Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Davanzo, Elis
Master of Applied Science - MASc
University of British Columbia
Collecting essential data through surveys is critical for shaping our understanding of travel demand, which influences important transportation planning decisions. However, conventional data collection methods face challenges, such as declining completion rates, under-representation of specific population sub-groups, confidentiality, and significant investment of resources. By employing diverse strategies in recruitment and data collection, it is possible to reach a broader and more representative sample of the population, thereby improving the overall effectiveness and accuracy of the surveys. With this motivation, this thesis examines various requirements for enhancing transportation surveys, emphasizing recruitment methods. First, an expert workshop was organized to understand the importance of transportation surveys. The event highlighted the need for a flexible and multi-faceted approach to sample collection, recognizing the dynamic nature of technology, societal preferences, and participant demographics in making informed decisions and developing effective transportation plans. Next, the implementation of a transportation survey deployed in British Columbia, Canada, is discussed, utilizing various recruitment strategies (mail, e-mail, SMS and call). Finally, the thesis presents analyses of the efficiency of each recruitment method used. To further identify factors that influence survey completion rates, an ordered logit model is developed to examine households' need for frequent communication to complete the survey, providing insights into the efficiency of different communication strategies. Additionally, a hazard model is formulated to investigate the time it takes for the households to complete the survey without frequent communication. Results indicate that both recruitment methods, mail and self-registration, were effective in creating a representative sample of the population. SMS was the most effective reminder method. For example, among the self-registrants, about 69% of households that received an SMS completed the survey on the same day, with approximately 40.5% finishing within one hour. Results also found that self-registrants, older adults and single individuals need more frequent communication to finalize the survey. In contrast, without reminders, males and residents of Metro Vancouver tend to complete surveys more quickly, while single individuals and full-time workers may require more time.
ANALYZING THE EFFICACY OF TRANSPORTATION SURVEY RECRUITMENT METHODS by Elis Davanzo B.Sc., State University of Londrina, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) August 2024 © Elis Davanzo, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: ANALYZING THE EFFICACY OF TRANSPORTATION SURVEY RECRUITMENT METHODS submitted by Elis Davanzo in partial fulfillment of the requirements of the degree of Master of Applied Science. Dr. Mahmudur Fatmi, School of Engineering Supervisor Dr. Sabine Weyand, School of Engineering Supervisory Committee Member Dr. Gordon Lovegrove, School of Engineering Supervisory Committee Member Dr. Mohammad Arjmand, School of Engineering University Examiner iii Abstract Collecting essential data through surveys is critical for shaping our understanding of travel demand, which influences important transportation planning decisions. However, conventional data collection methods face challenges, such as declining completion rates, under-representation of specific population sub-groups, confidentiality, and significant investment of resources. By employing diverse strategies in recruitment and data collection, it is possible to reach a broader and more representative sample of the population, thereby improving the overall effectiveness and accuracy of the surveys. With this motivation, this thesis examines various requirements for enhancing transportation surveys, emphasizing recruitment methods. First, an expert workshop was organized to understand the importance of transportation surveys. The event highlighted the need for a flexible and multi-faceted approach to sample collection, recognizing the dynamic nature of technology, societal preferences, and participant demographics in making informed decisions and developing effective transportation plans. Next, the implementation of a transportation survey deployed in British Columbia, Canada, is discussed, utilizing various recruitment strategies (mail, e-mail, SMS and call). Finally, the thesis presents analyses of the efficiency of each recruitment method used. To further identify factors that influence survey completion rates, an ordered logit model is developed to examine households' need for frequent communication to complete the survey, providing insights into the efficiency of different communication strategies. Additionally, a hazard model is formulated to investigate the time it takes for the households to complete the survey without frequent communication. Results indicate that both recruitment methods, mail and self-registration, were effective in creating a representative sample of the population. SMS was the most effective reminder method. For example, among the self-registrants, about 69% of households that received an SMS completed the survey on the same day, with approximately 40.5% finishing within one hour. Results also found that self-registrants, older adults and single individuals need more frequent communication to finalize the survey. In contrast, without reminders, males and residents of Metro Vancouver tend to complete surveys more quickly, while single individuals and full-time workers may require more time. iv Summary Governments worldwide face the challenge of providing essential services with limited resources. One of the most pressing issues is the aging transportation infrastructure. Informed transportation investment and policy decisions rely on objective information, and the scarcity of reliable and up-to-date data poses a critical obstacle. It is imperative to underscore that reliable data is the bedrock of informed transportation investment and policy decisions. Therefore, the continuous enhancement of transport survey methods is of utmost importance. Despite its importance, these surveys have been facing challenges, such as declining response rates and underrepresentation of population sub-groups. To counter these challenges, mixing recruitment and data collection