliness of an area (Hartanto, Grigolon, Maarseveen, & Brussel, 2017). This indicator is generated utilizing several factors such as bicycle infrastructure facility type, accessibility to various facilities, destination points, park and aquatic environment, and land use mix, among others (Equation 3-2). Estimated values for each of the factors are normalized on a scale 0 to 1. Using Equation 3-2, BI is calculated considering equal weightage for all the factors. A BI value closer to 1 indicates a higher bicycle friendliness of the area. In contrast, BI value closer to 0 illustrates a lower bicycle friendliness of the area. 37 𝐵𝑖𝑘𝑒 𝐼𝑛𝑑𝑒𝑥 (𝐵𝐼) = 𝑏𝑖𝑛𝑓 ∗ (1/11) + 𝑟𝑏𝑖𝑛𝑓𝑡𝑟 ∗ (1/11) + 𝑅𝐶𝐼 ∗ (1/11) + 𝑚𝑝𝑟𝑒 ∗ (1/11) + 𝑛𝑑𝑝 ∗(1/11) + 𝑙𝑠𝑤 ∗ (1/11) + 𝑑𝑏𝑠𝑡𝑝 ∗ (1/11) + 𝑑𝑝𝑎𝑟𝑘 ∗ (1/11) + 𝑑𝑙𝑎𝑘𝑒 ∗ (1/11) + 𝐿𝑈𝐼 ∗ (1/11) + 𝑎𝑎𝑔𝑠 ∗(1/11) (3-2) Here, binf = bicycle infrastructure rbinftr = ratio of bicycle infrastructure to road RCI = road-connectivity index mpre = mean percent rise in elevation ndp = number of destination points lsw = length of sidewalk dbstp = distance to nearest bus stop dpark = distance to nearest park dlake = distance to nearest lake LUI = Land Use Index aags = Area of aquatic and green space Road-connectivity index (RCI) is determined using the number of road segments and the number of road nodes (Victoria Transport Policy Institute, 2009) (Equation 3-3). A higher index signifies higher access to destinations, implying an increased route preference. 𝑅𝑜𝑎𝑑 − 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝐼𝑛𝑑𝑒𝑥 (𝑅𝐶𝐼) = 𝑙/𝑛 (3-3) Here, l = total number of road links, and n = total number of road nodes 38 To test the effects of road elevation, a slope raster is created from the elevation data using the ArcGIS spatial analyst tool. This provides an impression of the steepness of a location by determining the highest rate of change in the elevation value compared to its neighboring location (Dolan & Lucieer, 2014; Magesh, Chandrasekar, & Soundranayagam, 2012). The steeper the surface, the higher the percentage rise of elevation; which is determined by dividing the rise by run and expressed in percentages (ESRI, 2009; Ghazal & Salman, 2015). The mean value of percentage rise of elevation is determined from the percent rise raster. In the case of accessibility measures, road network-based distance to several facilities and activity locations are generated using the network analysis tool in ArcGIS. 39 Chapter 4: Bicycling Demand Modeling1 4.1 Introduction This chapter develops a direct demand model for bicycling by utilizing automated daily bicycle count data at different locations of Kelowna. This research contributes to the following two dimensions: i) This study extends the traditional regression modeling technique to an advanced latent segmentation-based negative binomial (LSNB) model. The major purpose for developing the LSNB model is to capture unobserved heterogeneity among the count locations. A flexible segment allocation model is embedded within the LSNB framework to capture latent heterogeneity. This segment allocation model allocates the count locations into different latent segments with a discrete distribution. ii) This research examines the impact of temporal characteristics such as weather (i.e. precipitation, temperature, and snowfall, among others), and spatial attributes such as bicycle infrastructure (i.e. cycle tracks, shared paths, and bike lanes), built environment and traffic characteristics (i.e. bike index, road-connectivity index, and AADT, among others), and accessibility measures (i.e. distance to CBD, bus stop, and park, among others) on bicycling demand. The outline of this chapter is as follows: section 4.2 shows an exploratory analysis of bicycle usage data. Then, section 4.3 discusses the LSNB modeling technique. Section 4.4 introduces the independent variables tested in the LSNB model. Section 4.5 presents the results of the model that include the goodness-of-fit measures, latent segment allocation 1 This chapter is derived from Orvin, M., Fatmi, M., & Chowdhury, S. (2020). A Tale of Two Cities: Development of Cycling Demand Models for New Zealand and Canada. 55th Canadian Transportation Research Forum (CTRF), Montreal, Canada.” (accepted) 40 component results, parameter estimation results, and bicycling demand prediction results using the developed LSNB model. Finally, section 4.6 summarizes the findings of this chapter. 4.2 Exploratory Analysis of Bicycle Usage Data This research develops the LSNB model using daily bicycle count data over a year in 2018. A total of 823,341 trips were counted in 2018, with a daily average of around 3,067 trips. A higher share of bicycle trips was observed in protected bicycle facilities. About 66% of trips occurred during weekdays. Figure 4.1 illustrates the monthly bicycle demand for the year 2018. The analysis reveals that the demand is higher in summer compared to other seasons. Note that summer is from June to August. Bicycling demand shows significant variation over the months. For example, the demand drops to around 1,000 trips/site in January (Winter) from over 12,000 trips/location in July (Summer). The daily flow across the seasons is shown in Figure 4.2. Figure 4.2 suggests that bicycling demand is lower during winter season than summer. In the case of demand by bicycle facilities, demand is higher for protected bicycle infrastructures (i.e. cycle tracks and shared paths) than unprotected facilities (i.e. bike lane and shared traffic lanes) (Figure 4.3). Figure 4.4 displays the impact of temperature on bicycling demand. Demand is significantly affected by temperature. In the case of rainfall, it is observed that the more the rainfall, the lesser the average flow (Figure 4.5). Snowfall is another important weather characteristics during winter. Analysis suggests that the higher the snowfall, the lower the bicycling demand. 41 Figure 4.1. Distribution of Monthly Bicycling Demand Figure 4.2. Distribution of Daily Bicycling Demand for Different Seasons Figure 4.3. Distribution of Monthly Bicycling Demand for Protected and Unprotected Facilities 032506500975013000Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecBicycle volume per locationMonth0 2000 4000 6000 8000 10000 12000SatSunMonTueWedThuFriBicycle volume per locationDaySummer Fall Winter Spring0100200300400500020004000600080001000012000Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecBicycle volume/ location (unprotected facilities)Bicycle volume/location (protected facilities)MonthProtected Bicycle Facilities Unprotected Bicycle Facilities 42 Figure 4.4. Distribution of Bicycling Demand with Temperature Figure 4.5. Distribution of Bicycling Demand with Precipitation 4.3 Methodology This study develops a bicycling demand model using count data. The Poisson regression model is the most commonly used method for count data modeling (Tabeshian & Kattan, 2014). One of the major limitations of the Poisson regression model is the restrictive assumption that the mean is equal to the variance of the dependent variable. In this study, the mean is not equal to the variance of the dependent variable. In fact, the variance exceeds the mean value, referring to the over-dispersion characteristics of the data. These limitations are 0100200300400500600700-15 -10 -5 0 5 10 15 20 25 30Average bicycle volume/location over the yearDaily average temperature (0C)0501001502002503000 5 10 15 20 25Average bicycle volume/location over the yearPrecipitationDaily average rainfall (mm)Daily maximum snowfall (cm) 43 relaxed in the negative binomial modeling technique (Yasmin & Eluru, 2016). However, traditional negative binomial models discount unobserved heterogeneity among the count locations. To capture such heterogeneity, this study develops a latent segmentation-based negative binomial (LSNB) model for bicycling demand analysis for Kelowna. The LSNB model captures heterogeneity by allocating bicycle count locations into discrete latent segments. Assuming that Yj is the daily bicycling demand occurring over a period of time at bicycle count site j, which is allocated to segment c. The probability expression for the negative binomial model is as follows: (𝑃𝑗[𝑌𝑗|𝑐]) = ∏(𝑌𝑗+ ∆𝑐−1)∏(𝑌𝑗+1) ∏(∆𝑐−1) (11+ ∆𝑐𝜇𝑗𝑐)1∆𝑐 (1 − 11+ ∆𝑐𝜇𝑗𝑐 )𝑌𝑗 (4-1) Here, ∏(. ) = Gamma function, Δ𝑐 = Negative binomial dispersion parameter specific to segment c. μ𝑗𝑐 = Expected bicycling demand over a period of time at count location j which is allocated to segment c The Gamma distributed disturbance term of the negative binomial relaxes the variance assumption of the Poisson regression method. The expression of μ𝑗𝑐 takes the following form: 𝜇𝑗𝑐 = 𝑒𝜃𝑐𝑍𝑗 (4-2) Here, θ𝑐 = segment-specific co-efficient of the parameter (to be estimated), and 𝑍𝑗 = observed spatial and temporal attribute of the count location j (to be estimated) 44 Now, the allocation of count site j into segment c is performed by formulating a flexible segment allocation component within the LSNB framework. This segmentation is defined using temporal and spatial attributes of the count sites j. This segment allocation model takes the shape of the following standard logit model: 𝑃𝑗𝑐 = 𝑒𝛼𝑐 + 𝛽𝑐𝑋𝑐∑ 𝑒𝛼𝑐 + 𝛽𝑐𝑋𝑐𝐶𝑐=1 (4-3) Here, 𝑋𝑐 = observed attributes of the count locations j β𝑐 = segment membership co-efficient of the parameter (to be estimated), and α𝑐 = segment membership constant (to be estimated) The unconditional probability can be written as: 𝑃𝑗(𝑌𝑗) = ∑ (𝑃𝑗[𝑌𝑗|𝑐]) ∗ (𝑃𝑗𝑐)𝐶𝑐=1 (4-4) The model estimates segment-specific parameters θ𝑐 for c segments and segment membership parameters β𝑐 and α𝑐 for c-1 segments. These parameters are estimated using the maximum likelihood method. The log-likelihood function for the entire dataset can be expressed as: 𝐿𝐿 = ∑ 𝑙𝑜𝑔(∑ (𝑃𝑗[𝑌𝑗|𝑐]) ∗ (𝑃𝑗𝑐)𝐶𝑐=1𝑁𝑛=1 ) (4-5) 45 Here, N is the total number of observations. The goodness-of-fit of the model is evaluated using Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and adjusted pseudo rho-squared value (Fatmi & Habib, 2016b; Khan, Fatmi, & Habib, 2017). 4.4 Independent Variables Tested in This Study To understand the effects of spatial variation on bicycling demand, built environment factors including land use attributes, traffic characteristics, and accessibility measures of the count sites are tested in the model. Built environment attributes are generated at the spatial unit of 1 km road network-based buffer from each counting site using ArcGIS. Built environment and traffic characteristics variables include bicycle infrastructure (e.g. bike lane, cycle track, shared path), bike index (BI), road-connectivity index (RCI), and Annual Average Daily Traffic (AADT). Land use attributes include land use index (LUI) and dwelling density. Among the accessibility measures, distance to CBD, distance to nearest park, waterbody, and bus stop are generated using the network analyst tool of ArcGIS. Furthermore, weather-related variables such as temperature, rainfall, and snowfall are tested in the model. The developed model also utilizes trip characteristics which include week type (e.g. weekday), season (e.g. summer and fall), among others. 4.5 Results This study develops latent segmentation-based negative binomial (LSNB) model to estimate bicycling demand. The summary statistics of the variables retained in the final model is presented in Table 4.1. 46 Table 4.1. Descriptive Statistics of the Independent Variables Used in the LSNB Bicycling Demand Model Variables Description % or Mean Std. dev. Trip characteristics and land use attributes Weekday Dummy; If trips made on weekday =1, else = 0 68.62 0.464 Summer Dummy; If trips made in summer =1, otherwise = 0 24.19 0.428 Fall Dummy; If trips made in fall =1, else = 0 30.36 0.459 Land use index Land use index 0.877 0.124 Dwelling density Dwelling density within 1 km buffer 1647 745 Weather characteristics Average temperature (0C) Average temperature for the day (0C) 9.57 8.78 Average rainfall (mm) Average rainfall in the day (mm) 0.87 2.28 Maximum Snowfall (cm) Highest snowfall in the day (cm) 1.88 4.25 Bicycle facility Shared path Dummy; If bicycle facility is protected path that is shared with pedestrian = 1, otherwise = 0 42.65 0.494 Cycle track Dummy; If bicycle facility is protected cycle track =1, else = 0 41.35 0.492 Built environment and traffic characteristics Bike index Bike index 0.54 0.107 Road-connectivity index Road-connectivity index 6.47 0.650 AADT less than mean Dummy; If annual average daily traffic (AADT) is less than the mean of the sample =1, otherwise = 0 88.69 0.317 Accessibility Measures Distance to bus stop (km) Distance from counter location to the nearest bus stop 0.33 0.264 Distance to CBD (km) Distance from counter location to CBD 1.05 0.990 Distance to park (km) Distance from counter location to the nearest park 0.79 0.565 Distance to waterbody (km) Distance from counter location to the nearest water body such as lake, river 2.23 1.189 4.5.1 Goodness-of-fit Measures The number of segments of the LSNB model is determined using AIC and BIC measures. Results suggest that LSNB model with two latent segments fits the data best with lower AIC (34,521.20) and BIC (34,727.87) measures (Appendix A). Therefore, the LSNB model with two segments is considered as the final model. In addition to the LSNB model, this study develops latent segmentation-based Poisson regression (LSPR), negative binomial (NB), and Poisson regression (PR) models for comparison purposes. The detailed model results are 47 illustrated in Appendix A. The goodness-of-fit measures suggest that the LSNB model outperforms the LSPR, NB, and PR models in-terms of AIC, BIC, and adjusted pseudo rho-squared values (Table 4.2). For instance, adjusted pseudo rho-squared values for the LSNB model (0.996) outperform that of LSPR (0.992), NB (0.791), and PR (0.849) models. Therefore, the LSNB model with two segments is considered as the final model for further discussion. Table 4.2. Goodness-of-fit Measures of Bicycling Demand Models Attributes LSNB LSPR NB PR Log-likelihood function -17,226.58 -34,717.97 -18,222.07 -78,718.93 Adjusted pseudo rho-squared 0.996 0.992 0.791 0.849 AIC 34,521.20 69,499.9 36,470.20 157,463.90 BIC 34,727.87 69,694.5 36,549.18 157,542.89 Table 4.3. Latent Segment Allocation Results of the LSNB Bicycling Demand Model Attributes Segment 1 Segment 2 Coefficient (t-stat) Coefficient (t-stat) Constant 11.08*** (5.53) reference Weekday -0.77*** (-3.47) reference Summer -7.30*** (-9.87) reference Fall -1.57*** (-6.73) reference Land use index -3.58** (-2.18) reference Dwelling density -0.29 *** (-8.03) reference *** significance at 1% level, ** significance at 5% level 4.5.2 Latent Segment Allocation Component Results The segment allocation component for the model retains the following variables: day of the week, season, land use index, and dwelling density (Table 4.3). The negative signs for the temporal variables representing summer, fall, and weekdays, suggest that segment 1 has a higher likelihood to include bicycle trips made in weekends and winter. The negative sign for the spatial factors representing land use index and dwelling density reveals that segment 1 48 has a higher probability to include bicycle trips made in lower mixed land use areas with low dwelling density. Therefore, segment 1 can be identified to include weekend, winter trips made in suburban areas. On the other hand, segment 2 can be identified to include weekday, summer and fall trips made in urban areas. Table 4.4. Parameter Estimation Results of the LSNB Bicycling Demand Model Segment 1 Segment 2 Attributes Coefficient (t-stat) Coefficient (t-stat) Constant -26.0*** (-37.87) 26.8*** (13.25) Dispersion parameter 5.02*** (26.72) 9.36*** (19.33) Weather characteristics Average temperature (0C) 0.10*** (46.60) 0.05*** (26.55) Average rainfall (mm) -0.11*** (-30.74) -0.04*** (-11.84) Maximum snowfall (cm) -0.08*** (-27.55) -0.09*** (-16.08) Bicycle facility Shared path -12.0*** (-38.20) 13.7*** (13.56) Cycle track -6.25*** (-18.38) 11.6*** (14.60) Built environment and traffic characteristics Bike index 41.8*** (39.01) -31.8*** (-9.69) Road-connectivity index 0.59*** (6.95) -0.51*** (-7.91) AADT less than mean/1000 3.39*** (25.16) -4.34*** (-12.60) Accessibility Measures Distance to bus stop (km) 19.1*** (41.86) -16.9*** (-11.47) Distance to CBD (km) 2.03*** (52.77) -1.19*** (-8.38) Distance to park * cycle track (km) 3.90*** (26.21) -4.94*** (-14.33) Distance to waterbody * cycle track (km) -0.82*** (-10.49) -0.43*** (-7.79) *** significance at 1% level, ** significance at 5% level, * significance at 10% level 4.5.3 Parameter Estimation Results The LSNB model confirms the effects of weather, bicycle facility, built environment, traffic, and accessibility attributes on bicycling demand (Table 4.4). All the variables are statistically significant at least at the 1% level. Among the weather characteristics, average daily temperature shows a positive relationship; whereas, average daily rainfall and maximum daily snowfall show negative relationships. This implies that good weather (i.e. higher temperature, 49 and lower rainfall and snow) increases bicycling demand. In the case of bicycle facilities, protected bicycle infrastructures such as cycle track and shared path show heterogeneity across the segments. These variables positively influence the demand in segment 2, which includes weekday trips in urban areas. This implies that protected facilities in urban areas increase bicycling demand. On the other hand, the same variables show a negative relationship in segment 1. This might be explained by the characteristics of segment 1, which includes a higher share of weekend trips in suburban areas. Among the built environment and traffic characteristics, bike index and road-connectivity index show a positive relationship in segment 1, which includes bicycle facilities in suburban areas. This implies that bicycling demand might increase in suburban areas, given that the neighborhoods are bicycle friendly with higher bike index and road-connectivity index. Variable representing lower AADT shows significant heterogeneity across the segments. Lower AADT reveals a positive relationship in segment 1. This increase in bicycling demand might be attributed by the perceived safety associated with lower AADT in relatively free-flowing suburban facilities. On the other hand, the same variable shows a negative relationship in segment 2, which includes urban facilities. This increase in bicycling demand with high AADT might be explained by the congested slow-moving roads in the urban areas during weekdays. In the case of accessibility measures, variables representing the distance to the nearest bus stop and distance to CBD reveal a negative relationship in segment 2. This reveals that facilities closer to bus stops and CBD attract higher bicycling demand. This increased bicycling demand might be attributed by the higher accessibility coupled with multi-modal trip-making opportunities in urban areas. In contrast, a positive relationship is found in segment 1. Variable 50 representing the distance to the nearest water body interacted with cycle track reveals a negative relationship, implying that bicycling demand increases in facilities closer to water bodies, given that it is a protected facility. Variable representing the distance to the nearest park interacted with cycle track reveals a negative relationship in segment 2, implying that bicycling demand increases in facilities closer to park in urban areas, given that it is a protected facility. Table 4.5. Predicted and Observed Average Daily Bicycling Demand Bicycle Facility Facility Type Predicted Average Daily Demand per Bicycle Facility Observed Average Daily Demand per Bicycle Facility Off-road cycle Track Separated from existing road (protected) 271 273 Off-road shared path Separated from existing road (protected) 316 314 On-road bike lane Not separated from exiting road (partially protected) 62 65 On-road Not separated from exiting road (fully unprotected) 31 27 4.5.4 Predicted Average Daily Bicycling Demand LSNB model is utilized to predict the average daily bicycling demand by facility types. The predicted average daily bicycling demand is 3,076. This prediction is reasonably satisfactory as the observed average bicycle trips per day is 3,067. In the case of facility types, the predicted average daily demand for off-road protected facilities such as cycle tracks and shared paths are 271 and 316 trips/day/facility type respectively (Table 4.5). Interestingly, shared paths are predicted to accommodate a higher bicycle volume than cycle tracks. Observed average daily demand in cycle track and shared path shows similar trend (Table 4.5). On-road bicycle facilities such as bike lanes and lanes shared with vehicles are predicted to attract a lower volume of bicycle trips than the protected facilities. Lanes shared with 51 vehicles are predicted to experience the lowest bicycling demand (31 trips/day/facility). Estimations from the developed model are satisfactory because the predicted average daily bicycling demand for all the bicycle facilities does not vary significantly with the observed average daily bicycling demand. 