methods can be a solution. This thesis examines multiple recruitment methods and the impact of contextual elements like socio-demographic characteristics and work arrangement on the requirements for constant communication for survey completion. The study's findings provide important insights which should assist in developing effective transportation survey strategies. v Preface This thesis is an original, unpublished work by Elis Davanzo. The author is the sole contributor to all work involved in the thesis and the writing of this thesis, under the supervision of Dr. Mahmudur Fatmi. This thesis is part of a large project led by Dr. Fatmi, which other individuals are contributing to. For example, the web-based tool used in the transportation survey was developed by Nicholas Blackwell, a software developer. The author was assisted in the development of the hazard model by Dr. Muntahith Orvin and the development of the ordered logit model by Dr. Shivam Kaddhar. Description of the use of generative artificial intelligence (AI): the author used Grammarly, Inc. Premium to review spelling, grammar, punctuation, clarity and delivery mistakes. The author also used the WordCloud Generator by MonkeyLearn to generate Figures 3.4 to 3.10. The text that fed the generator was extracted directly from the experts' participation in the workshop. The thesis was proofread by Bethany Sanjenko, Jeffery Derkach, Emilia Davanzo, and Dr. Muntahith Orvin. The Behavioral Research Ethics Board at the University of British Columbia Okanagan approved this study (Ethics ID: H23-01932). vi Table of Contents Abstract ...................................................................................................................................................... iii Lay Summary ............................................................................................................................................. iv Preface ....................................................................................................................................................... v Table of Contents ....................................................................................................................................... vi List of Tables ............................................................................................................................................... x List of Figures ............................................................................................................................................ xii List of Abbreviations ............................................................................................................................... xiii Acknowledgements ................................................................................................................................... xv Dedication ................................................................................................................................................. xvi 1. Chapter I – Introduction ............................................................................................................. 1 1.1 Background ................................................................................................................................. 1 1.2. Gaps in the Literature .................................................................................................................. 3 1.3. Research Objectives .................................................................................................................... 4 1.4. Chapter Outline ........................................................................................................................... 5 2. Chapter II – Literature Review .................................................................................................. 6 2.1. Trip-Based Surveys ..................................................................................................................... 6 2.2. Activity Time-Use Surveys ......................................................................................................... 9 2.3. Challenges with Transportation Surveys .................................................................................. 11 2.4. Methods of Recruitment and Data Collection ........................................................................... 13 2.5. Alternative Methods of Recruitment and Data Collection ........................................................ 17 2.6. Transportation Survey in Metro Vancouver and Okanagan Regions of British Columbia....... 19 2.6.1. Metro Vancouver Regional Trip Diary Survey ......................................................................... 20 2.6.2. North Shore Transportation Survey .......................................................................................... 22 2.6.3. Vancouver Transportation Panel Survey .................................................................................. 23 vii 2.6.4. Coquitlam Household Travel Survey ........................................................................................ 25 2.6.5. Okanagan Travel Survey ........................................................................................................... 25 2.7. Concluding Remarks ................................................................................................................. 26 3. Chapter III – Expert Workshop on Transportation Survey and Recruitment Methods.... 27 3.1. Introduction ............................................................................................................................... 27 3.2. Session 1 - Changing Times: New Trends in Travel Behaviour ............................................... 32 3.2.1. What are the new trends in travel behaviour and travel modes? ............................................... 33 3.2.2. What challenges and opportunities come with these new trends and travel modes? ................ 34 3.2.3. What data can be used to evaluate these changes? ................................................................... 35 3.3. Session 2 – Transportation Survey in the Era of Big Data ....................................................... 35 3.3.1. How do you use transportation survey data in your organization? ........................................... 38 3.3.2. Does big data substitute or complement transportation surveys? ............................................. 39 3.3.3. Given the changes in travel behaviour and availability of new travel modes, what new information should we collect in transportation surveys? ................................................................... 39 3.4. Session 3 – Transportation Survey Methods ............................................................................. 41 3.4.1. What are the opportunities and challenges with the following recruitment and data collection methods in these changing times? ....................................................................................................... 42 3.5. Session 4 – Demo of the British Columbia Activity Time-Use Survey (BC ATUS) ............... 44 3.5.1. Recommendations for improving BC ATUS ............................................................................ 45 3.6. Concluding Remarks ................................................................................................................. 46 4. Chapter IV – BC ATUS Recruitment Methodology .............................................................. 49 4.1. Introduction ............................................................................................................................... 49 4.2. Study Area and Sampling Technique........................................................................................ 51 4.3. Survey Instrument ..................................................................................................................... 53 4.4. Survey Implementation ............................................................................................................. 55 4.4.1. Consent Form ............................................................................................................................ 56 viii 4.4.2. Incentives .................................................................................................................................. 56 4.4.3. Recruitment and Data Collection .............................................................................................. 56 4.5. Concluding Remarks ................................................................................................................. 61 5. Chapter V – Modelling the Efficacy of Survey Recruitment Methods ................................. 62 5.1. Introduction ............................................................................................................................... 62 5.2. Descriptive Analysis ................................................................................................................. 62 5.2.1. Dataset ...................................................................................................................................... 62 5.2.2. Grouping Households Based on the Methods of Recruitment and Reminders ......................... 67 5.2.3. Group 1: Self-registrants ........................................................................................................... 71 5.2.4. Group 2: Address-based without reminders .............................................................................. 73 5.2.5. Group 3: Address-based with reminder .................................................................................... 77 5.3. Cost Analysis ............................................................................................................................ 81 5.3.1. Cost per Completion per Recruitment Method ......................................................................... 83 5.4. Modelling the Frequency of Communication to Complete the Survey .................................... 84 5.4.1. Modelling Method .................................................................................................................... 84 5.4.2. Results ....................................................................................................................................... 85 5.4.3. Elasticity Results ....................................................................................................................... 89 5.5. Modelling the Time to Complete the Survey Without Communication ................................... 90 5.5.1. Modelling Method .................................................................................................................... 90 5.5.2. Results ....................................................................................................................................... 92 5.6. Concluding Remarks ................................................................................................................. 95 6. Chapter VI – Conclusion .......................................................................................................... 96 6.1. Contributions............................................................................................................................. 98 6.2. Limitations ................................................................................................................................ 99 6.3. Recommendations and Future Work....................................................................................... 100 7. Bibliography ............................................................................................................................. 103 ix Appendices ............................................................................................................................................... 117 Appendix A ........................................................................................................................................... 117 Appendix B ........................................................................................................................................... 124 Appendix C ........................................................................................................................................... 125 x List of Tables Table 2.1 - Transportation Surveys in Metro Vancouver and Okanagan………………..........….…... 20 Table 3.1 - Characteristics of Big Data Types and Transportation Surveys ........................................ 36 Table 4.1 - BC ATUS Implementation Timeline ................................................................................. 55 Table 5.1 - Mail Drops ......................................................................................................................... 