4.6 Concluding Remarks This chapter investigates the combined effects of spatial and temporal factors on bicycling demand for Kelowna, Canada. The demand model adopts a latent segmentation-based negative binomial (LSNB) modeling technique utilizing daily bicycle count data for a year. One of the key features of this study is to capture unobserved heterogeneity among the bicycle facilities. Another key aspect of this research is to test the impacts of weather, bicycle facility, built environment, traffic, and accessibility characteristics on bicycling demand. The LSNB model outperforms the LSPR, NB, and PR models in terms of goodness-of-fit measures. Average daily bicycle demand by facility types predicted by the LSNB model suggests a satisfactory fit to the data. The LSNB model is estimated for two segments based on the AIC and BIC measures. The segment allocation model is estimated using the trip characteristics (e.g. day of week, season), and land use attributes (e.g. land use index, dwelling density). In the case of the segment allocation results, segment 1 includes weekend, winter trips made in suburban areas; whereas, segment 2 includes weekday, summer and fall trips made in urban areas. The model confirms the effects of spatial and temporal attributes. It is observed that higher temperature, lower rainfall, lower snowfall, and facilities closer to water bodies coupled with cycle tracks reveal a higher propensity to increase bicycling demand. The model also confirms significant heterogeneity across the segments. For instance, higher road-connectivity index and higher bike index reveal a higher likelihood to 52 increase bicycle volume in the suburban areas. In contrast, bicycling demand is found to be higher for lower road-connectivity index and bike index in segment 2, which includes facilities in urban areas. Similar heterogeneity is confirmed for AADT. Accessibility measures such as distance to the nearest bus stops and parks reveal heterogeneity across the segments. Model results are validated by predicting the average daily bicycling demand by facility types. The prediction results suggest that the LSNB model provides a satisfactory estimation of bicycle volume. The overall findings reveal that higher temperature, and lower rainfall and snowfall significantly increase bicycle volume. Bike index and road-connectivity index are identified as significant target variables to increase bicycling demand in the suburban areas of Kelowna. The results also reveal that shared paths are more likely to increase bicycling demand in the urban areas of Kelowna. Moreover, lower AADT is associated with increased bicycling demand in the suburban areas. In summary, the findings of this study provide important insights to increase bicycling demand. Particularly, this chapter reveals the combined influence of a city’s weather, built environment, and accessibility attributes on the use of bicycle infrastructures. Other cities can adopt the proposed LSNB model to better understand their bicycling demand and develop effective bicycling supportive policies. The developed model is expected to assist transportation planners and engineers to target critical spatial and temporal factors for inducing more bicycle trips. Finally, the heterogeneity captured in this chapter needs to be accommodated within the policies to effectively increase bicycle volume. 53 Chapter 5: Bicyclists’ Speed Choice Modeling2 5.1 Introduction This chapter develops bicyclists’ speed choice model by using dockless bikeshare service (DBS) users’ data from Kelowna. This research contributes to the following three dimensions: i) This study develops a machine learning (ML) algorithm to improve the trip identification procedure. ii) This research develops a latent segmentation-based linear regression (LSLR) model. A flexible segment allocation model is embedded within the LSLR framework to capture latent heterogeneity. This segment allocation model allocates the trips into different latent segments with a discrete distribution. iii) This research tests the impact of weather characteristics (i.e. temperature, precipitation, snowfall, and wind-chill index, among others), road infrastructure and built environment attributes (i.e. bike index, shared path, bike lane, mean percent rise of elevation, AADT, among others), and accessibility measures (i.e. distance to intersection, CBD, park, and haven, among others) on bicyclists’ speed. The outline of this chapter is as follows: section 5.2 shows an exploratory analysis of GPS data. Then, section 5.3 discusses the GPS data processing and LSLR modeling technique. Section 5.4 presents the independent variables tested in the LSLR model. Section 5.5 presents the results that include the GPS data processing results, goodness-of-fit measures of the model, latent segment allocation component results, parameter estimation results, and model validation results. Finally, section 5.6 summarizes the findings of this chapter. 2 This chapter is derived from “Orvin, M., and Fatmi, M. (2020). Developing Speed Choice Model Using GPS Data of Dockless Bikeshare Service Users. Journal of Transport and Land Use.” (under review) 54 5.2 Exploratory Analysis of GPS Data This research develops the LSLR model using GPS data. Raw GPS data included information regarding the trip ID, timestamp, and coordinates of trip trajectories. This data yielded a total of more than 0.6 million trip points corresponding to 19,601 unique trips. Approximately, 57% of these trips were made on weekdays. About 70% of the trips were completed during off-peak hours. The share of trips during the morning peak (7 AM to 9 AM) and evening peak (4 pm to 6 pm) were around 14% and 16% respectively. In the case of seasonal share of the trips, it was observed that majority of the trips (63%) were made in summer. About 35% of trips were made during fall. Only 2% of trips were completed in November, which is the starting of winter in Kelowna. It is to be mentioned that Kelowna often experiences snow during winter. Furthermore, around 48%, 28%, and 18% of GPS trip points were recorded in the arterial, local, and collector roads respectively. Further analysis suggests that around 81% of trip points were trailed in bicycle infrastructures and sidewalks. Figure 5.1. Distribution of Speed for Spatial and Temporal Characteristics 0.0010.0020.0030.00Speed (km/hr)85th percentile speed (km/hr) Average speed (km/hr) 55 Figure 5.2. Variation of Speed for Built Environment Attributes Instantaneous speed is determined using the location and timestamp information from the GPS trip point records. The average speed is found to be 10.34 km/hr. The 50th, 85th, and 99th percentile of speed is found as 7.23 km/hr, 20.81 km/hr, and 45.22 km/hr respectively. Figure 5.1 illustrates the distribution of speed for several trip characteristics (e.g. week, peak hour, and season), and road network infrastructure characteristics such as bicycle infrastructure and road hierarchy (e.g. local, collector, and arterial road). For instance, average speed is found relatively higher during the evening peak hour (10.38 km/hr) compared to the morning peak hour (9.33 km/hr). Figure 5.1 also suggests that for 85% of cases, the speed during the weekday is less than 24.32 km/hr. On the other hand, in the case of weekend days, 85th percentile speed is 20.41 km/hr. It is also observed that the average speed is higher for cycle track users (11.92 km/hr) compared to the bicyclists using shared path (9.58 km/hr). Figure 5.2 shows the variation of speed for several built environment attributes including AADT, 56 distance to the intersection, mean percent rise of elevation, and the ratio of the length of bicycle infrastructure to the length of road. For instance, mean speed is relatively higher for AADT less than 5,000 compared to AADT 5,000 to 14,000. Results also show that mean speed decreases closer to the road intersection and with the increase of elevation. Furthermore, mean speed is found to be increased with the ratio of the length of bicycle facility to the length of road. 5.3 Methodology 5.3.1 Machine Learning Algorithm for GPS Data Cleaning Built-in GPS device in the bicycles of the DBS fleet provides trip information such as location and timestamp of the trip points. Raw GPS data needs to be cleaned, since it includes noise points. Noise points do not represent the actual position of the trips. This chapter adopts a machine learning (ML) algorithm to tackle the challenges associated with the identification of actual trip points. Density-based Spatial Clustering of Applications with Noise (DBSCAN) is an unsupervised machine learning algorithm which splits data into meaningful sub-groups termed as ‘clusters’. Density based clustering algorithm are based on the hypothesis that clusters are dense areas in space segregated by regions of lower density. Thus DBSCAN is useful to identify pattern in data which is challenging to find using a heuristics method (Salton, 2017). Advantages of DBSCAN are its ability to detect the dense region by classifying data points that are closer to each other based on the distance measurement and to identify the outliers effectively. Two critical parameters for DBSCAN are: i) radius of the neighborhood region (r), and ii) minimum number of points (n) that should be retained in that neighborhood (GeeksforGeeks, 2019a). In the DBSCAN algorithm, there are three types of data points (Ester, Kriegel, Sander, 57 & Xu, 1996). A point is termed as ‘core point’ if it has more than n number of points within the r radius of the neighborhood. ‘Border point’ is the point that has fewer than n number of points within the r radius, however it is in the neighborhood of a ‘core point’. A point neither a ‘core point’ nor a ‘border point’ is identified as a ‘noise’ or an ‘outlier’ (Figure 5.3). The algorithm estimates the distance between every point and all other points. Core points and border points can form cluster of any size and shape. Figure 5.3. Three Types of Points in DBSCAN Algorithm Considering n = 4 The accuracy of DBSCAN method depends on the pre-set value of r and n (Ester et al., 1996). The assumption behind choosing r in this research is that for points in a cluster, their kth nearest neighbors are at approximately at the same distance. On the other hand, noise points have the kth nearest neighbor at a farther distance. Therefore, sorted distance of every point to its kth nearest neighbor is plotted. The point of maximum curvature in the plot is considered as the optimum value for r (Rahmah & Sitanggang, 2016). The programming code for the DBSCAN algorithm is developed in Python. This chapter evaluated the goodness of clusters by determining the Davies-Bouldin Index (DBI) (GeeksforGeeks, 2019b). The lower the DBI value the better is the clustering. 58 5.3.2 Speed Choice Modeling Approach This chapter develops a speed choice model for bicyclists using the GPS data. This research replaces the traditional ordinary least square (OLS) regression model to a latent segmentation-based linear regression (LSLR) model. The dependent variable used in the model is the bicycle speed for each trip point i determined from the GPS records. Equation 5-1 illustrates the linear regression framework of speed choice model for trip point i allocated to segment s. 𝑉𝑖𝑠 = (𝛽𝑠 ∗ 𝑋𝑖) + ∈𝑖𝑠 (5-1) Here, 𝑉𝑖𝑠 = bicycle speed for trip point i assigned to segment s, βs = segment specific coefficient of the model parameter (to be estimated), Xi = road infrastructure and built environment attribute of trip point i and ɛ is = random error term (assumed normally distributed to trip point i and segment s). Let, s = (1, 2, 3,...,S) is the homogenous segment defined by trip characteristics and land use attributes of the trip point i. The allocation of speed for trip point i into segment s is executed by formulating a flexible segment allocation component within the LSLR framework. This segment allocation model appears as the following standard logit model: 𝑃𝑖𝑠 = 𝑒𝛼𝑠 + 𝜙𝑠𝑋𝑠 ∑ 𝑒𝛼𝑠 + 𝜙𝑠𝑋𝑠 𝑆𝑠=1 (5-2) Here, 𝑋𝑠 = observed trip characteristic and land use attribute of the trip point i, α𝑠 = segment-membership constant (to be estimated), and 59 ϕ𝑠 = segment-membership coefficient of the parameter (to be estimated) The unconditional probability of speed for trip point i can be expressed as: 𝑓(𝑉𝑖𝑠) = ∑ (𝑓(𝑉𝑖𝑠 | 𝛽𝑠)) ∗ (𝑃𝑖𝑠)𝑆𝑠=1 (5-3) The model estimates segment specific parameter β𝑠 for s segments and segment membership parameter ϕ𝑠 and α𝑠 for s-1 segments. These parameters are evaluated using the maximum log-likelihood method. The log-likelihood function for the entire dataset can be stated as: 𝐿𝐿 = ∑ 𝑙𝑜𝑔(∑ (𝑓(𝑉𝑖 | 𝛽𝑠)) ∗ (𝑃𝑖𝑠𝑆𝑠=1𝑁𝑛=1 )) (5-4) Here, N is the total number of observations. The goodness-of-fit measure of the model is evaluated using the Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and adjusted pseudo rho-squared value. The number of segments for the model is decided observing the lowest BIC value. 5.4 Independent Variables Tested in This Study This chapter examines the effects of transportation infrastructure and land use attributes, neighborhood characteristics, accessibility measures, and weather attributes on bicyclists’ speed. These variables are generated at the spatial resolution of 250 m X 250 m grid. The trip points are generally closely spaced. Therefore, 250 m x 250 m grid is used as the spatial unit 60 to capture greater variation among the observations. Transportation infrastructure associated variables include road network, road hierarchy (e.g. local, arterial, and collector road), active transportation network (i.e. sidewalk, cycle track, shared path, and bike lane), mean percent rise of elevation, bike index (BI), designated bicycle park areas (i.e. haven locations), AADT, number of bus stops, among others. Zonal statistics is used to determine the mean percent rise of elevation for each grid from the percent rise raster. Land use and neighborhood attributes include land use index (LUI), percentage of different land use types (e.g. commercial, residential), and population density, among others. In the case of accessibility measures, road network-based distance from trip points to several facilities and activity locations (e.g. CBD, intersection, nearest haven, and nearest park) are generated using the network analyst tool in ArcGIS. To test the impact of weather conditions, temperature, wind speed, relative humidity, and rainfall data, among others are utilized. In addition, wind-chill index (WI) is calculated using the wind speed and temperature (National Weather Office, 2019). In the case of trip characteristics, time of trip (e.g. peak hour), week type (e.g. weekend), and season (e.g. summer), among others are tested. 5.5 Results 5.5.1 GPS Data Processing Result 5.5.1.1 Preliminary Filtering and Application of DBSCAN Algorithm The raw GPS data contains 19,601 unique trips including 666,155 trip points. Initially, 34,901 trip points outside the study area (i.e. Kelowna boundary) are discarded. Besides, duplicate timestamps are excluded. Trip point is also deleted if there is a missing data either for longitude and latitude, or timestamp. In addition, negative time gaps between trip points are identified and eliminated. Trips having less than 10 trip records are also deleted. Furthermore, trips are removed if the total distance and travel time of a trip is less than 50 m and 1 min 61 respectively. Bicycle speed typically ranges from 5 to 40 km/hr (Kassim, Pascoe, Ismail, & El Halim, 2012). Clarry et al. (2019) developed speed choice model where maximum bicycle speed retained was 52 km/hr. Therefore, in this research, trip points exceeding speed of 48 km/hr are removed. Finally, after the preliminary filtering, a total of 464,308 trip points remain. Post preliminary processing, this chapter implements the DBSCAN algorithm to identify the outliers in the GPS trip trajectory. The radius of the neighborhood region (r) is determined by observing the point of maximum curvature in the sorted distance of every point to its kth nearest neighbor plot. With the optimum radius, number of points generating the lower DBI value is taken as the minimum number of points (n). For example, for r = 0.01, and n = 4 if DBI is found lower (1.285) compared to the DBI (1.813) for r = 0.01, and n = 5, then r = 0.01 and n = 4 is considered due to yielding a lower DBI. Trip points remaining after applying the DBSCAN algorithm is 427,109 from 11,427 unique trips, which is approximately 64% of the raw trip points. This indicates that about 36% trip points are discarded in the process of data cleaning. Figure 5.4 and Figure 5.5 show the implementation results of the DBSCAN algorithm. For comparison purposes, a rule based (RB) method is applied as well. The RB method used in this study follows similar rules developed by Bohte & Maat (2009) to eliminate erroneous GPS track points. The preliminary filtering follows a similar procedure as discussed above. Trip point is discarded if the distance between the current trip point and previous trip point is less than 10 m. In this process approximately 67% trip points are eliminated, which significnatly higher than that of the DBSCAN method. A statistical comparison of DBSCAN method with RB method is performed using Welch’s t-test which is suitable for unequal sample and variances. This test compares the distribution of trip attributes including instantaneous 62 distance, travel time, and speed. For all the attributes, the t-stat is found to be higher for the DBSCAN method. For instance, t-stat for the distribution of speed yields a value of 5.99 for DBSCAN method; whereas the value is 4.94 for the RB method. Therefore, the DBSCAN method outperforms the RB method in-terms of goodness-of-fit measures. Figure 5.4. Sample Results of DBSCAN Algorithm Implementation Figure 5.5. Trip Points After Applying DBSCAN Algorithm 63 5.5.1.2 Map Matching GPS data often deviates from the road network. These points need to be map matched within the road network. Post the application of DBSCAN algorithm, a number of trip points are not aligned with the road network, which posits challenges to detect the actual navigated bicycle path. Therefore, a map matching technique is adopted to align the GPS data with the road network. For the purpose of map matching, this chapter uses a complete road network including sidewalks, on-street bicycle facilities (i.e. bike lanes), and off-street bicycle facilities (i.e. cycle track and shared path). It is important to utilize such complete network as bicyclists take path that include roads, sidewalks, bike lanes, cycle tracks, and shared paths. This study follows a geometric map matching algorithm. This method matches the geometry of road network segments utilizing the closest section to the considered point (Pereira & Costa, 2009). Figure 5.6 and Figure 5.7 illustrate the trip points before and after applying the map matching algorithm. Figure 5.6. Trip Points Before Applying Map Matching Algorithm 64 Figure 5.7. Trip Points After Applying Map Matching Algorithm 5.5.2 Model Results This chapter develops a latent segmentation-based linear regression (LSLR) model to examine bicyclists’ speed choice behavior. Out of 427,109 trip points, 75% trip points are randomly selected for developing the model. The rest 25% trip points are used to validate the model. A summary statistic of the variables retained in the final model is presented in Table 5.1. 