62 Table 5.2 - Household Locations .......................................................................................................... 63 Table 5.3 - Completes by Source and Region ...................................................................................... 64 Table 5.4 - Households' Socio-demographics Information .................................................................. 65 Table 5.5 - Comparison of Households’ Socio-Demographics Information with Census 2021 .......... 66 Table 5.6 - Comparison of Households’ Socio-Demographics Information Among Groups .............. 69 Table 5.7 - Comparison of Primary Respondents’ Socio-Demographics Information Among Group 70 Table 5.8 - Group 1 Information .......................................................................................................... 72 Table 5.9 - Group 1 Reminder Frequency ............................................................................................ 73 Table 5.10 - Group 1 Time for Completion per Reminder Method ..................................................... 73 Table 5.11 - Group 2 Time for Completion After the Mailing Date .................................................... 74 Table 5.12 - Group 3 Region of Residence .......................................................................................... 77 Table 5.13 - Group 3 Time for Completion After the Mailing Date .................................................... 78 Table 5.14 - Group 3 Reminder Counts and Frequency ....................................................................... 79 Table 5.15 - Group 3 Time for Completion per Reminder Method ..................................................... 80 Table 5.16 - Group 3 Time for Completion After Receiving a Call..................................................... 81 Table 5.17 - BC ATUS Wave 1 Total Cost .......................................................................................... 82 Table 5.18 - Cost per Completion per Recruitment Method ................................................................ 83 Table 5.19 - Cost per Completion per Mailing Lists ............................................................................ 84 Table 5.20 - Descriptive Statistics of the Variables Used in the Ordered Logit Model ....................... 86 Table 5.21 - Ordered Logit Model Results ........................................................................................... 87 Table 5.22 - Elasticity Results .............................................................................................................. 89 xi Table 5.23 - Descriptive Statistics of the Variables Used in the Hazard-based Duration Model ......... 92 Table 5.24 - Hazard-based Duration Model Results ............................................................................ 93 xii List of Figures Figure 3.1 - In-person Workshop Agenda ............................................................................................ 30 Figure 3.2 - Online Workshop Agenda ................................................................................................ 31 Figure 3.3 - New Trends in the Urban Context .................................................................................... 32 Figure 3.4 - Word Cloud for Session 1, Question 1 ............................................................................. 33 Figure 3.5 - Word Cloud for Session 1, Question 2 ............................................................................. 34 Figure 3.6 - Word Cloud for Session 1, Question 3 ............................................................................. 35 Figure 3.7 - Word Cloud for Session 2, Question 1 ............................................................................. 38 Figure 3.8 - Word Cloud for Session 2, Question 2 ............................................................................. 39 Figure 3.9 - Word Cloud for Session 2, Question 3 ............................................................................. 40 Figure 3.10 - Word Cloud for Session 3, Question 1 ........................................................................... 44 Figure 3.11 - BC ATUS Survey Outline .............................................................................................. 45 Figure 4.1 - Okanagan Region.............................................................................................................. 52 Figure 4.2 - Metro Vancouver Region ................................................................................................. 52 Figure 4.3 - BC ATUS Questionnaire Framework ............................................................................... 55 Figure 4.4 - BC ATUS Framework: Recruitment and Data Collection Efforts ................................... 57 Figure 4.5 - Recruitment Setup Flowchart ........................................................................................... 60 Figure 4.6 - Example of an SMS Sent to Participants .......................................................................... 60 Figure 5.1 - Households per Group ...................................................................................................... 67 Figure 5.2 - Group 2: Region of Residence .......................................................................................... 74 Figure 5.3 - Counts of Households per Duration in Days (Group 2 - Okanagan) ................................ 75 Figure 5.4 - Counts of Households per Duration in Days (Group 2 - Metro Vancouver) .................... 76 Figure 5.5 - Counts of Households per Duration in Days (Group 3 - Okanagan) ................................ 77 Figure 5.6 - Counts of Households per Duration in Days (Group 3 - Metro Vancouver) .................... 78 xiii List of Abbreviations API ………………………………………………………………... Application Programming Interface BC ATUS ………………………………………………… British Columbia Activity Time Use Survey CATI ………………………………………………………. Computer-Assisted Telephone Interview CAWI ……………………………………………………………. Computer-Assisted Web Interview CBD ………………………………………………………………………... Central Business District COVID-19 ………………………………………………………………….. Coronavirus Disease 2019 EV ……………………………………………………………………………………... Electric Vehicle FAQ ………………………………………………………………………. Frequently Asked Questions FSA ……………………………………………………………………………. Forward Sortation Area GPS ………………………………………………………………………… Global Positioning System HETUS ………………………………………………………. Harmonised European Time Use Survey HTS …………………………………………………………………….…… Household Travel Survey ICT ……………………………………………………... Information and Communication Technology IT ……………………………………………………………………………... Information Technology MAED …………………………………………………………… Mobility Activity Expenditure Diary MAG …………………………………………………………... Maricopa Association of Governments NHTS ……………………………………………………………… National Household Travel Survey QR ……………………………………………………………………………... Quick-Response Code REB …………………………………………………………………………….. Research Ethics Board xiv SANDAG …………………………………………………….. San Diego Association of Governments SMS ……………………………………………………………………………. Short Message Service SQL …………………………………………………………………..…… Structured Query Language SSH ……………………………………………………………………………… Secure Shell Protocol SSL ………………………………………………………………………………. Secure Sockets Layer TTS …………………………………………………………………. Transportation Tomorrow Survey TUS …………………………………………………………………………………... Time Use Survey UBC …………………………………………………………………… University of British Columbia UEL …………………………………………………………………….. University Endowment Lands UiTR ……………………………………………………. UBC Integrated Transportation Research Lab U.K. …………………………………………………………………………………... United Kingdom USA ……………………………………………………………………..…… United States of America VPN …………………………………………………………………………... Virtual Private Network ZEV ………………………………………………………………………….. Zero-Emission Vehicle xv Acknowledgements I want to thank my supervisor, Dr. Mahmudur Fatmi, for all his guidance, encouragement, trust, and support. Working alongside his brilliant mind over the past years has been an incredible experience, and I am immensely thankful for the opportunities he has granted me. I also thank my supervisory committee, Dr. Sabine Weyand and Dr. Gordon Lovegrove, for their feedback and guidance throughout this journey. I want to express my appreciation to Nicholas Blackwell and Mostaq Ahmed, who worked tirelessly by my side to develop and implement the survey. A special thank you goes to Dr. Muntahith Orvin for his unwavering support, guidance, and knowledge, particularly for helping with the models. I also want to thank Dr. Shivam Khaddar for his help with the ordered logit model. Bethany Sanjenko, Jeffery Derkach, and Emilia Davanzo thank you for meticulously and willingly proofreading my writing and ensuring the quality of this work. My heartfelt thanks go to all my colleagues and friends at the UiTR Laboratory, especially Bijoy, Dakota, Hellen, Nick, Shivam, and Uthpalee. I will miss our day-to-day interactions and hope our paths cross again. A very special thank you to Kristy and Wes Stevenson. This dream began thanks to your generosity in opening your home and caring for me. Thank you for the enlightening conversations, advice, and shared meals. I want to express my gratitude to my parents and my closest family. I especially thank my sister, Emilia Davanzo, for her advice and meaningful conversations, which have been invaluable. She has always been my guiding light. I also want to thank Jeffery Derkach, whose unconditional support has been a constant source of strength. Lastly, I acknowledge the funding agencies that made this project possible: Environment and Climate Change Canada under their Climate Action & Awareness Fund and the University of British Columbia Okanagan. xvi Dedication This thesis is dedicated to my sister. From playing school when we were little kids to right here, you have always been my most significant influence in life. I am glad I was born into a world where you exist. This thesis is also dedicated to my grandmother, who unfortunately passed away during my Master’s. I will always remember her as my sweet, funny and loving grandma. 1 1. Chapter I – Introduction 1.1 Background Transportation surveys are crucial for effective transportation planning and policy-making because they collect detailed information on travel behaviour, such as modes of transportation, trip purposes, travel times, and distances (Zmud et al., 2013). This data is essential for developing accurate travel demand models, which predict future travel patterns and assess the impact of various factors on travel behaviour (Bhat & Koppelman, 2003). Policymakers and planners use this information to make informed decisions about transportation infrastructure investments, public transit improvements, and other transportation-related policies. Additionally, these surveys help measure the performance of existing transportation systems, providing insights into congestion levels, service quality, and overall system efficiency (Mondschein & Taylor, 2017). They also contribute to environmental impact analyses by assisting in the analysis of greenhouse gas emissions, air quality and energy consumption (Clifton & Gregor, 2012; Shaw et al., 2024). Accurate survey data aids resource allocation, ensuring investments are directed where they are most needed (Zmud et al., 2013). Furthermore, these surveys gather information on household characteristics (e.g., location, dwelling type, income, mobility tools ownerships, etc.), individual characteristics (age, gender, education level, employment, driver's license ownership, etc.), and finally, information on trips taken by all household members. Consequently, they help identify disparities in transportation access among different demographic groups, supporting the development of equitable policies (Young & Farber, 2019). By analyzing trends over time, transportation surveys help track the effects of new policies, technologies, and societal changes, ensuring transportation systems evolve to meet the population's needs. Therefore, the continuous enhancement of transport survey methods is of utmost importance. One key application for such data is travel demand forecasting models. Traditionally, most municipalities and agencies use the four-stage travel demand model, which relies on data from household travel surveys (HTS) (Bifulco et al., 2010). In these surveys, ‘trip’ is the unit of travel, and can be defined as a journey made by a person or vehicle from one location to another, without any intermediate stops 2 (National Academies of Sciences, Engineering, and Medicine, 2012). However, advancements in transportation planning have led to the development of activity-based models, which provide a more detailed and dynamic approach to understanding travel behaviour by modelling individual activities and their associated travel patterns (Bhat & Koppelman, 2003). These models require extensive data that is difficult to obtain from traditional travel surveys. In this context, time-use and activity surveys are a better