5.5.2.1 Goodness-of-fit Measures The number of segments for the LSLR model is determined based on the Bayesian Information Criteria (BIC) measures. The results suggest a BIC value of 1,938,279.19 for two latent segments (Table 5.2). On the other hand, BIC value for three latent segments is 1,964,480.67. The model with a lower BIC measure fits the data best. Therefore, the two-segment LSLR model is selected as the final model. 65 Table 5.1. Descriptive Statistics of the Independent Variables Used in the LSLR Bicyclists’ Speed Choice Model Variables Description % or Mean Std. dev. Trip characteristics Weekend Dummy; If trip point is on weekend =1, else = 0 37.26 0.483 Summer Dummy; If trip point is on summer =1, else = 0 63.97 0.480 Mid-day Dummy; If trip point is on mid-day (11 AM- 2 PM) =1, otherwise = 0 2.59 0.155 Morning peak Dummy; If trip point is on morning peak hour (7 AM- 9 AM) =1, otherwise = 0 8.12 0.273 Evening peak Dummy; If trip point is on evening peak hour (4 PM- 6 PM) =1, otherwise = 0 11.14 0.315 Weather characteristics Maximum temperature (0C) Maximum temperature for the day (0C) 26.25 7.214 Wind-chill index Maximum wind-chill index for the day 29.03 7.820 Land use and neighborhood characteristics Land use index Land use index within 250 m x 250 m grid 0.54 0.213 Percentage of residential area Percentage of residential area within 250 m x 250 m grid 31.00 32.75 Percentage of commercial area Percentage of commercial area within 250 m x 250 m grid 27.07 29.45 Population density Population density within 250 m x 250 m grid/100 23.19 15.04 Road infrastructure and built environment attributes Local road Dummy; If road hierarchy type is local =1, else = 0 27.84 0.448 Arterial road Dummy; If road hierarchy type is arterial =1, else = 0 48.21 0.499 Major collector road Dummy; If road hierarchy type is major collector =1, else = 0 10.48 0.306 Mean percentage rise of elevation Mean percentage rise of elevation within 250 m x 250 m grid 9.78 24.21 Bike index Bike index within 250 m x 250 m grid 0.41 0.086 Protected cycle track facility Dummy; If protected cycle track facility is present =1, else = 0 2.70 0.169 Bike lane length Length of bike lane in km within 250 m x 250 m grid 0.22 0.229 Shared path length Length of shared use pathway in km within 250 m x 250 m grid 0.06 0.103 AADT Annual Average Daily Traffic (AADT) 10,410 14,870 Accessibility features Distance to intersection Distance to the nearest intersection in km 0.05 0.076 Distance to CBD Distance to CBD in km 2.04 2.141 Distance to haven location Distance to the nearest haven location in km 0.54 1.502 Distance to park < 500 m Dummy; If the distance to the nearest park is less than 500 m = 1, otherwise = 0 42.18 0.493 For comparison purposes, the LSLR model is compared with the ordinary least square regression (OLSR), and random parameter linear regression (RPLR) models. The detailed 66 model results are presented in Appendix B. The goodness-of-fit measures suggest that the LSLR model outperforms the OLSR and RPLR models (Table 5.2). For instance, the AIC value for the two-segment LSLR model (1,937,852.10) is lower compared to OLSR (1,939,291.56) and RPLR (2,121,931.50) model. Therefore, the LSLR model is considered for further result discussion. The results of the LSLR model are reported in Table 5.3 and Table 5.4. It is found that the majority of the parameters show a statistical significance at the 1% significance level. Table 5.2. Goodness-of-fit Measures of the Bicyclists’ Speed Choice Models Model Log-likelihood function Adjusted pseudo rho-squared AIC BIC OLS regression (OLSR) -969,632.78 0.014 1,939,291.56 1,939,430.36 Random parameter linear regression (RPLR) -1,060,940.72 0.001 2,121,931.50 2,122,198.37 2-latent segment linear regression (LSLR) -968,886.05 0.026 1,937,852.10 1,938,279.19 3-latent segment linear regression (LSLR) -981,828.33 0.012 1,963,786.74 1,964,480.67 5.5.2.2 Latent Segment Allocation Component Results Latent segment allocation model is estimated using trip characteristics, and land use attributes (Table 5.3). Segment 2 is considered as the reference segment. In the case of trip characteristics, trips during the morning peak periods show a negative relationship for segment 1. This suggests that morning peak trips are less likely to be included in segment 1. Likewise, trips during evening peak and midday have a lower likelihood to be assigned to segment 1. However, variables representing weekend trips and summer trips show a positive relationship for segment 1. Therefore, segment 1 has a higher likelihood to include trips made during weekend, off-peak hour, and summer season. In contrast, segment 2 is more likely to include peak hour weekday trips made in fall and winter seasons. 67 Among the land use attributes, segment 1 shows higher probability to include trips made in areas with a lower land use index, higher percentage of residential areas, lower percentage of commercial areas, and lower population density. On the other hand, segment 2 has higher likelihood to assign higher trips made in areas with a higher land use index, higher percentage of commercial areas, and higher population density. Overall, segment 1 can be identified to include recreational suburban trips made in summer; whereas urban peak hour weekday trips commuted in fall and winter are more likely to be allocated in segment 2. Table 5.3. Latent Segment Allocation Results of the LSLR Bicyclists’ Speed Choice Model Attributes Segment 1 Segment 2 coefficient t-stat coefficient t-stat Constant 1.36557*** 58.34 reference Trip characteristics Morning peak -0.23188*** -13.65 reference Evening peak -0.11985*** -7.98 reference Mid-day -0.08044*** -2.66 reference Weekend 0.05474*** 5.41 reference Summer 0.04165*** 4.14 reference Land use and neighborhood characteristics Land use index -0.11539*** -4.26 reference Percentage of residential area 0.00379*** 16.97 reference Percentage of commercial area -0.00023 -1.14 reference Population density -0.00092*** -2.64 reference *** significance at 1% level, ** significance at 5% level, * significance at 10% level 5.5.2.3 Parameter Estimation Results The model confirms the effects of weather characteristics, road infrastructure and built environment attributes, and accessibility measures (Table 5.4). In the case of road infrastructure and built environment attributes, mean percent rise of elevation in the local road show a negative relationship. This implies that speed is more likely to decrease with the increase in mean percentage rise of elevation of roads. The length of shared path in local roads has a higher likelihood to increase speed. A positive relationship is found for the variable 68 representing bike index interacted with local road. The higher the bike index in local road, the higher the likelihood for increased bicycle speed. This is expected, as the bicyclists might prefer to travel faster in bike friendly local areas with a higher bike index. Interestingly, heterogeneity across the segments is confirmed while bike index is interacted with arterial road. Bike index in arterial road positively influences bicycle speed in segment 2, which includes weekday commute trips made in urban areas. However, a negative relationship is found for segment 1, which includes recreational suburban trips. This implies that speed might be lower for recreational trips made in the suburban arterials with a higher bike index. Similar heterogeneity is captured for the variables representing the length of protected cycle track facility and bike lane interacted with major collector road. For example, cycle track facility shows a positive sign in segment 2, which includes weekday peak hour urban trips. On the other hand, cycle track facility influences negatively in segment 1. In the case of AADT, heterogeneity is confirmed across the segments. AADT shows a negative relationship in segment 2. This reveals that speed is more likely to be lower in roads with a higher traffic volume in urban areas, which might be attributed by the perceived safety associated with higher AADT in congested urban roads during peak hours. However, a positive relationship is found in segment 1, which includes recreational trips in suburban areas. This higher speed might be explained by the comparative free-flowing road facilities in suburban areas during weekends. Among the weather characteristics, the maximum temperature and wind-chill index show heterogeneity. Maximum temperature shows a negative relationship in segment 1, which includes trips made in summer. This suggests that there is a higher probability of reduced speed during higher temperature in the summer season. On the other hand, maximum temperature shows a negative relationship for fall and winter trips in segment 2. In the case 69 of variable representing wind-chill index, negative relationship is observed in segment 2. This implies that speed is more likely to be reduced for fall and winter trips with the increase of wind-chill index. Table 5.4. Parameter Estimation Results of the LSLR Bicyclists’ Speed Choice Model Attributes Segment 1 Segment 2 coefficient t-stat coefficient t-stat Constant 8.22645*** 114.38 0.10663*** 21.15 Weather characteristics Maximum temperature (0C) -0.05524*** -15.47 0.00309*** 12.04 Wind-chill index 0.01956*** 5.80 -0.00104*** -4.49 Road infrastructure and built environment attributes Local road * mean percentage rise of elevation -0.01153*** -14.50 -0.00011** -1.98 Arterial road*bike index -0.20411** -2.48 0.05420*** 9.02 Local road*bike index 0.79465*** 6.63 0.03008*** 3.43 Protected cycle track facility -0.20248** -2.45 7.09715*** 36.84 Major collector road*bike lane length -0.48818*** -3.21 0.02809*** 2.77 Local road*shared path length 0.78248*** 2.59 0.05423*** 2.58 AADT 0.00011 1.05 -0.00001** -2.29 Accessibility features Distance to intersection 0.42918** 2.31 0.06903*** 5.35 Distance to CBD 0.28951*** 24.75 0.00116 1.45 Distance to haven location 0.25228*** 16.41 -0.00305*** -2.84 Distance to park < 500 m -0.31238*** -10.51 -0.00670*** -3.33 *** significance at 1% level, ** significance at 5% level, * significance at 10% level In the case of the accessibility features, variable representing distance from road intersection depicts a positive association with speed. This indicates that speed declines as bicyclists approaches closer to the road intersection. The presence of signals and stop signs at the intersections act as an impedance of speed. Similarly, a positive association is found for the variable representing distance from the CBD. This refers that bicycle speed tends to decrease in areas closer to CBD. CBD and its surrounding areas have a higher volume of bicycle traffic, which might attribute to the speed reduction near the CBD. Furthermore, the variable 70 representing distance to park areas less than 500 m shows a negative sign. This suggests that there is higher probability of reduced speed in areas closer to the parks. Table 5.5. Predictive Performance Evaluation for Bicyclists’ Speed Choice Models Model Goodness-of-fit measures MAD MSPE OLS regression (OLSR) 5.15 44.34 Random parameter linear regression (RPLR) 5.11 43.90 Latent segmentation-based linear regression (LSLR) 3.98 33.56 𝑀𝐴𝐷 = ∑ |𝑍𝑝 − 𝑍𝑜|𝑁𝑛=1𝑁, 𝑀𝑆𝑃𝐸 = ∑ (𝑍𝑝 − 𝑍𝑜)2𝑁𝑛=1𝑁 Here, 𝑍𝑝 = predicted value, 𝑍𝑜 = observed value, and N = total observation 5.5.2.4 Model Validation Results To evaluate the performance of the LSLR model results, a validation is performed using a hold-out sample of 106,777 trip points (25% of total dataset). The predictive performance is compared with the OLSR and RPLR models (Table 5.5). Goodness-of-fit measures include Mean Absolute Deviation (MAD) and Mean Squared Prediction Error (MSPE) (Yasmin & Eluru, 2016). Validation results suggests a higher prediction accuracy for the LSLR model compared to the OLSR and RPLR models. For example, MAD for LSLR model is 3.98, which is smaller than that of RPLR (i.e. 5.11) and OLSR (5.15) models (Table 5.5). Similarly, MSPE value for LSLR is also lower compared to the OLSR and RPLR models. Therefore, it can be concluded that the LSLR model offers reasonably satisfactory accuracy for predicting the speed of bicyclist. Figure 5.8 illustrates the predicted speeds of the validation sample. The figure depicts some of the aspects indicated by the developed model. For example, bicycle speed is found to be in in the lower range (8-12 km/hr) in the areas around the CBD. Higher bicyclists volume, higher number of activity points, and presence of higher number of intersections, among others, are the some of the factors that might be responsible for this 71 lower speed. Speed is found relatively higher in roads with the bicycle infrastructures (including on-street and off-street cycle facilities) and relatively level terrains. Figure 5.8. Predicted Speed Using the LSLR Model for Validation Sample 5.6 Concluding Remarks This chapter presents the results of a bicyclists’ speed choice model using GPS data. The contributions of this chapter are two-fold: i) adopting an advanced unsupervised machine learning (ML) method to identify trips in the GPS data, and ii) developing a latent segmentation-based linear regression (LSLR) model to capture unobserved heterogeneity. For the trip identification purposes, this study utilizes a Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Subsequently, a geometric map matching algorithm is utilized to map-match the filtered GPS data to the road network. Using the filtered GPS data, the LSLR speed choice model is developed. A flexible segment allocation model 72 is formulated within the LSLR framework to capture unobserved heterogeneity. One of the key features of this research is to extensively test the influence of weather characteristics, road infrastructure and built environment attributes, accessibility measures, and trip characteristics. The results suggest that the proposed ML algorithm outperform the conventional rule based (RB) method with a higher accuracy, based on the Welch’s t-test results. Model results suggest that LSLR model outperforms OLSR and RPLR models in terms of goodness-of-fit measures. The LSLR model is estimated for two segments based on the AIC and BIC measures. The segment allocation model is estimated using the trip characteristics (e.g. time of day, day of week, season), and land use and neighborhood attributes (e.g. land use index, population density). The results of the segment allocation component of the LSLR model suggest that segment 1 can be identified to include weekend suburban trips made in off-peak hours during summer season. On the other hand, urban peak hour weekday trips made in fall and winter seasons have a higher likelihood to be allocated in segment 2. The parameter estimation results confirm the impact of transportation infrastructure, weather characteristics, and accessibility attributes on bicycle speed. Bicyclists are found to travel faster in streets with fewer lanes, lower volume of vehicular traffic, level terrain, bicycle facilities, and good weather conditions. For example, in the case of transportation infrastructure, mean percent rise of elevation in local road is more likely to decrease bicycle speed. Interestingly, results suggest that improvement of bike index in local roads might increase bicycle speed. Besides, length of shared path reveals a higher likelihood for increased speed. In the case of accessibility measures, bicycle speed tends to decrease closer to the road intersections and CBD. The model confirms significant heterogeneity across the two segments. For example, wind-chill index reveals a negative relationship in segment 2. This indicates that higher value of wind- 73 chill index might decrease bicycle speed in the fall and winter seasons. On the other hand, a negative sign is observed in segment 1, which includes summer trips. In the case of variable representing maximum temperature, positive sign is observed for segment 2. This signifies that bicycle speed is more likely to increase with an increase in temperature during the fall and winter seasons. In contrast, negative sign in segment 1 indicates that higher temperature negatively influences bicycle speed during the summer season. Heterogeneity is also confirmed for the road infrastructure and built environment attributes. For instance, variables representing bike index and protected cycle facility shows positive relationship in segment 2 that includes weekday peak hour urban trips; whereas, a negative relationship is found for segment 1. Interestingly, a higher AADT negatively influences bicycle speed for commute urban trips in segment 2. In contrast, positive relationship is found in segment 1. In the case of the accessibility measures, speed is more likely to be lower closer to the road intersections and areas around CBD. To validate the LSLR model, the predictive performance using a hold-out sample is estimated based on the Mean Absolute Deviation (MAD) and Mean Squared Prediction Error (MSPE). The results suggest that the LSLR model provides a satisfactory estimates of bicycle speed. In summary, this chapter presents a novel machine learning algorithm for improved GPS trip identification, followed by the development of an advanced econometric method for accurately predicting bicyclist speed. The modeling framework developed in this chapter provides important insights towards bicyclists travel time analysis. The speed analysis is also an essential LOS measure for multimodal transportation network and infrastructure planning. The findings of this chapter will assist in developing plans and policies to promote bicycle as a faster transportation mode. 74 Chapter 6: Destination Choice Modeling of Dockless Bikeshare Service (DBS) Users3 6.1 Introduction The chapter develops a destination choice model by using dockless bikeshare service (DBS) users’ trip itinerary information from GPS records in Kelowna. This research contributes in the following three dimensions: i) This study develops a random parameter latent segmentation-based logit (RPLSL) model to capture multi-dimensional unobserved heterogeneity, which includes inter-segment and intra-segment heterogeneity. Inter-segment heterogeneity is accommodated by formulating a flexible segment allocation component within the RPLSL framework. This segment allocation model allocates the DBS trips into different latent segments with a discrete distribution. Intra-segment heterogeneity is addressed with a continuous distribution of parameters within the segments. ii) This research develops a micro-spatial resolution for the spatial choice model which is defined as the bicycle analysis zone (BAZ). iii) This research tests the inter-dependencies between the trip origin and destination. The RPLSL model examines the origin attributes as the segment membership parameters, and destination attributes as the segment specific parameters. The model extensively tests the effects of built environment attributes, which includes transportation infrastructure (i.e. bicycle infrastructures, sidewalk, bus stops, elevation, bike index, road-connectivity index, among others), land use (i.e. land use index, area of park, commercial and residential land use, among others), accessibility (i.e. distance to CBD, bus stop, food stores, lake, haven, 3This chapter is derived from “Orvin, M., and Fatmi, M. (2020). Modeling Destination Choice Behavior of the Dockless Bikesharing Services Users: Assessing Origin-destination Inter-dependency Using Their Built Environment Attributes. Transportation Research Record (TRR): Journal of Transportation Research Board (TRB).” (under final review) 75 among others), and neighborhood characteristics (i.e. dwelling density and employment density). The outline of this chapter is as follows: section 6.2 shows an exploratory analysis of trip itinerary information from GPS records. Section 6.3 discusses the data processing technique. Then, section 6.4 discusses the RPLSL modeling technique including the spatial unit and choice set generation method. Section 6.5 introduces the independent variables tested in the RPLSL destination choice model. Section 6.6 presents the results of the model that include the goodness-of-fit measures, latent segment allocation component results, and parameter estimation results, and model validation results. Finally, section 6.7 summarizes the findings of this chapter. 