fit. While traditional surveys focus on trips, time-use and activity surveys capture all tasks performed by individuals, including in-home activities (e.g., online shopping, working from home, virtual doctors’ appointments) and out-of-home activities. The COVID-19 pandemic has significantly altered travel patterns, making it crucial to understand the interplay between in-home and out-of-home activities. During the pandemic, policies enforcing work-from-home, online schooling, and social distancing became standard practice, leading to lasting changes in daily activities. Even now, many people continue to work from home, and institutions offer online courses, while online shopping has become a routine part of life. These shifts highlight the importance of capturing comprehensive data on all activities, not just travel, to reflect current behaviours and plan for future needs accurately. Consequently, the impact of COVID-19 underscores the need for enhanced survey methods, like time-use and activity surveys, to provide the detailed data required for effective transportation planning in a post-pandemic world (Anik & Habib, 2023). Despite the importance of such surveys, several challenges might affect the data quality, including reduced response rates, proxy responses and underrepresentation of specific subgroups of the population, such as students living on campus (Verreault & Morency, 2018). Reduced response rates are exacerbated by non-responses, which can be refusals or non-contacts (National Academies of Sciences, Engineering, and Medicine, 2008). The first encompasses people who are not interested in participating. The second is people who might be open to participating but do not due to a lack of communication (e.g., interested individuals who forget to complete the survey). In this scenario, using multiple recruitment methods can alleviate some challenges by communicating with prospective participants using a method that reaches them (e.g., mail, call, SMS, etc.) and improving the chances of reaching more people. Telephone calls, mail 3 and the Internet are the most common recruitment methods. Telephone interviews are more common in North America (Sharp & Murakami, 2011), while mail is largely used in Europe (Aschauer et al., 2019). In recent years, the Internet has been steadily gaining popularity in transportation surveys (Bourbonnais & Morency, 2018; Silvano et al., 2020). Social media outlets have been used to recruit participants in some studies, with many of them concluding that through social media there is an overrepresentation of women, with many being car-less, childless and full-time employed (Silvano et al., 2020; Zhang et al., 2020). This thesis explores the efficacy of various recruitment methods used in transportation surveys, including alternative approaches (e.g., SMS and self-registration). By examining different recruitment strategies and their impact on sample characteristics, this research aims to enhance the understanding of how to improve the recruitment process for transportation surveys. The findings of this research are expected to provide valuable insights into the field of transportation research, contributing to the development of more effective survey methodologies and ultimately enhancing the accuracy and reliability of transportation data collection. 1.2. Gaps in the Literature Despite the importance of transportation surveys in transportation planning, the high cost of implementing these surveys poses a significant barrier to their widespread adoption. These surveys are crucial for gathering data on travel behaviour, preferences, and patterns, which inform decisions on infrastructure development, policy-making, and service improvements. However, the financial burden of conducting these surveys often limits their frequency and scope. The recruitment process is one of the main sources of budgetary consumption. Traditional recruitment methods, such as telephone calls and mail surveys, are labour-intensive and expensive, requiring significant human and material resources. Additionally, response rates for these methods have been declining, leading to higher costs per completed survey and raising concerns about the representativeness of the collected data. 4 Therefore, a further examination of recruitment methods is necessary to identify cost-effective strategies that municipalities and transportation agencies can feasibly implement. Several gaps persist in the current literature regarding cost-effective recruitment methods for transportation surveys. While there has been some research on alternative approaches, such as online surveys and social media recruitment, comprehensive studies incorporating them and traditional techniques are limited. Therefore, there is a need for more evidence on the effectiveness of mixed-methods approaches that combine traditional and alternative recruitment strategies. This thesis aims to contribute to the existing literature by exploring a mixed approach to the recruitment process, utilizing traditional methods, such as calls and mail, coupled with alternative methods, such as SMS, email and self-registration via social media. By examining the efficacy, cost, and response rates of different recruitment strategies, this research seeks to provide practical recommendations for transportation agencies. The goal is to identify a balanced approach that leverages the strengths of both traditional and innovative methods, ultimately leading to more efficient and cost-effective transportation surveys. Through this investigation, the thesis aims to fill the gaps in current knowledge and support the development of best practices in survey recruitment for transportation planning. 1.3. Research Objectives This thesis evaluates the efficacy of various recruitment methods used in transportation surveys and provides insights and recommendations to address challenges related to the practice of transportation data collection methodology. The scope of this thesis revolves around the following objectives: • To conduct an expert workshop to analyze the need, challenges, opportunities, and prevailing practices for transportation surveys and their recruitment methods; • To adopt diverse recruitment strategies to design and implement a transportation survey in British Columbia; 5 • To evaluate the effectiveness of different recruitment methods used in the transportation survey. 