6.2 Exploratory Analysis of Trip Itinerary Information from GPS Records The trip itinerary information consists of DBS trip ID, user ID, bike ID, trip start and end point, and trip duration from July to November in Kelowna. Raw data included 23,789 unique trips made by 7,249 individuals with 300 bicycles. Data also included information about whether the trip ended at designated bicycle parking area known as ‘havens’ or not. It is observed that about 51% trips were ended in havens. About 69.23% trips were made during July and August which is summer in Kelowna. Rest of the trips were made during fall and winter (i.e. from September to November). Among the 23,789 trips, about 61% and 10% trips were made during weekdays and peak hour respectively. 6.3 Data Processing Trip itinerary data processing involves several steps. First, trips missing destination coordinate are discarded. Trips with inconsistent information such as duration less than 1 min and greater 76 than 90 min, distance less than 30 m and greater than 15 km, and origin and destination of a trip having the same coordinate, are discarded. Following data cleaning, a total of 14,877 trips are retained for further analysis. Out of the 14,877 trips, 8,559 were made on weekdays, and the rest on weekends. Around 10% of the weekday trips were made on peak hour (i.e. 7 to 9 am and 4 to 6 pm). The average duration and distance of the trips were 17.16 min and 1.69 km respectively. It is also observed that average travel distance and trip duration in weekends are relatively higher than weekdays. 6.4 Methodology 6.4.1 Bicycle Analysis Zone and Choice Set Generation This chapter develops a random parameter latent segmentation-based logit (RPLSL) model for investigating the destination choice decisions of dockless bikeshare service (DBS) users. One of the challenges for destination choice modeling of DBS users is defining the spatial unit of analysis. This chapter proposes a 1 km x 1 km grid as the unit for bicycle analysis and defines it as the bicycle analysis zone (BAZ). Around 20% of the bicycle trips in Kelowna are intra-zonal for this 1 km x 1 km grid size. This assumption is similar to Clifton et al. (Clifton et al., 2016), who defined the size of a pedestrian analysis zone as a 0.4 km x 0.4 km grid that accommodates around 25% intra-zonal trips. The choice set for the destination choice model is generated by adopting a rule-based method. Distance is a critical factor in activity-based destination choice modeling, since shorter trip distance positively influences the probability for undertaking an activity (Banister, 2011). Bicycle trips are more sensitive to travel distance (Faghih-Imani & Eluru, 2015). Individuals are less likely to consider destinations at a longer distance as a potential alternative option. Therefore, the choice set generation technique needs to account individuals’ sensitivity to the 77 trip distance. This chapter adopts a rule-based technique to generate a threshold for bicycle trip distance, beyond which, destinations are less likely to be considered as an alternative option. The distance threshold is assumed to be 5.8 km, since 99th percentile of the bicycle trip distance in Kelowna is within this range. This distance represents road network-based distance from the origin to the destination. In this method, the choice set for each trip varies in hundreds, which might be computationally burdensome. A feasible choice set is generated for each trip by randomly selecting a subset of the alternatives. The size of the choice set is ten, which includes one chosen and nine potential alternative destinations. 6.4.2 Destination Choice Modeling Approach This study develops a destination choice model using trip itinerary information from GPS records. This research utilizes a hybrid of random parameter logit (RPL) and latent segmentation-based logit (LSL) model. One of the limitations of the RPL model is the prior assumption of parameter distribution over the sample population (Revelt & Train, 1998). LSL model is an alternative perspective to the RPL model and captures heterogeneity by distributing individuals into discrete latent segments (Greene & Hensher, 2003). However, LSL models pose a homogeneity assumption within the segments (Greene & Hensher, 2013). Ignoring such heterogeneity within the segments might result in inconsistent estimation and poor forecasting accuracy. This chapter utilizes the random utility-based discrete choice modeling technique to develop the random parameter latent segmentation-based logit (RPLSL) model. One of the purposes for developing the RPLSL model is to capture multi-dimensional heterogeneity including inter-segment, and intra-segment heterogeneity. The RPLSL model captures heterogeneity across the segments by distributing trips into discrete latent segments. In the case of addressing the 78 intra-segment heterogeneity, a continuous distribution of parameters is assumed within the segments. Assuming that trip i originating from BAZ o and destined to BAZ j is allocated to segment s. The segment allocation model takes the following multinomial logit form: 𝜑𝑖𝑠 = 𝑒𝛼𝑠+ 𝜃𝑠𝑋𝑖∑ 𝑒𝛼𝑠+ 𝜃𝑠𝑋𝑖𝑆𝑠=1 (6-1) Here, α𝑠 and θ𝑠 are the segment-membership constant and parameter vector, respectively. 𝑋𝑖 is the observed attributes of the trips. Since this study captures heterogeneity based on the origin locations, 𝑋𝑖 represents the attributes of trip i’s origin. Now, the utility function for trip i to be destined to BAZ j can be written as: 𝑈𝑖𝑗𝑐 | (𝑖𝜖𝑠) = 𝛽𝑠𝑌𝑖𝑗𝑐 + 𝑝𝑖𝑗𝑐 (6-2) Here, β𝑠 is the coefficient of the parameter vector, 𝑌𝑖𝑗𝑐 is the explanatory variable, 𝑝𝑖𝑗𝑐 denotes the error term, and c is the trip sequence. The trip itinerary data includes multiple trips from the same DBS user. To accommodate the panel effect of the data, this term c is introduced to represent the choice situations (where c ≥ 1). The unobserved heterogeneity within the segments is accommodated by allowing continuous variation of random parameters within the segments. Now, β𝑖 is the vector of estimable parameters that varies across the trips. β𝑖 can be expressed as: 𝛽𝑖 = 𝑚 + 𝛿𝑍𝑖 + 𝜆𝑖 (6-3) 79 Here, m = mean parameter estimates, 𝑍𝑖 = vector of explanatory variables representing the characteristics of destination j for trip i that affect the mean of β𝑖, δ = vector of estimable parameters, and λ𝑖 = randomly distributed term that captures unobserved heterogeneity across the trips. Assuming β𝑖 vector has a continuous density function 𝑃𝑟𝑜𝑏 (β𝑖 = β) = 𝑓 (β|λ), where λ is a vector of parameters. The choice probability can be written as: 𝑃𝑗𝑐(𝑖 ∈ 𝑠) = ∫ ∏𝑒𝑥𝑝 (𝛽𝑠𝑌𝑖𝑗𝑐)∑ 𝑒𝑥𝑝 (𝛽𝑠𝑌𝑖𝑗𝑐) 𝑓(𝛽|𝜆)𝑑𝛽𝐶𝑐=1 (6-4) Since repeated observations from the same individual exist in the data, the unobserved portion of the utility function is correlated. This correlated sequence of repeated choices is captured by estimating the joint probability of the choice sequence, c. The unconditional probability is expressed as: 𝑃𝑗(𝑖 ∈ 𝑠) = ∑ (𝜑𝑖𝑠)𝑆𝑠=1 ∗ (∫ ∏𝑒𝑥𝑝 (𝛽𝑠𝑌𝑖𝑗𝑐)∑ 𝑒𝑥𝑝 (𝛽𝑠𝑌𝑖𝑗𝑐) 𝑓(𝛽|𝜆)𝑑𝛽)𝐶𝑐=1 (6-5) The maximum simulated log-likelihood is utilized to estimate the factors in the log-likelihood expression. The simulated log-likelihood for the entire dataset can be written as: 𝐿𝐿 = ∑ 𝑙𝑜𝑔[∑ (𝜑𝑖𝑠 ) ∗ 𝑆𝑠=1 (𝑁𝑛=11𝑅∑ (∫ ∏𝑒𝑥𝑝 (𝛽𝑠𝑌𝑖𝑗𝑐)∑ 𝑒𝑥𝑝 (𝛽𝑠𝑌𝑖𝑗𝑐) 𝑓(𝛽|𝜆)𝑑𝛽))]𝐶𝑐=1𝑅𝑟=1 (6-6) 80 Here N is the total number of observations. Values of β are drawn from 𝑓(β|λ) and corresponding choice probability is estimated. Halton draws is used for improved accuracy (Greene, 2012). Similarly, choice probabilities are estimated for several draws of β. Finally, the approximate choice probability is estimated by determining the average of the choice probabilities from several draws. R is the total number of draws. The goodness-of-fit of the model is evaluated by using adjusted pseudo rho-squared, Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC) measures. 6.5 Independent Variables Tested in This Study The spatial unit of analysis for this study is bicycle analysis zone (BAZ), which is a 1 km x 1 km grid. Kelowna has a total of 313 BAZs. The secondary data are compiled at the spatial resolution of the BAZs. For neighborhood characteristics, the data are extracted at the dissemination area (DA) level. In this case, the weighted average area of DAs within each BAZ is computed by determining the percentage of the DA area that falls inside the BAZ. Henceforth the neighborhood attributes are adjusted as per the proportion of DAs within the BAZ. Transportation infrastructure characteristics (e.g. bicycle facility and bus stop) are also generated at the BAZ level. Furthermore, the road-connectivity index (RCI) for each BAZ is computed in each BAZ. The mean percent rise of elevation from destination to origin BAZ is deducted to identify whether the destination BAZ is at a higher elevation than the origin BAZ. To test the effect of bicycle friendliness of the BAZs, bike index (BI) for each BAZ is computed. Land use attributes include land use index (LUI), percentage of different land use types (e.g. commercial and residential), and area of park, among others for each BAZ. Among the neighborhood features, percentage of different dwelling types (e.g. single-detached and low-rise apartment), employment density, and dwelling density for each BAZ are determined. In the case of accessibility measures, road network-based distance from each BAZ to several 81 facilities and activity locations are generated which include distance to the Central Business District (CBD), midtown city center, bus stop, food store, school, retail store, lake, and recreational center, among others. In the case of trip characteristics, variables include travel distance, week type (e.g. weekday trip), and time of trip (e.g. off-peak hour trip), among others. 6.6 Results This chapter develops a RPLSL model to examine the trip-level destination choice behavior of the DBS users. Among the 14,877 trips retained for analysis, 7,000 trips are randomly selected for developing the model and the rest is used for validation. A summary statistic of the variables retained in the final RPLSL model is reported in Table 6.1. 6.6.1 Goodness-of-fit Measures The number of segments for the RPLSL model is determined on the basis of BIC and AIC measures. The results suggest BIC and AIC values of 11,155.3 and 10,633.0 respectively for two segments (Table 6.2). The BIC and AIC values for three segments are 12,113.8 and 11,291.5 respectively (Appendix C). Since the model with lower BIC and AIC measures fits the data best, the two-segment RPLSL model is selected as the final model. For comparison purposes, the RPLSL model is compared with the random parameters logit (RPL), latent segmentation-based logit (LSL), and multinomial logit (MNL) models. The detailed model results are presented in Appendix C. The goodness-of-fit measures suggest that the RPLSL model outperforms the RPL, LSL, and MNL models (Table 6.2). For example, the adjusted pseudo rho-squared value for the models are as follows: RPLSL is 0.675, RPL is 0.590, LSL is 0.429, and MNL is 0.352. In the case of the log-likelihood ratio test, the RPLSL model outperforms with a chi-squared value of 21,753.8 at the 1% significance level. Therefore, the RPLSL model is considered for further discussion. 82 Table 6.1. Descriptive Statistics of the Independent Variables Used in the RPLSL Destination Choice Model Variable Name Definition % or Mean Std. Dev. Trip characteristics Weekday trips Dummy; If trips are made on weekday =1, else = 0 57.5 0.49 Off-peak hour trips Dummy; If trips are made during off-peak hour =1, otherwise = 0 89.7 0.30 Travel distance Distance from trip origin to destination in kilometer 1.69 1.24 Transportation infrastructure characteristics of the trip origin Number of bus stops Number of bus stops at origin BAZ 14.1 6.68 Bike index greater than mean Dummy; If bike index is greater than the average of the sample at origin BAZ =1, else = 0 58.8 0.49 Land use attributes of the trip origin Higher percentage of commercial land use Dummy; If commercial land use is greater than 50% at origin BAZ =1, else = 0 9.36 0.29 Land use index Land use index at origin BAZ 0.80 0.15 Higher percentage of residential land use Dummy; If residential land use is greater than 50% at origin BAZ =1, else = 0 37.4 0.48 Neighborhood features of the trip origin Higher percentage of low-rise apartments Dummy; If percentage of apartment less than 5 stories is greater than 50% at origin BAZ =1, else = 0 69.1 0.46 Dwelling density greater than mean Dummy; If dwelling density is greater than the average of the sample at origin BAZ =1, else = 0 37.5 0.48 Median income Median income of people at origin BAZ 34953 4475 Land use characteristics of the trip destination Land use index Land use index 29.2 0.45 Area of park Area of park in sq. km 98.2 0.13 Lower percentage of commercial land use Dummy; If commercial land use is lower than 50% =1, else = 0 0.55 0.31 Higher percentage of residential land use Dummy; If residential land use is greater than 50% =1, else = 0 0.04 0.05 Transportation infrastructure characteristics of the trip destination Length of cycle track Total bicycle track length in meter 263 408 Length of shared path Total shared path length in meter 515 770 Length of sidewalk Total sidewalk length in meter 6964 6487 Number of bus stops Number of bus stops 8.68 8.48 Destination at higher elevation compared to origin Dummy; If difference of mean percent rise of elevation between destination and origin is positive =1, else = 0 37.8 0.48 Road-connectivity index Road-connectivity index 1.29 0.79 83 Variable Name Definition % or Mean Std. Dev. Neighborhood features of the trip destination Employment density Number of jobs per sq. km 164 78 Dwelling density Number of dwelling units per sq. km 527 433 Percentage of single-detached house Percentage of single-detached house 36.41 24.4 Accessibility measures of the trip destination Distance to nearest haven less than 500 m Dummy; If distance to the nearest haven is less than 500 meter =1, else = 0 78.4 0.41 Distance to CBD less than 500 m Dummy; If distance to the CBD is less than 500 meter =1, else = 0 24.0 0.43 Distance to midtown city center less than 500 m Dummy; If distance to the midtown city center is less than 500 meter =1, else = 0 3.33 0.18 Distance to nearest bus stop less than 500 m Dummy; If distance to the nearest bus stop is less than 500 meter =1, else = 0 71.11 0.45 Distance to nearest food store less than 500 m Dummy; If distance to the nearest food store is less than 500 meter =1, else = 0 59.90 0.49 Distance to nearest school less than 500 m Dummy; If distance to the nearest educational institution is less than 500 meter =1, else = 0 49.72 0.49 Distance to nearest retail store less than 500 m Dummy; If distance to the nearest retail store is less than 500 meter =1, else = 0 50.65 0.49 Distance to nearest lake Nearest lake distance in meter 1861 991 Distance to nearest recreational activity point less than 500 m Dummy; If distance to the nearest recreational activity point less than 500 meter =1, else = 0 40.36 0.49 Table 6.2. Goodness-of-fit Measures of the Destination Choice Models Model Log-likelihood function Adjusted pseudo rho-squared AIC BIC RPLSL -5,241.2 0.675 10,633.0 11,155.3 RPL -6,597.7 0.590 13,259.5 13,478.7 LSL -9,209.5 0.429 18,531.1 18,914.8 MNL -10,436.1 0.352 20,916.0 21,066.9 6.6.2 Latent Segment Allocation Component Results Latent segment allocation model is estimated based on the trip characteristics, and attributes of the origin BAZ including transportation infrastructure, land use, and neighborhood features (Table 6.3). The model is estimated assuming segment 2 as the reference segment. Among 84 the trip characteristics, weekday trips show a positive relation for segment 1. This indicates that weekday trips are more likely to be included in segment 1. Similarly, results suggest that peak hour and longer distance trips are more likely to be included in segment 1. On the other hand, segment 2 has a higher likelihood to include trips from weekend, off-peak period, and shorter length. In the case of transportation infrastructure attributes of the trip origin, BAZs with higher number of bus stops and higher bike index have a higher likelihood to be included in segment 1. Among the land use characteristics of the origins, segment 1 has a higher likelihood to include BAZs with lower percentage of residential areas, lower land use index, and higher percentage of commercial areas. Besides, neighborhood characteristics reveal that origin BAZs with higher percentage of low-rise apartments, higher dwelling density, and higher median income are more likely to be allocated in segment 1. Table 6.3. Latent Segment Allocation Results of the RPLSL Destination Choice Model Latent Segment Allocation Attributes Segment 1 Segment 2 coefficient t-stat coefficient t-stat Constant -7.773*** -3.64 reference Trip characteristics Weekday trips 0.257* 1.73 reference Off-peak hour trips -0.763*** -3.46 reference Travel distance 0.560*** 12.86 reference Transportation infrastructure characteristics of the trip origin Number of bus stops 0.016 1.43 reference Bike index greater than mean 0.944*** 3.55 reference Land use attributes of the trip origin Higher percentage of commercial land use 3.208*** 8.28 reference Land use index -6.470*** -9.27 reference Higher percentage of residential land use -7.145*** -13.55 reference Neighborhood features of the trip origin Higher percentage of low-rise apartments 6.616*** 14.43 reference Dwelling density greater than mean 3.387*** 7.87 reference Median income 0.0002*** 3.93 reference *** significance at 1% level, ** significance at 5% level, * significance at 10% level 85 Overall, segment 1 can be identified to include peak hour, longer, weekday trips originating from urban neighborhoods. In contrary, segment 2 can be identified to include shorter distance, off-peak period, weekend trips originating from the suburban neighborhoods. 6.6.3 Parameter Estimation Results The model tests several hypotheses regarding the built environment characteristics of the trip destination BAZ, which includes transportation infrastructure, land use, accessibility, and neighborhood characteristics (Table 6.4). It is found that the majority of the parameters show a statistical significance at least at the 1% significance level. The model tests heterogeneity within the segments for the bike index variable, representing the bicycle friendliness of the destination BAZs. Among the transportation infrastructure attributes, destination with longer lengths of cycle track shows a positive relationship. This implies a high propensity of trips to be destined to destinations having protected bike facilities. For shared paths, heterogeneity across the segments is observed. A negative relationship is found for segment 1, which includes peak hour and comparatively longer trips. DBS users might prefer dedicated facility for such longer trips, potentially made for commute purposes. In contrast, the variable shows a positive relationship in segment 2. Another variable representing the number of bus stops shows a negative relationship. Interestingly, this variable reveals heterogeneity within the segment by confirming significant heterogeneity in means. For the number of bus stops, bike index at the destination has a higher mean. This result reveals that DBS users prefer destinations with good transit accessibility if it is coupled with higher bicycle-friendly environment, further illustrating the attractiveness of destinations offering transfer between travel modes. Similarly, in the case of destination at a higher elevation compared to the origin, bike index shows significant heterogeneity in means. Variable representing road-connectivity index shows a positive relationship, implying that destinations with well-connected road 86 network are more likely to attract DBS trips. Destinations with higher road-connectivity have a shorter distance between intersections, offering convenient and shorter turning opportunities to the bicyclists. Table 6.4. Parameter Estimation Results of the RPLSL Destination Choice Model Attributes Segment 1 Segment 2 coefficient t-stat coefficient t-stat Land use characteristics of the trip destination Land use index# -39.6573*** -3.57 -106.049*** -31.91 Area of park# -63.0842** -2.46 -41.0792*** -12.81 Lower percentage of commercial land use -17.7649*** -14.38 1.13186*** 3.76 Higher percentage of residential land use 20.8032*** 10.39 4.22723*** 12.82 Transportation infrastructure characteristics of the trip destination Length of cycle track 0.00890*** 9.87 0.00352*** 13.42 Length of shared path -0.00235*** -6.19 0.00015* 1.92 Length of sidewalk -0.00029** -2.26 0.00064*** 23.64 Number of bus stops# -5.2982*** -10.62 -4.00571*** -18.95 Destination at higher elevation compared to origin# 30.5494*** 10.18 12.5413*** 11.86 Road-connectivity index -1.65581* -1.94 1.60158*** 6.24 Neighborhood features of the trip destination Employment density 0.13125*** 10.44 0.00304** 2.46 Dwelling density -0.01537*** -7.76 -0.00518*** -15.48 Percentage of single-detached house -0.11058*** -6.64 -0.04585*** -10.94 Accessibility measures of the trip destination Distance to nearest haven less than 500 m# 3.33121 1.14 -41.4724*** -26.33 Distance to CBD less than 500 m 0.99904 1.22 7.58223*** 27.25 Distance to midtown city center less than 500 m 12.8621*** 10.31 -1.90871*** -6.21 Distance to nearest bus stop less than 500 m 9.32437*** 9.76 7.13620*** 27.13 Distance to nearest food store less than 500 m -17.8242*** -11.66 0.46904*** 2.94 Distance to nearest school less than 500 m 1.97457*** 7.36 -1.18519*** -11.95 Distance to nearest retail store less than 500 m 8.06866*** 12.41 -4.64154*** -29.64 Distance to nearest lake 0.00056*** 3.22 -0.00018*** -2.89 Distance to nearest recreational activity point less than 500 m -9.92361*** -14.26 -1.94645*** -13.37 Heterogeneity in mean for random parameters of the trip destination Land use index: bike index 187.288*** 8.53 202.346*** 30.99 Area of park: bike index 58.5197 1.44 86.9975*** 14.43 Number of bus stops: bike index 6.74230*** 9.17 5.94111*** 17.26 Destination at higher elevation: bike index -46.6010*** -9.73 -20.0505*** -11.47 Distance to nearest haven less than 500 m: bike index 7.65400* 1.69 64.4329*** 24.65 *** significance at 1% level, ** significance at 5% level, * significance at 10% level # random parameter 87 In the case of the land use attributes, variables representing land use index and area of park at destinations show negative relationships. Both variables reveal significant heterogeneity in means. For destinations with a higher land use index, variable representing bike index shows a higher mean. This increases the likelihood for trips to be destined to such bike friendly mixed land use destinations. Similar results are found for destinations with a higher area of park and bike index. These results reveal that a higher mixed land use and a higher share of park areas do not make a location attractive for bicyclists. It also requires to be complemented by bicycle friendly infrastructure and environment. Destinations with lower commercial land uses are found to be less attractive destination for segment 1. This is expected as a lower commercial land use refers to limited opportunities for performing activities, implying fewer desirable destinations. In contrast, trips in segment 2 show a positive relationship. For neighborhood characteristics, employment density shows a positive relationship. The higher density of jobs attracts commute trips and employment-related errands, which might attribute to this preference. The coefficient value in segment 1 is more than 30 times stronger than segment 2. This might be attributed by the weekday peak hour trips included in segment 1. Variable representing a higher percentage of single-detached house at the destination shows a negative relationship. In the case of the accessibility measures, variable representing destinations within 500 m from the haven locations show heterogeneity across the segments. Havens are designated locations encouraging DBS users to return their bicycles upon completion of a trip and receive a monetary credit towards their next purchase. Since most of the havens are located in the urban areas, trips originating in such urban BAZs in segment 1 are likely to end within 500 m from the havens. On the other hand, trips originating in the suburban BAZs in segment 2 have 88 a lower likelihood to be destined to the urban locations within 500 m from the havens. Interestingly, this variable shows significant heterogeneity in means. For destinations within 500 m from havens, bike index has a higher mean. This implies that trips originating in suburban BAZs might also be destined to havens if bicycle friendly environment is provided. In addition, destinations within 500 m from the CBD are more likely to be chosen, as expected. For destinations within 500 m from the midtown city center, a positive relationship is retained in segment 1. The midtown city center area of Kelowna includes a shopping mall along with several retail stores, shops, groceries, and restaurants. As a result, trips originating in urban areas in segment 1 which are closer to the midtown city center, show a higher likelihood to be destined to such location. On the other hand, trips originating in suburban areas in segment 2, show a negative relationship. Destinations within 500 m from the bus stops reveal a positive relationship. Variables representing destinations within 500 m from the food stores, schools, retail stores, and lakes suggest a heterogeneous relationship across the segments. 6.6.4 Model Validation Results The RPLSL model results are validated using a hold-out sample to evaluate the performance of the model. The hold-out sample includes 7,877 trips. Similar to the RPLSL model, the choice set of the validation sample involves ten destination BAZ alternatives for each trip, including the chosen destination BAZ. The validation results suggest that the values for the log-likelihood function and adjusted pseudo rho-squared are - 4,416.48 and 0.61, respectively, revealing a good fit of the model for the validation sample. The predicted probability for choosing the destination BAZs is compared with the actually chosen destination BAZs for the validation sample. The percentage of correct prediction is estimated as 73%, which is satisfactory. The average predicted probabilities for trips to be destined at different BAZs in weekdays and weekends are estimated. For example, the average predicted probabilities for 89 weekday trips made during peak and off-peak hours are illustrated in Figure 6.1 and Figure 6.2. The analysis suggests that density is highly skewed at the CBD and the nearby downtown areas during the weekday peak hours (Figure 6.1). The percentage of correct prediction varies within a range of 73.26% - 75.48% for the weekday and weekend trips. This further confirms the satisfactory model fit and prediction accuracy of the RPLSL model. Figure 6.1. Predicted Average Probability of Choosing Destination BAZs for Weekday Peak Hour Trips Figure 6.2. Predicted Average Probability of Choosing Destination BAZs for Weekday Off-peak Hour Trips 90 6.7 Concluding Remarks This chapter investigates the trip-level destination choice behavior of bicyclists. A random parameter latent segmentation-based logit (RPLSL) model is developed using the GPS records of the trip itinerary of DBS users in Kelowna. The choice set for the RPLSL model is generated by adopting a rule-based method. The spatial unit of analysis for this research is bicycle analysis zone (BAZ), which is defined as a 1 km x 1 km grid. One of the key features of the RPLSL model is to capture multi-dimensional heterogeneity, that is inter-segment and intra-segment heterogeneity. Inter-segment heterogeneity is captured by formulating a segment allocation model that allocates DBS trips into discrete latent segments. In the case of the intra-segment heterogeneity, a continuous distribution of parameters is assumed within the segments. This chapter extensively tests the impact of built environment attributes including transportation infrastructure, land use, accessibility, and neighborhood characteristics. This research disentangles inter-dependencies between the origin and destination characteristics by testing origin attributes as the segment membership parameters and destination attributes as the segment specific parameters. The goodness-of-fit measures suggest that the RPLSL model outperforms random parameters logit, latent segmentation-based logit, and multinomial logit models. The RPLSL model is estimated for two latent segments based on the AIC and BIC measures. Segment allocation model is estimated using the trip characteristics and their origin attributes. The results suggest that segment 1 is more likely to include peak hour, longer distance, weekday trips originating from the urban neighborhoods. On the other hand, segment 2 includes shorter distance, off-peak period, weekend trips originating from the suburban neighborhoods. The parameter estimation results suggest that DBS trips are more likely to be destined at 91 destinations with longer length of cycle tracks, higher employment density, and shorter distance from the CBD (i.e. within 500 m). The model confirms the existence of multi-dimensional heterogeneity. In the case of inter-segment heterogeneity, longer commute trips in segment 1 are less likely to be destined to destinations with shared path. On the contrary, a positive relationship is found in segment 2. Destinations with lower commercial land use offer limited activity opportunities; thus, are less desirable destinations in segment 1. On the other hand, DBS users in segment 2 show a positive relationship. Destinations within 500 m from the midtown city center of Kelowna show a positive relationship in segment 1, which includes trips originating in the urban areas. In contrast, trips originating in suburban areas in segment 2, show a negative relationship. Further, the model confirms significant intra-segment heterogeneity in relation to the bike index attribute of the destinations. For variables representing the number of bus stops, elevation, land use index, area of park, and distance to designated bike return area (i.e. havens), there exist substantial heterogeneity in means for bike index. This reflects the existence of heterogeneity within the segments. One of the interesting findings is the variable representing the distance to havens that shows intra-segment and inter-segment heterogeneity. For example, trips originating from the urban BAZs in segments 1 are more likely to be destined to destinations within 500 m from the havens, since most havens are located near the urban areas. In contrast, shorter trips originating in suburban BAZs in segment 2 show a negative relationship. Interestingly, this variable shows significant heterogeneity by revealing a higher mean for bike index. This implies that bicycle friendly environment might increase the attractiveness of destinations closer to havens for trips originating in suburban BAZs. The validation exercise is performed by estimating the average 92 predicted probability, log-likelihood function, and adjusted pseudo rho-squared measures for the hold out sample. The results suggest that the performance of the model is satisfactory. In summary, the findings of this study provide important behavioral insights for making policies and strategic decisions to effectively promote dockless bikeshare services as an emerging and promising alternative transportation mode. 93 Chapter 7: Modeling the Reasons for Choosing Dockless Bikeshare Services (DBS)4 7.1 Introduction This chapter investigates dockless bikeshare service (DBS) users’ behavior; particularly, focusing on their reasons for choosing DBS over other modes of transportation. The reasons are thematically aggregated into the following six categories: cheapest option, fastest option, exercise purpose, recreational purpose, parking constraint, and unavailability of other modes (e.g. car, transit) or other reasons. This research contributes in the following two dimensions: i) This chapter develops a latent segmentation-based logit (LSL) model utilizing the user survey data from the dockless bikeshare service in Kelowna. The LSL model is developed to capture unobserved heterogeneity among DBS users. A flexible latent segment allocation model is formulated within the LSL framework, which allocates DBS users into discrete latent segments based on their characteristics. ii) This chapter examines the influence of socio-demographic characteristics (i.e. age, gender, and income, among others), travel attributes (i.e. bicycle usage frequency), and built environment measures such as transportation infrastructure (i.e. cycle track, bike lane, shared path, bike index, bus stop, sidewalk, among others), land use (i.e. land use index), and neighborhood attributes (i.e. dwelling density and employment density). The outline of this chapter is as follows: section 7.2 shows an exploratory analysis of DBS users’ survey data. Then, section 7.3 discusses the LSL modeling technique. Section 7.4 4 This chapter is derived from “Orvin, M., and Fatmi, M. (2020). Modeling the Reasons for Choosing Dockless Bikesharing Services?: Assessing the Effects of Transportation Infrastructure, Land Use, and Neighborhood Attributes. Published in the fully peer-reviewed proceedings of the 99th Annual Meeting of the Transportation Research Board (TRB), Washington D.C., USA”. 94 introduces the independent variables tested in the LSL model. Section 7.5 presents the results of the model that include the goodness-of-fit measures, latent segment allocation component results, parameter estimation results, and results of elasticity effects of explanatory variables. Finally, section 7.6 summarizes the findings and contributions of this chapter. 7.2 Exploratory Analysis of Dockless Bikeshare Service (DBS) Users’ Survey Data Figure 7.1 and Figure 7.2 illustrate the DBS users haven locations and trip origin respectively. Havens are designated parking locations for bicycles (City of Kelowna, 2018). These are simple marked areas for bicycle parking with no large infrastructure. The analysis suggests that the density of havens is skewed around the downtown business and cultural activity area, and nearby beaches of Kelowna. In the case of trip origins using DBS, the density is highly skewed in the downtown area. A secondary peak is observed around the beaches near downtown area. Another secondary peak is found around the mid-town city center, which is a major destination area that includes shopping malls, big-box retail stores, cinemas, and groceries, among others. The distribution of the primary reasons for choosing DBS by the users is as follows (Figure 7.3): cheapest option (20.35%), fastest option (20%), exercise purpose (7.14%), fun purpose (36.79%), parking constraint (6.79%), and unavailability of other modes (e.g. car) or other reasons (8.93%). Figure 7.4, Figure 7.5, Figure 7.6, and Figure 7.7 present the distribution of reasons by the respondents’ socio-demographic and travel characteristics. For example, a higher share of male users chooses DBS for the reasons of being the cheapest option (65%), fastest option (58%), and parking difficulties (69%), as shown in Figure 7.4. On the other hand, females predominantly choose it for fun (57%) and exercise purposes (60%). In the case of age, the fastest option includes a higher share (51%) of younger population (i.e. age group 25-34 years), which is illustrated in Figure 7.5. The 95 reason referring to parking difficulty is dominated (35%) by middle aged user (i.e. 35-44 years). In the case of income, around 38% and 36% of the users choosing DBS as the fastest option and cheapest option respectively are lower income with annual income less than $50,000 (Figure 7.6). In the case of exercise purpose, around 32% of users are higher income with income greater than $150,000. Interestingly, higher share (approximately 50%) of the middle-income population (i.e. income $50,000-$99,000) choose it for difficulties with parking spaces. For access to bicycles, around 69% of the users have a walking access time to get a DBS is less than 5 minutes (Figure 7.7). Figure 7.1. Density of Haven Locations in Kelowna 96 Figure 7.2. Density of DBS Trip Origin in Kelowna Figure 7.3. Reasons to Choose DBS 20.3520.007.1436.796.798.930 10 20 30 40Cheapest optionFastest optionExercise purposeRecreational purposeParking constraintUnavailability of other modesPercentage (%)Reasons to choose DBS 97 Figure 7.4. Gender Distribution for DBS Users Figure 7.5. Age Group Distribution for DBS Users 0 15 30 45 60 75Cheapest optionFastest optionExercise purposeRecreational purposeParking constraintUnavailability of other modesPercentage (%)Reasons to choose DBSfemale male0 20 40 60 80 100Cheapest optionFastest optionExercise purposeRecreational purposeParking constraintUnavailability of other modesPercentage (%)Reasons to choose DBSage<25 age 25-34 age35-44 age45-54 age55-64 age65+ 98 Figure 7.6. Individual Income Distribution for DBS Users Figure 7.7. Percentage Distribution of Access Time to Bicycles of DBS by Walking 020406080100CheapestoptionFastest option ExercisepurposeRecreationalpurposeParkingconstraintUnavailabilityof other modesPercentage (%)Reasons to choose DBS< $50,000 $50,000-99,000 $1,00,000-1,49,000 > $1,50,00034.6633.9415.168.304.333.610-2 mins 3-5 mins 5-10 mins 10-20 mins 20-45 mins 45+ mins 99 7.3 Methodology This study develops a random utility-based discrete modeling (RUM) technique to investigate individuals’ reasons for choosing the DBS over other modes of transportation using DBS users’ survey data. In the RUM family, multinomial logit (MNL) model is a widely used method due to its simplicity and computational efficiency (Khasnabis & Cynecki, 1988). However, the independence from irrelevant alternative (IIA) assumption of MNL models, which imposes the homogenous assumption on the unobserved component of the utility function (Khan et al., 2017). Discounting such unobserved heterogeneity might result in biased estimation and inconsistent forecasting. To capture such unobserved heterogeneity, this chapter extends the MNL model to a latent segmentation-based logit (LSL) model (Greene & Hensher, 2003; Hasnat et al., 2019; Khan et al., 2017). Assuming that individual DBS user d belonging to segment s chooses DBS due to alternative reason a from the set of alternatives A. The utility function can be written as: 𝑈𝑑/𝑠(𝑎) = 𝑋𝑑𝑎𝛽𝑠 + ∈𝑑𝑎 (7-1) Here, Xda = characteristic attribute ∈da = random error term βs = segment-specific parameter co-efficient The probability for choosing DBS can be expressed as: 𝑃𝑑/𝑠(𝑎) = 𝑒𝑋𝑑𝑎𝛽𝑠∑ 𝑒𝑋𝑑𝑎𝛽𝑠𝐴𝑎=1 (7-2) 100 Unobserved heterogeneity is captured by allocating individual users into discrete latent segments. This allocation is performed by formulating a segment allocation model within the LSL framework. This segment allocation model takes the following standard logit form: 𝜆𝑑𝑠 = 𝑒𝛿𝑠+ ∆𝑠𝑝𝑑∑ 𝑒𝛿𝑠+ ∆𝑠𝑝𝑑𝑆𝑠=1 (7-3) where, δ = segment-membership constant Δ = segment-membership coefficient p = individual users’ attributes representing their socio-demographic and travel characteristics s = total number of segments This chapter formulates the segment allocation model as a flexible element by characterizing the model using individuals’ attributes. This flexibility is introduced by the p parameter in the above equation. The unconditional probability can be written as: 𝑃𝑑(𝑎) = ∑ {𝑃𝑑/𝑠𝑆𝑠=1 (𝑎)} ∗ (𝜆𝑑𝑠) (7-4) The segment-specific parameters βs for s segments and segment allocation parameters δ𝑠 and ∆s for s-1 segments can be determined using the maximum log-likelihood function. The log-likelihood function for the entire dataset can be expressed below: 𝐿𝐿 = ∑ 𝑙𝑜𝑔[𝑁𝑛=1 𝑃𝑑(𝑎)]𝛼𝑑𝑎 (7-5) 101 Where, N = total number of observations α𝑑𝑎 = dummy, 1 while user d makes alternative choice a or else 0. The goodness-of-fit measures of the model are evaluated using the adjusted pseudo rho-squared, Akaike information criteria (AIC), and Bayesian information criteria (BIC) measures. 