1.4. Chapter Outline The thesis is organized into six chapters, beginning with the introduction. Chapter II summarizes the literature review on the challenges and opportunities for collecting transportation data using surveys. It explicitly examines recruitment and data collection methods, including newer technologies. Moreover, the chapter explores transportation surveys implemented in the Metro Vancouver and Okanagan regions of British Columbia. Chapter III details the outcomes of an expert workshop held in 2023. Transportation engineers, planners, and industry experts shared their experiences and perspectives on transportation surveys. The future of these surveys was discussed, including efforts to increase participation. Chapter IV outlines the design and methodology of the British Columbia Activity Time Use Survey (BC ATUS), incorporating feedback gathered from the workshop to enhance its development. Chapter V presents recruitment analyses performed on BC ATUS, including descriptive analysis and statistical models. Finally, Chapter VI concludes the thesis by consolidating the key research findings. It summarizes the study's main contributions, offers a concluding overview, and proposes potential avenues for future research. 6 2. Chapter II – Literature Review In transportation studies, traditional data collection methods often involve travel surveys, which are crucial for understanding travel patterns and informing transportation planning. However, a newer approach called activity time-use surveys is gaining traction for its ability to provide more detailed and nuanced data, particularly for activity-based models. This chapter delves into the realm of transportation surveys, starting with trip-based travel surveys and then introducing the concept of activity-based time-use surveys and their potential implications for advancing transportation modelling. Many examples of transportation surveys are discussed, including efforts conducted in Metro Vancouver and Okanagan, British Columbia, Canada. A particular emphasis is placed on recruitment methods and data collection, which are most commonly used in transportation surveys. 2.1. Trip-Based Surveys In trip-based surveys, the ‘trip’ is the fundamental unit of travel. It is defined as a person or vehicle going from an origin to a destination without any intermediate stops (National Academies of Sciences, Engineering, and Medicine, 2012). Trip-based surveys have, historically, been implemented at the household level. As such, they are known as household travel surveys (HTS). These surveys have been conducted over the past several decades by many transportation organizations worldwide (Verreault & Morency, 2023) and can be understood as a “transportation census” that collects crucial socio-demographics and travel data from a random representative population sample. This data has been instrumental in shaping many transportation planning and modelling processes, such as travel demand forecasting models, infrastructure investments, ridership forecasts, mobility strategies, and more (Lawson et al., 2023; Verreault & Morency, 2023; Winkler et al., 2022). The importance of these datasets cannot be overstated. In fact, Stopher & Greaves (2007) asserted that a model and a process can only be as good as the data feeding them. The primary and most important data collected on these surveys relate to travel behaviour - how people move inside a particular region. This data is typically collected using trip diaries, which are the most 7 challenging part of a HTS. According to literature, the first trip diary was developed in the 70s in Germany and made its way to the United States of America (USA) in the 80s (Stopher & Greaves, 2007). These trip diaries collect information on the participant's origin and destination movement on a previous day, including each trip's purpose, mode, start time, and end time (Stopher & Greaves, 2007). Other variables, such as if the trip was made with a companion, can be included depending on the administrator (Aschauer et al., 2019). There are many different trip diary designs, each with its own sequence of questions. For instance, in the 2015-2016 National Household Travel Survey (NHTS) in the USA, respondents were initially asked to list the locations they travelled along with their departure times. They were then asked about the purpose of their trips, the transportation mode used, and the times when their journeys ended (Westat, 2016). On the other hand, the 2015 Edmonton Region Household Travel Survey used a cyclic trip diary design, where a set of questions was repeated for each trip performed (City of Edmonton, 2018). This complexity and variation in trip diary designs pose significant data collection and analysis challenges. Typically, a trip diary collects information on a previous weekday that is not a holiday. The NHTS, for instance, collects information from 4:00 a.m. on the assigned day through 4:00 a.m. the following day. However, there are examples of multi-day surveys, such as the U.K. National Travel Survey and the German Mobility Panel, which collected seven days of data (Aschauer et al., 2019). Many trip diary design choices make reporting all trips less burdensome while improving data quality, such as timeline visualization (Bourbonnais & Morency, 2013), short instructional text and straightforward yet visually attractive interface (Chung et al., 2021). Travel surveys typically also collect socio-demographic questions, such as income, dwelling type, level of education, etc. This information is essential for calibrating the sample's representativeness of the actual population (using census data, for example) and understanding the effect these characteristics have on a person's choice to make a trip. In fact, Winkler et al. (2022) argue that daily activities, household structure, financial situation, lifestyle, and values may influence the trip choice. 