7.4 Independent Variables Tested in This Study Built environment attributes are generated for a 1 km buffer area from the respondents’ home location using ArcGIS. Among the built environment characteristics, transportation infrastructure attributes include the number of bus stops, length of sidewalk, and length of bicycle facilities including shared path, cycle track, and bike lane. Land use characteristics include the percentage of different land uses, land use index (LUI), bike index (BI), and distance from home to different facilities such as destination locations and CBD. Neighborhood characteristics are also utilized that include population density, dwelling density, and employment density, among others. Socio-demographic and travel characteristics include age, income, gender, vehicle ownership, and bicycle usage frequency among others. 7.5 Results This chapter tests several independent variables during model estimation stage. The latent segment allocation model is estimated based on the individuals’ socio-demographic and travel attributes. The LSL model extensively tests the influence of built environment attributes on the individuals’ decisions for using dockless bikeshare services. The summary statistics of the explanatory variables retained in the final model are presented in Table 7.1. 102 Table 7.1. Descriptive Statistics of the Independent Variables Used in the Reasons to Choose DBS LSL Model Variable Name Description Percentage/ Mean Socio-demographic characteristics Age less than 44 Dummy; If users’ age is less than 44 years =1, otherwise = 0 70.60 Annual income greater than $1,20,000 Dummy; If individuals’ income is greater than $1,20,000 annually =1, otherwise = 0 16.31 Female Dummy; If gender is female =1, else = 0 46.80 No personal vehicle ownership Dummy, if individual does not own a personal vehicle =1, else 0 6.73 Travel attributes Frequent bicycle usage Dummy; If bicycle riding frequency is 11-20 times during the past 3 months =1, otherwise = 0 64.18 Frequent dropbike usage Dummy; If dropbike riding frequency is 11-20 times in last 3 months =1, else = 0 7.70 Alternate mode to dropbike-drive Dummy; People who prefer driving to reach the destination if dropbike is unavailable =1, else 0 19.15 Transportation infrastructure attributes Bicycle infrastructure Length of total bicycle infrastructure that includes shared path, bike lane, and cycle track within 1 km buffer from home location (km) 12.99 Length of bike lane and cycle track Length of bike lane and cycle track within 1 km buffer (km) 10.05 Length of shared path Shared path length within 1 km buffer (km) 3.12 Length of bike lane Bike lane length within 1 km buffer (km) 8.95 Number of transit stops Number of transit stop within 1 km buffer 32.79 Bike index (BI) Bike index 0.61 Length of sidewalk Sidewalk length within 1 km buffer (km) 29.82 Land use characteristics Density of destination locations Number of destination points within 1 km buffer 14.06 Land use index (LUI) Land use index 0.71 Neighborhood characteristics Dwelling density Number of dwelling units divided by 1 km buffer area 613.14 Employment density Number of jobs divided by respondents’ home dissemination area 6.08 7.5.1 Goodness-of-fit Measures The number of segments for the LSL model is determined based on the BIC and AIC measures (Yasmin & Eluru, 2016). BIC value for two segments is lower (i.e. 1,049.3) than that for three segments (i.e. 1,171.1) (Appendix D). Similarly, AIC value for two segments is 878.1, while for three segments is 926.2 (Appendix D). The model with two segments is selected as 103 it fits the data best. For comparison purposes, this chapter develops a multinomial logit (MNL) model in addition to the LSL model. The detailed MNL model result is presented in Appendix D. The LSL model outperforms the MNL model in terms of goodness-of-fit measures. For instance, adjusted pseudo rho-squared value for the MNL and LSL models is 0.035 and 0.223 respectively (Table 7.2). Therefore, the LSL model with two segments is considered as the final model for further discussion. Table 7.2. Goodness-of-fit Measures of the Reasons to Choose DBS Models Attributes LSL MNL Log-likelihood function - 392.07 - 421.88 Adjusted pseudo rho-squared 0.223 0.035 AIC 878.1 885.7 BIC 1,049.3 1,108.9 Table 7.3. Latent Segment Allocation Results of the Reasons to Choose DBS LSL Model Latent Segment Allocation Attributes Segment 1 Segment 2 coefficient t-stat coefficient t-stat Socio-demographic and travel characteristics Age less than 44 years -0.70** -1.97 reference Annual income greater than $1,20,000 -1.19** -2.54 reference Female 0.90*** 2.93 reference Frequent bicycle usage 0.36 1.13 reference *** significance at 1% level, ** significance at 5% level, * significance at 10% level The LSL model estimation results are presented in Table 7.3 and Table 7.4. It is observed that majority of the parameters are statistically significant (significance at least at the 5% level) in at least one segment. Several variables are excluded due to either poor statistical significance or inconsistency with the hypothesis. Some variables in the model are not statistically significant; however, they are retained due to important policy implications. The 104 assumption is that these variables might exhibit statistical significance if a larger sample size were used. 7.5.2 Latent Segment Allocation Component Results The segment allocation component retains the following socio-demographic characteristics such as age, gender, and income, travel attributes such as frequent bicycle user (Table 7.3). The model is estimated assuming segment 2 as the reference segment. The model results suggest that individuals with an annual income of more than $120,000 and age less than 44 years show negative relationships in segment 1. This indicates that comparatively higher income and older individuals are less likely to be included in segment 1. In contrast, higher income and younger individuals have a higher likelihood to be included in segment 2. The positive sign of female shows that females have a higher probability to be included in segment 1. Furthermore, the positive sign for individuals with frequent bicycle usage suggests that frequent bicycle riders are more likely to be included in segment 1. Therefore, segment 1 can be identified to include relatively older, lower-income, frequent bicycle rider, females. On the contrary, segment 2 can be identified to include relatively higher income, younger, non-frequent bicycle user, males. 7.5.3 Parameter Estimation Results The parameter estimation results of the model are presented in Table 7.4. In the case of the reason for choosing DBS as the cheapest option, transit accessibility represented by the number of transit stops within 1 km from home is found to have a significant positive effect. The higher the number of bus stops, the higher is the opportunity to access transit services using DBS. Such multi-modal trips are often cheaper than other modes such as car or taxi. As a result, DBS emerges as the cheapest alternative mode in areas with higher transit 105 accessibility. This might indicate the potential use of DBS by transit riders to tackle their first mile and last mile challenges. Variables representing the length of bike lane and shared path show a positive relationship. Such bicycle friendly neighborhoods attract more bike sharing and multi-modal trips, further making DBS as the cheapest mode option. Table 7.4. Parameter Estimation Results of the Reasons to Choose DBS LSL Model Attributes Segment 1 Segment 2 coefficient t-stat coefficient t-stat Cheapest option Number of transit stops 0.49*** 3.47 0.04* 1.82 Length of shared path 1.35** 2.15 0.19 1.19 Length of bike lane 0.61** 2.55 0.05 0.87 Fastest option Bicycle infrastructure 0.67*** 3.60 0.006 0.23 Density of destination locations 0.21*** 2.74 0.02 0.65 Frequent dropbike usage 0.87* 1.86 0.87* 1.86 Exercise purpose Bike index (BI) -6.43 -1.22 34.88 1.28 Length of sidewalk 0.69*** 3.59 0.04 0.53 Recreational purpose Land use index (LUI) 7.49** 2.22 -15.89 -1.51 Length of sidewalk 0.63*** 3.29 -0.43** -1.99 Parking constraint Alternative mode to dropbike-drive -5.46 -0.57 1.19** 1.96 Dwelling density 0.03*** 3.12 0.002 1.28 Employment density -0.71 -0.64 0.05 1.09 Unavailability of other modes or other reasons Length of bike lane and cycle track 0.89** 2.32 0.09 1.18 Bike index (BI) -19.10 -1.14 8.0* 1.85 No personal vehicle ownership 16.01*** 3.30 -0.34 -0.32 Constants Cheapest mode -16.95** -2.33 4.51* 1.73 Fastest option -20.19*** -2.70 5.45** 2.07 Exercise purpose -2.67 -0.47 -20.85 -1.03 Recreational purpose -7.50 -1.23 19.54** 2.36 Due to difficulties in finding parking space -13.91* -1.88 3.53 1.28 Unavailability of other modes (reference) - - - - *** significance at 1% level, ** significance at 5% level, * significance at 10% level 106 For the reason fastest option, the length of bicycle infrastructure that includes bike lane, cycle track, and shared path reveal a positive relationship. Bicycle infrastructure facilitates convenient and faster travel to bike riders. As a result, individuals residing in bicycle supportive neighborhoods more likely to choose DBS as a faster option. Individuals residing in neighborhoods with a higher density of activity locations such as cinemas, and restaurants are more likely to choose DBS as a faster option, since such closer by attractions can be easily accessed using DBS. Furthermore, frequent DBS riders show a higher likelihood to choose DBS as the fastest mode. For recreational purposes, variables representing land use index and length of the sidewalk show significant heterogeneity across the segments. Land use index and length of sidewalk exhibit a positive relationship in segment 1. Older and lower income frequent riders residing in higher mixed land use areas with good active transportation infrastructure show preference towards choosing DBS for recreational purposes. On the other hand, higher-income individuals in segment 2 reveal a lower affinity towards using DBS for recreational purposes. For parking unavailability at destination locations, DBS is found to replace driving. This variable shows heterogeneity across the segments. The positive relationship for segment 2 implies that DBS has a higher possibility to replace driving of the higher income younger individuals in scenarios when parking is demanding and strenuous at the destinations. Another variable representing dwelling density shows a positive relationship. Higher dwelling density in home neighborhoods might reflect limited parking availability. Parking constraints at both home and destination locations refer to unavailability and higher cost of parking, which influences individuals to choose DBS. Individuals residing in neighborhoods with higher 107 employment density show a negative relationship in segment 1; in contrast, a positive relationship is observed in segment 2. In the case of choosing DBS for exercise purposes, bike index shows heterogeneity across the segments. A positive relationship for segment 2 suggests that a higher bicycle friendly environment increases the probability of choosing DBS for physical exercise purposes. This positive relationship might be attributed to higher share of younger and higher income population in segment 2. In contrast, this variable shows a negative relationship in segment 1. Length of the sidewalk reveals a positive relationship, implying that individuals residing in neighborhoods with good active transportation infrastructure are more likely to use DBS for exercise purposes. In the case of unavailability of other modes (i.e. car, transit) or other reasons, individuals residing neighborhoods with good active transportation infrastructure such as longer length of bike lane and cycle track, show a higher likelihood to choose DBS if other modes are not available, which is expected. Higher income younger individuals in segment 2, who resides in bicycle friendly neighborhoods with higher bike index are more likely to choose DBS during the unavailability of other modes. In contrast, older people are less likely to choose DBS due to the unavailability of other modes. Variable representing individuals without household car ownership shows a positive relationship in segment 1. This implies that older and lower income population without car ownership has a higher likelihood to choose DBS due to unavailability of other modes. In contrast, higher income population in segment 2 shows a negative relationship. 108 7.5.4 Results of Elasticity Effects The parameters estimation results do not reveal the magnitude of the impact of the determinants on individuals’ reasons for choosing DBS. To determine the magnitude of impact, it is important to estimate the elasticity effects of the explanatory variables (N. A. Khan, Fatmi, & Habib, 2017). Elasticity effects are estimated across the two segments. The results suggest significant heterogeneous magnitude of impact of the independent variables across the segments (Table 7.5). In the case of older, lower income, frequent bicycle rider, females in segment 1, variables representing transit accessibility, active transportation infrastructures such as length of sidewalks, bike lanes, and cycle tracks, dwelling density, and vehicle ownership show a substantial positive impact. For example, good transit accessibility reveals a significant positive impact on individuals’ decisions for choosing DBS as the cheapest option for segment 1. This finding indicates that DBS might be a potential solution to the first mile and last mile challenges of older, lower income transit riders in segment 1. The positive impact of the length of the sidewalks for exercise and recreational purposes suggest that good active transportation infrastructure in the home neighborhoods significantly influences older, lower income females’ decisions to choose DBS for exercise and recreational purposes. The positive impact of higher dwelling density in home neighborhoods for choosing DBS due to parking constraint at destination reveals that unavailability and higher cost of parking at both home and destination locations influence lower income individuals in segment 1 to choose DBS. The significant positive impact of the variable representing individuals without household car ownership while other modes are unavailable indicates the preference of older and lower income population in segments 1 to choose DBS in such situations. In the case of comparatively higher income, younger, non-frequent bicycle user, males in segment 2, variables representing bike index, length of sidewalk, land use index (LUI), and 109 personal vehicle ownership show substantial impact. For example, bike index in the home neighborhood is found to significantly impact higher income, younger males to choose DBS for exercise purposes. Interestingly the negative impact of the length of sidewalk and LUI for recreational purposes suggest that higher income individuals in segment 1 are less likely to choose DBS for recreational purposes. Interestingly, the positive impact of bike index during the unavailability of other modes refer that higher income individuals residing in such bicycle friendly neighborhoods are more likely to choose DBS while other modes are unavailable. The heterogeneity in terms of the magnitude of impact captured across the two segments should be considered during policy-making to effectively promote dockless bikeshare services. Table 7.5. Elasticity Effects Variable Segment 1 Segment 2 Cheapest option Number of transit stops 15.09 0.77 Length of shared path 3.86 0.41 Length of bike lane 5.02 0.30 Fastest option Bicycle infrastructure facility 3.10 0.45 Density of destination locations 2.35 0.23 Frequent dropbike usage 5.55 1.26 Exercise purpose Bike index (BI) -3.58 19.96 Length of sidewalk 18.62 1.23 Recreational purpose Land use index (LUI) 1.68 -10.60 Length of the sidewalk 6.39 -12.50 Parking constraint Alternate mode to dropbike-drive -12.31 1.2 Dwelling density 17.57 1.32 Employment density -4.32 0.28 Unavailability of other modes or other reasons Length of the bike lane and cycle track 6.34 1.32 Bike index (BI) -11.25 4.17 No personal vehicle ownership 26.19 -11.30 110 7.6 Concluding Remarks This chapter contributes by investigating the behavior of dockless bikeshare service (DBS) users. Specifically, this chapter examines the reasons for choosing DBS over other travel modes. A latent segmentation-based logit (LSL) model is developed using data from a DBS users’ survey conducted in Kelowna. A flexible segment allocation model is formulated within the LSL framework to capture unobserved heterogeneity. This segment allocation model allocates DBS users into discrete latent segments. One of the key features of this research is to extensively test the influence of socio-demographic characteristics, travel attributes, and built environment measures such as transportation infrastructure, land use, and neighborhood characteristics. The LSL model outperforms the MNL model in terms of goodness-of-fit measures. The LSL model is estimated for two segments based on the AIC and BIC measures. The segment allocation model is estimated using the socio-demographic characteristics and travel attributes. The segment allocation model results suggest that segment 1 can be identified to include relatively older, lower income, frequent bicycle rider, females. On the other hand, segment 2 can be identified to include relatively higher income, younger, non-frequent bicycle user, males. The parameter estimation results confirm the influence of transportation infrastructure, land use, and neighborhood attributes for choosing DBS. For example, individuals residing in bicycle friendly neighborhoods with good transit accessibility, bike lanes, and shared paths are more likely to choose DBS as the cheapest option. Individuals residing in neighborhoods with a higher density of destinations and a longer length of bicycle infrastructure such as bike lanes, cycle tracks, and shared paths reveal a higher likelihood for choosing DBS as the fastest option. The model confirms significant heterogeneity across the two segments. For example, land use index and length of sidewalk exhibit a positive 111 relationship in segment 1. This implies that older and lower income frequent riders residing in higher mixed land use areas with good active transportation infrastructure reveal preference towards choosing DBS for recreational purposes. On the other hand, higher-income individuals in segment 2 show a lower likelihood of choosing DBS for recreational purposes. Furthermore, individuals residing in bicycle friendly neighborhoods with higher bike index are more likely to choose DBS during the unavailability of other modes. This relationship is found for segment 2, which includes higher income younger individuals. In contrast, older people in segment 1 are less likely to choose DBS due to the unavailability of other modes. The elasticity effects offer important behavioral insights for developing DBS supportive policies. For example, elasticity analysis captures heterogeneity across the two segments and reveals the need to devise policies targeting different population groups for the effective promotion and operation of DBS. For example, in the case of older, lower income, frequent bicycle rider, females in segment 1, variables representing transit accessibility, length of sidewalks, length of bike lanes, length of cycle tracks, dwelling density, and vehicle ownership show substantial positive impact for choosing DBS. On the other hand, for higher income, younger, non-frequent bicycle user, males in segment 2, variables representing bike index, length of sidewalk, land use index, and personal vehicle ownership show a substantial impact. In summary, this chapter provides important behavioral insights for policy-making. Specifically, the heterogeneity captured across the population segments needs to be accommodated within the policies to ensure effective promotion and operation of dockless bikeshare systems. 112 Chapter 8: Conclusion 8.1 Summary of Contributions Bicycling is a popular alternative sustainable travel mode yielding economic, social, environmental, and health benefits. As a result, cities around the world are making significant investments in their bicycle infrastructures. It is important to estimate the bicycling demand for effective infrastructure investment decision-making. For effective bicycle planning and infrastructure investment decision-making, it is critical to predict bicycling demand and understand the bicycle user behavior. This thesis develops advanced modeling techniques to estimate the bicycling demand and bicycle user behavior such as speed choice, destination choice, and reasons to choose newer shared mobility services. The models are developed using multiple source of data such as eco-counter, GPS records of trip trajectory and trip itinerary, and survey data. The bicycling demand model adopts a latent segmentation-based negative binomial (LSNB) modeling method utilizing daily bicycle count data over a year. This research accommodates the combined effects of the spatial and temporal attributes on bicycling demand. Latent heterogeneity is captured by formulating a flexible segment allocation model within the LSNB framework. Based on the goodness-of-fit measures, the LSNB model is estimated for two segments. The model results of the segment allocation component reveal that segment 1 has a higher probability to include weekend, winter trips made in suburban areas. On the other hand, segment 2 has a higher likelihood to include weekday, summer, and fall trips made in urban areas. Parameter estimation results suggest that higher temperature, lower rainfall and snowfall, and facilities closer to water bodies coupled with cycle tracks illustrate a higher possibility to increase bicycling demand. The model also confirms significant heterogeneity 113 across the segments. For example, higher bike index and higher road-connectivity index show a higher likelihood to increase bicycle volume in the suburban areas. In contrast, bicycling demand is likely to be higher in urban areas even with a lower road-connectivity index and bike index. The results also reveal that shared paths are more likely to increase bicycling demand in the urban areas and lower AADT is associated with increased bicycling demand in the suburban areas. Finally, the results are validated by predicting the average daily bicycling demand by facility types. The prediction results suggest that the LSNB model provides satisfactory estimation of bicycling demand. The bicyclists’ speed choice analysis is performed using GPS records - trip trajectory information. An unsupervised machine learning (ML) method; specifically, a Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is adopted to identify actual trip trajectory in the GPS data. Geometric map-matching algorithm is used to map-match the GPS data to the existing road infrastructure network. Following the data processing, a latent segmentation-based linear regression (LSLR) model is developed using the filtered GPS data. A flexible segment allocation model is embedded within the LSLR framework to capture latent heterogeneity. In the case of the goodness-of-fit measures, the DBSCAN algorithm outperforms the traditional rule based techniques. The LSLR results confirm that the model fits data best with two segments. The segment allocation component results suggest that segment 1 has higher likelihood to include weekend suburban trips made in off-peak hours during summer season. On the other hand, urban peak hour weekday trips made in fall and winter seasons are more likely to be included in segment 2. Model estimation results suggest that bicyclists tend to travel faster in shared path and areas with a higher bike index. In contrast, bicycle speed is more likely to decrease with increase in road elevation and locations closer to the road intersections and CBD. The model confirms significant heterogeneity across 114 the two segments. For example, bicyclists’ speed tends to increase with an increase in temperature during the fall and winter seasons. On the other hand, speed is more likely to decrease with higher temperature during the summer season. Furthermore, bicycle speed is found negatively associated with higher AADT for commute urban trips. In contrast, positive relationship is observed for suburban trips. Results also show that bicyclists tend to travel faster at location with a higher bike index and protected cycle infrastructure for trips made in urban areas. Finally, a model validation exercise is conducted by comparing the predictive performance of the model with a hold-out sample based on the Mean Absolute Deviation (MAD) and Mean Squared Prediction Error (MSPE). The results suggest that the LSLR model provides a reasonably satisfactory estimates of bicycle speed. In the case of the destination choice analysis, a random parameter latent segmentation-based logit (RPLSL) model is developed using trip itinerary information from the GPS records of the DBS users. This research defines bicycle analysis zone (BAZ) as the unit of analysis for the spatial choice model. One of the motivations for developing the RPLSL model is to capture multi-dimensional heterogeneity assuming a discrete distribution to allocate trips into discrete latent segments followed by the assumption of a continuous distribution of parameters within each segment. The model also accomodates inter-dependencies between the origin and destination of trips by using trip origin attributes as the segment membership parameters, and trip destination attributes as the segment-specific parameters. Goodness-of-fit measures suggest that RPLSL model fits the data best with two latent segments. The segment allocation results suggest that segment 1 is more likely to include peak hour, longer distance, weekday trips originating from the urban neighborhoods. In contrast, segment 2 has a higher likelihood to include shorter distance, off-peak period, weekend trips originating from the suburban neighborhoods. The parameter estimation results reveal that DBS trips have a higher 115 probability to be destined at BAZs with longer length of cycle tracks, higher employment density, and closer to CBD. The model confirms the presence of multi-dimensional heterogeneity. For example, in the case of inter-segment heterogeneity, destinations with lower commercial land use are less desirable for trips originating from urban neighborhoods. In contrast, DBS users from suburban neighborhoods show a positive relationship. Intra-segment heterogeneity is tested for the bike index attribute of the destinations. The results suggest that a substantial heterogeneity in mean for bike index exists of destinations with a higher elevation, land use index, and distance to haven, among others. For example, results suggest that trips are less likely to be destined to destinations with a higher land use index. However, this variable shows significant heterogeneity by revealing a higher mean for bike index, implying that higher bike index coupled with a higher land use index might increase the attractiveness of destinations. Finally, based on the validation results using a hold-out sample, it can be concluded that the RPLSL model provides a satisfactory performance. In the case of the reasons to choose DBS over other travel modes, a latent segmentation-based logit (LSL) model is developed using the users’ survey data of DBS users. A flexible segment allocation model is formulated within the LSL framework to capture unobserved heterogeneity. This research also examines the influence of socio-demographic characteristics, travel attributes, and built environment measures such as transportation infrastructure, land use, and neighborhood attributes. Based on the goodness-of-fit measures, the LSL model is estimated for two segments. The segment allocation model results reveal that segment 1 can be identified to include relatively older, female, lower income, and frequent bicycle rider. On the other hand, segment 2 can be identified to include relatively higher income, younger, male, non-frequent bicycle user. Parameter estimation results suggest individuals residing in bicycle friendly neighborhoods with good transit accessibility, bike 116 lanes, and shared paths, have a higher likelihood to choose DBS as the cheapest option. Furthermore, individuals residing in neighborhoods with a higher density of destinations and a longer length of bicycle infrastructure such as bike lanes, cycle tracks, and shared paths are more likely to prefer using DBS as the fastest option. The model confirms significant heterogeneity across the two segments. For example, older and lower income frequent riders residing in higher mixed land use areas with good active transportation infrastructure reveal preference towards choosing DBS for recreational purposes. In contrast, higher-income individuals in segment 2 reveal a lower probability of choosing DBS for recreational purposes. Finally, elasticity analysis reveals that for older, lower income, frequent female bicycle users in segment 1, neighborhoods with higher transit accessibility, longer length of cycle tracks, shared paths, bike lanes and sidewalks, and higher dwelling density and with no personal vehicle ownership show substantial positive impact for choosing DBS. On the other hand, for higher income, younger, non-frequent male bicycle users in segment 2, variables representing bike index, land use index, length of sidewalk, and no personal vehicle ownership reveal a substantial impact for choosing DBS. In summary, this thesis contributes by developing advanced methods to improve the estimation procedure of bicycling demand and bicycle user behavior. This understanding is important to assist in developing plans and policies to promote bicycle as a faster, popular, and sustainable transportation mode. The contributions of this thesis are briefly presented below: i) This thesis investigates bicycling demand and user behavior such as speed choice, destination choice, and reasons to choose newer shared mobility services. To improve model estimation techniques, advanced econometric models such as latent 117 segmentation-based linear regression (LSLR), a latent segmentation-based negative binomial (LSNB), and latent segmentation-based logit (LSL) model are adopted to capture heterogeneity using a discrete distribution of the parameters. ii) This thesis develops a random parameter latent segmentation-based logit (RPLSL) model to capture multi-dimensional heterogeneity – i.e. inter-segment and intra-segment heterogeneity. Inter-segment heterogeneity is accommodated by formulating a flexible segment allocation component within the RPLSL framework. Intra-segment heterogeneity is addressed with a continuous distribution of parameters within the segments. This model is tested for the destination modeling exercise. iii) The destination choice model disentangles the relationship between origin and destination attributes of trips. This model is developed at the micro-spatial resolution by introducing and defining bicycle analysis zone (BAZ). iv) This thesis adopts a machine learning (ML) technique - a Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify actual trip trajectory from GPS records. This ML algorithm outperforms the traditional rule-based method. v) This thesis confirms that effects of weather (e.g. temperature, snow, rain, wind-chill index, among others), built environment and traffic (e.g. bicycle infrastructures, bike index, bus stops, mean percent rise of elevation, AADT, road-connectivity index, road types, among others), land use (e.g. land use index, residential and commercial land use, among others), accessibility (e.g. distance to CBD, bus stop, park, among others), 118 neighborhood (e.g. employment density, dwelling density, among others), socio-demographic (e.g. age, gender, income, among others), and travel (e.g. time of day, type of week, season, travel distance, among others) characteristics on bicycle usage. 8.2 Concluding Remarks This thesis develops advanced econometric and machine learning modeling techniques to investigate bicycling demand and bicycle user behavior such as speed choice, destination choice, and reasons to choose dockless bikeshare service (DBS). The findings of the bicycling demand model provide important behavioral insights towards how temporal and spatial attributes effect bicycling demand. Other similar cities can adopt the proposed model to better understand their bicycling demand and develop effective bicycling supportive policies. The model is expected to assist transportation planners and engineers to target critical spatial and temporal factors for inducing more bicycle trips. The bicyclists’ speed choice modeling framework developed in this thesis provides critical understanding towards bicyclists travel time. The speed of bicyclists is an essential LOS measure for multimodal transportation network and infrastructure planning. The findings of the speed choice model will assist in developing plans and policies to promote bicycle as a faster alternative transportation mode. The findings of destination choice model of DBS users and the reasons to choose DBS have significant implications for policy-making and strategic decisions to effectively promote dockless bikeshare services as an emerging and promising alternative transportation mode. The heterogeneity captured in this thesis needs to be accommodated within the policies to increase bicycle volume and usage, and ensure effective promotion and operation of dockless bikeshare systems. 119 This thesis has certain limitations. For example, this research could not confirm whether the models can be applied for other cities or not. Another limitation of this research is that it could not investigate the causal relationship between the dependent and independent variables. Structural equation models could be developed to examine the causal association. In addition, the magnitude of impact of the independent variables is tested only for the reasons to choose DBS model. Elasticity analysis needs to be performed to examine the magnitude of impact of the variables for bicycling demand, speed choice, and destination choice models. For the destination choice analysis, this research could not compare between the percentage of intra-zonal trips within the average TAZ size and the BAZ size. In the case of speed choice analysis, independent variables are generated for 250 x 250 m buffer size. Alternative buffer dimensions have not tested to determine the optimal buffer size. In addition, friction factor of the road surface is an important indicator for speed choice analysis. This thesis could not accommodate the friction attribute due to the unavailability of such data. 8.3 Future Research This thesis develops and presents the findings of bicycling demand, speed choice, destination choice, and reasons to use DBS models. The following recommendations can be made as potential future research directions: i) Further research is required to understand whether the models can be applied for other cities or not; particularly, spatial transferability of the models to other cities needs to be tested. ii) Speed choice model is developed using GPS data. Post preliminary processing, a machine learning algorithm – DBSCAN is implemented to identify the outliers in the 120 GPS trip trajectory. Future research should highlight the comparison of the DBSCAN algorithm with other machine learning algorithms such as random forest clustering algorithm. iii) In the case of map-matching technique, this research adopts a geometric map-matching method in ArcGIS to align the GPS trajectory points with the road and bicycle network. Future research should concentrate on developing advanced map-matching techniques that includes topological algorithms. iv) The destination choice model adopts a random sampling technique to reduce the choice set following the rule-based approach for choice set generation. Future research should focus on developing advanced alternative methods for reducing the choice set. v) In the case of modeling the reasons for choosing DBS, this thesis utilizes a DBS user survey data. It was a small sample size with 282 responses. Therefore, validation of the LSL model is not performed. Future research direction should focus on extensive data collection about DBS users’ behavior. 121 References Bachand-Marleau, J., Lee, B. H. Y., & El-Geneidy, A. M. (2013). Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use. 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Parameter Estimation Results of the 3-segment LSNB Bicycling Demand Model Segment 1 Segment 2 Segment 3 Attributes Coefficient (t-stat) Coefficient (t-stat) Coefficient (t-stat) Constant -17.21 (0.00) -16.18* (-1.81) -26.84*** (-35.94) Dispersion parameter 0.24 x 10-5 (0.00) 5.22*** (11.54) 5.05*** (23.18) Weather characteristics Average temperature (0C) 0.06 (0.00) 0.06*** (22.71) 0.09*** (50.32) Average rainfall (mm) 0.11 (0.00) -0.03*** (-4.38) -0.07*** (-19.42) Maximum snowfall (cm) -0.41 (0.00) -0.05*** (-5.15) -0.08*** (-27.72) Bicycle facility Shared path -9.21 (0.00) -6.95 (-1.57) -12.18*** (-33.89) Cycle track -2.44 (0.00) -2.53 (-0.67) -5.81*** (-15.59) Built environment and traffic characteristics Bike index 30.83 (0.00) 32.22** (2.23) 39.99*** (33.25) Road-connectivity index 0.12 (0.00) -0.08 (-0.23) 0.89*** (9.96) AADT less than mean/1000 1.77 (0.00) 1.61 (0.86) 3.18*** (22.71) Accessibility Measures Distance to bus stop (km) 13.92 (0.00) 13.40** (2.04) 19.37*** (38.64) Distance to CBD (km) -0.62 (0.00) 1.83*** (3.25) 1.99*** (42.39) Distance to park * cycle track (km) 3.98 (0.00) 2.23 (1.42) 3.88*** (23.17) Distance to waterbody * cycle track (km) 3.16 (0.00) -0.49 (-1.54) -0.95*** (-10.26) *** significance at 1% level, ** significance at 5% level, * significance at 10% level Log-likelihood function = -17,425.75 Adjusted pseudo rho-squared = 0.995 AIC = 34,959.5 BIC = 35,287.8 138 Table A.3. Latent Segment Allocation Results of the LSPR Bicycling Demand Model Attributes Segment 1 Segment 2 Coefficient (t-stat) Coefficient (t-stat) Constant -2.78*** (-4.72) reference Weekday -0.09 (-1.20) reference Summer 0.07 (0.80) reference Fall 0.37*** (4.31) reference Land use index 1.83** (3.52) reference Dwelling density 0.05*** (5.21) reference *** significance at 1% level, ** significance at 5% level 139 Table A.4. Parameter Estimation Results of the LSPR Bicycling Demand Model Segment 1 Segment 2 Attributes Coefficient (t-stat) Coefficient (t-stat) Constant -17.83*** (-44.40) -25.45*** (-79.66) Weather characteristics Average temperature (0C) 0.08*** (209.77) 0.08*** (309.69) Average rainfall (mm) -0.06*** (-65.33) -0.07*** (-46.00) Maximum snowfall (cm) -0.09*** (-62.90) -0.12*** (-70.49) Bicycle facility Shared path -8.22*** (-39.87) -9.65*** (-65.76) Cycle track -3.91*** (-25.35) -4.05*** (-36.62) Built environment and traffic characteristics Bike index 31.05*** (53.15) 37.41*** (85.08) Road-connectivity index 0.38*** (26.96) 0.66*** (44.53) AADT less than mean/1000 2.23*** (35.39) 2.93*** (60.25) Accessibility Measures Distance to bus stop (km) 14.43*** (49.77) 18.05*** (81.87) Distance to CBD (km) 1.54*** (63.16) 1.83*** (100.54) Distance to park * cycle track (km) 2.73*** (38.64) 3.67*** (67.13) Distance to waterbody * cycle track (km) -0.52*** (-43.14) -0.76*** (-60.44) *** significance at 1% level, ** significance at 5% level, * significance at 10% level Log-likelihood function = -34,717.97 Adjusted pseudo rho-squared = 0.992 AIC = 69,499.9 BIC = 69,694.5 140 Table A.5. Parameter Estimation Results of the NB Bicycling Demand Model Attributes Coefficient (t-stat) Dispersion parameter 0.35*** (40.24) Weather characteristics Average temperature (0C) 0.09*** (52.98) Average rainfall (mm) -0.06*** (-19.85) Maximum snowfall (cm) -0.07*** (-26.74) Bicycle facility Shared path 0.19 (1.20) Cycle track 2.03*** (7.60) Built environment and traffic characteristics Bike index 9.08*** (11.77) Road-connectivity index -0.32*** (-4.68) AADT less than mean/1000 -0.41*** (-4.31) Accessibility Measures Distance to bus stop (km) 1.68*** (9.42) Distance to CBD (km) 0.72*** (25.68) Distance to park * cycle track (km) -0.51*** (-7.37) Distance to waterbody * cycle track (km) -0.43*** (-7.49) *** significance at 1% level, ** significance at 5% level, * significance at 10% level Log-likelihood function = -18,222.07 Adjusted pseudo rho-squared = 0.791 AIC = 36,470.20 BIC = 36,549.18 141 Table A.6. Parameter Estimation Results of the PR Bicycling Demand Model Attributes Coefficient (t-stat) Constant -17.45*** (-111.91) Weather characteristics Average temperature (0C) 0.07*** (458.44) Average rainfall (mm) -0.06*** (-90.43) Maximum snowfall (cm) -0.12*** (-135.75) Bicycle facility Shared path -7.48*** (-99.75) Cycle track -3.39*** (-57.23) Built environment and traffic characteristics Bike index 30.80*** (136.40) Road-connectivity index 0.25*** (32.33) AADT less than mean/1000 2.18*** (86.62) Accessibility Measures Distance to bus stop (km) 13.73*** (124.50) Distance to CBD (km) 1.55*** (169.15) Distance to park * cycle track (km) 2.45*** (90.07) Distance to waterbody * cycle track (km) -0.46*** (-65.89) *** significance at 1% level, ** significance at 5% level, * significance at 10% level Log-likelihood function = -78,718.93 Adjusted pseudo rho-squared = 0.849 AIC = 157,463.90 BIC = 157,542.89 142 Appendix B : Bicyclists’ Speed Choice Model Results Table B.1. Latent Segment Allocation Results of the 3-segment LSLR Bicyclists’ Speed Choice Model Attributes Segment 1 Segment 2 Segment 3 coefficient t-stat coefficient t-stat coefficient t-stat Constant 1.36*** 60.58 -7.19*** -8.01 reference Trip characteristics Morning peak -0.74*** -47.84 -0.16 -0.26 reference Evening peak -0.32*** -23.33 0.75 1.57 reference Mid-day 0.08*** 2.69 3.81*** 10.50 reference Weekend 0.24*** 25.29 -0.68* -1.72 reference Summer 0.12*** 13.02 -0.73*** -2.60 reference Land use and neighborhood characteristics Land use index -0.85*** -32.73 2.96*** 3.41 reference Percentage of residential area 0.23 x 10-4 0.11 0.001 0.28 reference Percentage of commercial area -0.003*** -15.55 0.007 1.21 reference Population density 0.005*** 17.28 -0.004 -0.45 reference *** significance at 1% level, ** significance at 5% level, * significance at 10% level 143 Table B.2. Parameter Estimation Results of the 3-segment LSLR Bicyclists’ Speed Choice Model Attributes Segment 1 Segment 2 Segment 3 coefficient t-stat coefficient t-stat coefficient t-stat Constant 10.04*** 123.27 -19.18*** -5.42 0.04*** 3.93 Weather characteristics Maximum temperature (0C) -0.05*** -12.47 1.03*** 5.25 -0.02*** -38.56 Wind-chill index -0.013*** -3.42 -0.10 -0.55 0.02*** 39.83 Road infrastructure and built environment attributes Local road * mean percentage rise of elevation 0.027*** 32.23 0.69** 4.41 0.002*** 27.22 Arterial road * bike index -0.21** -2.25 23.60*** 5.36 0.64*** 42.66 Local road * bike index -1.47*** -10.82 0.86 0.17 0.58*** 26.55 Protected cycle track facility -3.36*** -41.33 46.10 0.00 8.61*** 45.45 Major collector road * bike lane length 0.30* 1.73 26.91*** 3.89 0.26*** 10.03 Local road * shared path length 5.72*** 17.00 -532.22** -2.39 -1.21*** -22.40 AADT -0.001*** -13.11 -0.05*** -3.51 0.00046*** 24.45 Accessibility features Distance to intersection 3.49*** 18.27 -25.06** -2.24 0.30*** 13.96 Distance to CBD 0.35*** 25.71 5.85*** 9.24 -0.02*** -11.22 Distance to haven location -0.16*** -9.04 -6.45*** -7.22 0.00042 0.16 Distance to park < 500 m -0.35*** -10.22 6.45*** 5.40 0.05*** 10.48 *** significance at 1% level, ** significance at 5% level, * significance at 10% level Log-likelihood function = -981,828.33 Adjusted pseudo rho-squared = 0.012 AIC = 1,963,786.74 BIC = 1,964,480.67 144 Table B.3. Parameter Estimation Results of the OLSR Bicyclists’ Speed Choice Model Attributes coefficient t-stat Constant 6.57*** 103.98 Weather characteristics Maximum temperature (0C) -0.027*** -8.45 Wind-chill index 0.004 1.44 Road infrastructure and built environment attributes Local road * mean percentage rise of elevation -0.014*** -19.47 Arterial road * bike index -0.40** -5.54 Local road * bike index 0.22** 2.07 Protected cycle track facility 1.14*** 16.02 Major collector road * bike lane length -0.71*** -5.53 Local road * shared path length 2.22*** 8.34 AADT 0.0003*** 3.65 Accessibility features Distance to intersection 0.76*** 4.37 Distance to CBD 0.27*** 24.67 Distance to haven location 0.08*** 5.49 Distance to park < 500 m -0.33*** -12.87 *** significance at 1% level, ** significance at 5% level, * significance at 10% level Log-likelihood function = -969,632.78 Adjusted pseudo rho-squared = 0.014 AIC = 1,939,291.56 BIC = 1,939,430.36 145 Table B.4. Parameter Estimation Results of the RPLR Bicyclists’ Speed Choice Model Attributes coefficient t-stat Constant 6.05*** 89.51 Weather characteristics Maximum temperature (0C) -0.003 -1.02 Wind-chill index 0.0005 0.18 Road infrastructure and built environment attributes Local road * mean percentage rise of elevation -0.01*** -20.07 Arterial road * bike index -1.06*** -14.13 Local road * bike index 0.40*** 3.78 Protected cycle track facility 0.98*** 11.79 Major collector road * bike lane length -0.92*** -6.91 Local road * shared path length 1.62*** 6.08 AADT# 0.002*** 8.70 Accessibility features Distance to intersection 1.32*** 7.93 Distance to CBD 0.15*** 13.98 Distance to haven location 0.19*** 14.11 Distance to park < 500 m -0.12*** -4.43 *** significance at 1% level, ** significance at 5% level, * significance at 10% level # random parameter Log-likelihood function = -1,060,940.72 Adjusted pseudo rho-squared = 0.001 AIC = 2,121,931.50 BIC = 2,122,198.37 146 Appendix C : Destination Choice Model Results of DBS Users Table C.1. Latent Segment Allocation Results of the 3-segment RPLSL Destination Choice Model Attributes Segment 1 Segment 2 Segment 3 coefficient t-stat coefficient t-stat coefficient t-stat Constant -17.14 -0.72 5.83*** 3.54 reference Trip characteristics Weekday trips 0.36 0.76 -0.20** -1.98 reference Off-peak hour trips -0.58 -0.72 -0.29* -1.78 reference Travel distance 0.70*** 4.26 -0.32*** -8.10 reference Transportation infrastructure characteristics of the trip origin Number of bus stops 0.002 0.09 0.03*** 4.83 reference Bike index greater than mean -0.59 -0.85 0.92*** 3.63 reference Land use attributes of the trip origin Higher percentage of commercial land use 8.94 0.38 -11.81 -0.02 reference Land use index -0.94 -0.44 -6.49*** -12.16 reference Higher percentage of residential land use -14.87 0.00 -13.66 -0.02 reference Neighborhood features of the trip origin Higher percentage of low-rise apartments 2.90*** 2.63 1.20*** 3.76 reference Dwelling density greater than mean -4.42 -0.15 12.58 0.02 reference Median income 0.00017* 1.67 0.13 x 10-4 0.37 reference *** significance at 1% level, ** significance at 5% level, * significance at 10% level 147 Table C.2. Parameter Estimation Results of the 3-segment RPLSL Destination Choice Model Attributes Segment 1 Segment 2 Segment 3 coefficient t-stat coefficient t-stat coefficient t-stat Land use characteristics of the trip destination Land use index# 26.53 0.00 -122.62*** -12.32 -50.12*** -4.78 Area of park# 3.07 0.00 -64.93*** -3.20 16.83 0.81 Lower percentage of commercial land use 21.29 0.00 6.04*** 5.92 -7.67*** -11.14 Higher percentage of residential land use -57.90 0.00 -2.59*** -3.11 14.05*** 23.06 Transportation infrastructure characteristics of the trip destination Length of cycle track 0.11 0.00 0.02*** 19.95 -0.009*** -6.00 Length of shared path -0.001 0.00 -0.0007*** -4.17 0.001*** 8.52 Length of sidewalk -0.007 0.00 -0.0002*** -2.92 0.002*** 18.95 Number of bus stops# -13.27 0.00 -5.47*** -15.81 -9.46*** -13.73 Destination at higher elevation compared to origin# -18.92 -0.01 18.15*** 11.82 18.45*** 6.02 Road-connectivity index -30.88 0.00 7.05*** 12.60 1.95** 2.18 Neighborhood features of the trip destination Employment density 0.09 0.00 -0.017*** -5.30 0.01*** 4.35 Dwelling density 0.07 0.00 0.009*** 8.80 -0.006*** -7.76 Percentage of single-detached house -1.51 0.00 -0.46*** -20.30 -0.08*** -7.06 Accessibility measures of the trip destination Distance to nearest haven less than 500 m# 1.06 0.00 -86.81*** -18.45 -56.31*** -10.98 Distance to CBD less than 500 m -45.94 0.00 4.03*** 5.38 28.33*** 20.92 Distance to midtown city center less than 500 m -55.17 0.00 -27.25*** -13.75 12.02*** 15.37 Distance to nearest bus stop less than 500 m 13.26 0.00 13.41*** 16.45 14.76*** 16.87 Distance to nearest food store less than 500 m 22.00 0.00 2.35*** 5.80 -4.23*** -10.58 Distance to nearest school less than 500 m 20.93 0.00 -1.38*** -7.58 -5.76*** -15.52 Distance to nearest retail store less than 500 m -41.92 0.00 -13.65*** -20.30 -3.76*** -10.99 Distance to nearest lake 0.005 0.00 -0.003*** -17.38 0.001*** 7.84 Distance to nearest recreational activity point less than 500 m 18.66 0.00 6.84*** 11.75 -2.10*** -7.07 Heterogeneity in mean for random parameters of the trip destination Land use index: bike index 10.03 0.00 255.19*** 14.09 92.01*** 4.95 Area of park: bike index -2.75 0.00 130.60*** 3.69 45.68 1.28 Number of bus stops: bike index 19.90 0.00 6.76*** 13.78 13.98*** 12.21 Destination at higher elevation: bike index 21.95 0.00 -26.55*** -10.83 -30.88*** -6.16 Distance to nearest haven less than 500 m: bike index -12.07 0.00 121.98*** 16.66 84.39*** 10.34 *** significance at 1% level, ** significance at 5% level, * significance at 10% level # random parameter Log-likelihood function = -5,525.7 Adjusted pseudo rho-squared = 0.657 AIC = 11,291.5 BIC = 12,113.8 148 Table C.3. Latent Segment Allocation Results of the LSL Destination Choice Model Attributes Segment 1 Segment 2 coefficient t-stat coefficient t-stat Constant 17.38*** 3.46 reference Trip characteristics Weekday trips 0.28** 2.32 reference Off-peak hour trips -0.21 -1.15 reference Travel distance 0.52*** 13.67 reference Transportation infrastructure characteristics of the trip origin Number of bus stops 0.02* 1.87 reference Bike index greater than mean 4.49*** 8.83 reference Land use attributes of the trip origin Higher percentage of commercial land use -4.39*** -5.85 reference Land use index -3.97*** -3.13 reference Higher percentage of residential land use -7.83*** -5.97 reference Neighborhood features of the trip origin Higher percentage of low-rise apartments -0.82 -0.91 reference Dwelling density greater than mean 1.00 1.38 reference Median income -0.00047*** -3.97 reference *** significance at 1% level, ** significance at 5% level, * significance at 10% level 149 Table C.4. Parameter Estimation Results of the LSL Destination Choice Model Attributes Segment 1 Segment 2 coefficient t-stat coefficient t-stat Land use characteristics of the trip destination Land use index 41.51 0.55 -2.03*** -4.46 Area of park -38.41 -0.09 6.44*** 12.21 Lower percentage of commercial land use 5.52 0.24 -0.30 -1.34 Higher percentage of residential land use -48.26 -0.50 -1.38*** -6.83 Transportation infrastructure characteristics of the trip destination Length of cycle track 0.01 0.16 -0.001*** -7.69 Length of shared path -0.02 -0.61 0.0005*** 10.84 Length of sidewalk -0.00027 -0.09 0.0004*** 19.87 Number of bus stops -1.96 -1.20 -0.09*** -6.93 Destination at higher elevation compared to origin 27.69*** 10.00 0.56*** 8.22 Road-connectivity index -5.76 -0.33 1.66*** 10.30 Neighborhood features of the trip destination Employment density 0.69 1.48 0.006*** 5.97 Dwelling density 0.0054 0.13 -0.003*** -10.65 Percentage of single-detached house -0.98 -0.32 -0.002 -0.63 Accessibility measures of the trip destination Distance to nearest haven less than 500 m -9.44 -0.15 0.84*** 7.56 Distance to CBD less than 500 m -66.35* -1.80 2.45*** 14.33 Distance to midtown city center less than 500 m 16.19 0.13 -2.75*** -15.23 Distance to nearest bus stop less than 500 m 9.34 0.08 2.00*** 11.16 Distance to nearest food store less than 500 m -14.51 -1.05 1.13*** 8.98 Distance to nearest school less than 500 m 7.01 0.18 0.08 1.29 Distance to nearest retail store less than 500 m -16.82 -0.17 -1.03*** -29.64 Distance to nearest lake -0.005 -0.77 -0.00018*** -10.16 Distance to nearest recreational activity point less than 500 m -0.32 -0.01 -1.29*** -12.16 *** significance at 1% level, ** significance at 5% level, * significance at 10% level Log-likelihood function = -9,209.5 Adjusted pseudo rho-squared = 0.429 AIC = 18,531.1 BIC = 18,914.8 150 Table C.5. Parameter Estimation Results of the RPL Destination Choice Model Attributes coefficient t-stat Land use characteristics of the trip destination Land use index# -70.65*** -29.00 Area of park# -63.69*** -10.14 Lower percentage of commercial land use -2.70*** -9.76 Higher percentage of residential land use 5.52*** 18.22 Transportation infrastructure characteristics of the trip destination Length of cycle track 0.004*** 20.50 Length of shared path -0.0008*** -14.09 Length of sidewalk 0.0001*** 4.34 Number of bus stops# -3.25*** -35.97 Destination at higher elevation compared to origin# 9.60*** 20.66 Road-connectivity index 0.80*** 4.86 Neighborhood features of the trip destination Employment density 0.02*** 15.38 Dwelling density -0.005*** -15.76 Percentage of single-detached house -0.009** -2.33 Accessibility measures of the trip destination Distance to nearest haven less than 500 m# -33.54*** -17.17 Distance to CBD less than 500 m 1.28*** 5.68 Distance to midtown city center less than 500 m 0.78*** 3.30 Distance to nearest bus stop less than 500 m 4.55*** 20.62 Distance to nearest food store less than 500 m -0.45*** -2.86 Distance to nearest school less than 500 m -0.29*** -3.88 Distance to nearest retail store less than 500 m -1.06*** -6.84 Distance to nearest lake -0.0004*** -7.99 Distance to nearest recreational activity point less than 500 m -1.53*** -11.35 Heterogeneity in mean for random parameters of the trip destination Land use index: bike index 148.21*** 31.84 Area of park: bike index 117.11*** 11.41 Number of bus stops: bike index 4.72*** 34.07 Destination at higher elevation: bike index -15.01*** -20.60 Distance to nearest haven less than 500 m: bike index 57.80*** 17.20 *** significance at 1% level, ** significance at 5% level, * significance at 10% level # random parameter Log-likelihood function = -6,597.7 Adjusted pseudo rho-squared = 0.590 AIC = 13,259.5 BIC = 13,478.7 151 Table C.6. Parameter Estimation Results of the MNL Destination Choice Model Attributes coefficient t-stat Land use characteristics of the trip destination Land use index 1.46*** 3.80 Area of park 4.69** 9.56 Lower percentage of commercial land use -0.89*** -4.83 Higher percentage of residential land use -1.14*** -5.83 Transportation infrastructure characteristics of the trip destination Length of cycle track -0.0005*** -4.88 Length of shared path 0.85 x 10-4** 2.31 Length of sidewalk 0.0003*** 18.45 Number of bus stops -0.15*** -12.49 Destination at higher elevation compared to origin 0.93*** 17.68 Road-connectivity index 0.94*** 7.69 Neighborhood features of the trip destination Employment density 0.014*** 16.45 Dwelling density -0.003*** -14.90 Percentage of single-detached house -0.007*** -2.69 Accessibility measures of the trip destination Distance to nearest haven less than 500 m 0.76*** 6.36 Distance to CBD less than 500 m 0.58*** 4.20 Distance to midtown city center less than 500 m -1.96*** -12.34 Distance to nearest bus stop less than 500 m 1.08*** 8.53 Distance to nearest food store less than 500 m 0.95*** 7.51 Distance to nearest school less than 500 m 0.23*** 4.32 Distance to nearest retail store less than 500 m -0.79*** -7.62 Distance to nearest lake -0.62 x 10-4* -1.66 Distance to nearest recreational activity point less than 500 m -1.38*** -14.63 *** significance at 1% level, ** significance at 5% level, * significance at 10% level Log-likelihood function = -10,436.02 Adjusted pseudo rho-squared = 0.352 AIC = 20,916.0 BIC = 21,066.9 152 Appendix D : Reasons to Choose DBS Model Results Table D.1. Parameter Estimation Results of the Reasons to Choose DBS MNL Model Attributes coefficient t-stat Cheapest option Number of transit stops 0.41*** 2.79 Length of shared path 2.28** 2.38 Length of bike lane 0.41 1.62 Fastest option Bicycle infrastructure 0.75*** 3.36 Density of destination locations 0.23*** 2.66 Frequent dropbike usage 2.15 1.39 Exercise purpose Bike index (BI) -0.61 -1.12 Length of sidewalk 0.77*** 3.37 Recreational purpose Land use index (LUI) 7.79** 2.28 Length of sidewalk 0.72*** 3.12 Parking constraint Alternative mode to dropbike-drive -3.94 -0.94 Dwelling density 0.03*** 3.03 Employment density -1.02 -0.68 Unavailability of other modes or other reasons Length of bike lane and cycle track 0.85** 2.35 Bike index (BI) -0.91 -0.58 No personal vehicle ownership 34.25** 2.42 Constants Cheapest mode -13.15* -1.84 Fastest option -20.50*** -2.98 Exercise purpose 0.003 0.00 Recreational purpose -5.01 -0.89 Due to difficulties in finding parking space -11.78* -1.75 Unavailability of other modes reference *** significance at 1% level, ** significance at 5% level, * significance at 10% level Log-likelihood function = -421.88 Adjusted pseudo rho-squared = 0.035 AIC = 885.7 BIC = 1,108.9
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Bicyclists’ travel behavior analysis: modeling bicycling demand, speed choice, destination choice, and usage of shared mobility services
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