8 One aspect often present in most HTS is invitation letters, also known as advance letters, often the first step of many surveys. This letter is mailed to the sampled household at least a few days before any telephone call is placed to inform the household members that they should expect a phone call or an in-person interviewer to knock on their doors. The Transportation Tomorrow Survey (TTS), for example, mails out a letter three to five days before calling the household to gather their trip data from the immediately preceding weekday (Malatest, 2018). The NHTS in the USA has a different approach. A phone call is placed right after the household receives the letter, and the agency assigns a specific day for the trip information to be collected. The agency then places another call within six days of the assigned day and gathers the trip diary of all household members (Sharp & Murakami, 2011). Even if the household does not receive the letter, e.g., if courier issues prevent the letter from reaching its destination, the NHTS’ approach of double calling prepares the participant for the trip diary call. In contrast, TTS participants who have not received the letter could be caught off guard by a phone call. A study conducted in 2017 with TTS data from 2001 and 2006 found that 45.4% of households in 2001 and 49.5% in 2006 did not receive the invitation letter, which led to a significant decrease in trip rates and a substantial increase in phone calls needed to complete the survey (Badoe & Biney, 2017). A study conducted by TransLink during their 2008 Trip Diary Survey revealed that participants who read the invitation letter are more than twice as likely to participate in the survey, showcasing these documents' significant effect (Mustel Group, 2010). Moreover, in the case of an online survey, the letter serves as an invitation for the participant to log in to a website to complete the survey. These letters should introduce the survey in an informative and persuasive way, with the use of official letterhead and a professional layout (Gray & Guppy, 1994). They frequently contain some incentives, either direct deposit or a prize draw. In summary, HTS collects socio-demographic and travel data from a representative population sample, with households as the sampling unit (Aschauer et al., 2019). These datasets are fundamental for transportation planning, including demand forecasting models, infrastructure investment, and mobility strategies. Trip diary design and invitation letters are essential parts of a successful HTS. However, one of 9 the most significant drawbacks is the lack of in-home activities. This is particularly concerning for immobile respondents and also for the time before the first trip and after the last trip. 2.2. Activity Time-Use Surveys Time Use Surveys (TUS) gather information on activities performed throughout the day, including type and location (Aschauer et al., 2019). This information can be used in a variety of studies and different applications. For example, in the economic realm, experts can understand the monetary value of non-paid work. They can understand the division of household tasks among its members, especially between males and females. Furthermore, sociologists can use TUS to analyze the time spent with children among mothers and fathers and even with friends (U.S. Census Bureau, 2023). Transportation experts have also used TUS. For example, a study used the American TUS data to analyze activities done with companionship – also known as joint activity. This study found that these joint activities tend to have longer durations, are significantly more likely to occur at other people's residences, and are often restricted to weekdays (Srinivasan & Bhat, 2008). In Europe, the Harmonised European Time Use Surveys (HETUS) have been developing guidelines for TUS for over 25 years. For example, they recommend using mail-back as a data collection method and a time interval for reporting activities of 10 minutes. This interval mainly reduces variations in the level of detail provided by participants and facilitates the coding and data-cleaning processes (Aschauer et al., 2019). Furthermore, the European agency recommends reporting two diary days: one weekday and one weekend. However, some agencies deviate from these guidelines. For example, the 2008/2009 Austrian Time Use Survey collected information from one day using a 30-minute interval between 11 pm and 5 am and a 15-minute interval for the rest of the day (Aschauer et al., 2019). In the 90s, an activity diary was introduced in North America to create essential data to feed newer modelling techniques. This includes activity models, which are considered the most effective method for capturing individuals' travel behaviour considering their spatial, temporal, and social dimensions (Goulias et al., 2013). In this method, the focus shifts from trips to the activities undertaken by the participant, 10 demonstrating a keen awareness of in-home activities. Interestingly, evidence shows a 20% increase in trips reported compared to trip diaries (Stopher, 1992). Goulias et al. (2013) described an ideal data collection framework, focusing on the data transportation planners need to make effective policies. They concluded that the data derived from traditional travel survey design cannot estimate activity-based models. Activity diaries are believed to be more closely related to how people function daily (Stopher, 1992). Travel diaries understand trips as the end goal, while, in reality, most trips are just a way to get to the actual activity. Travelling is necessary to reach a location and engage in non-mandatory (e.g., a trip to the movies) or mandatory activities (work trips) (Aschauer et al., 2019). In this sense, trips can be understood as a derived demand. For example, a person needs to get groceries – this is the demand. They might choose to make a physical trip to the supermarket, or they might choose to complete their purchase online. In the former, one trip is happening as a derived demand for groceries. In the latter, no trip happened, but the demand was met by purchasing the products online and having them delivered to their residence. Although a trip had to happen for the groceries to get to the individual’s house (e.g., delivery services), the individual did not make that trip. Stopher (1992) reported that two main issues with travel surveys can be solved with an activity-based survey: a) the underreporting of shorter trips whose purposes are seen as unimportant and b) the misconception and different connotations of the word trip. To illustrate these two points, think of a person going to work. Midway, this person decides to stop to get a coffee at a drive-thru and get cash at an ATM. When reporting in a travel diary, this individual will most likely consider this one trip and omit the secondary stops. Most people will not report the coffee trip and the cash withdrawal trip because of the definition of a trip to them. For most people, this is just one trip. Humans are activity-oriented in how they conduct their lives and, thus, will often recall their activities more vividly than the locations or means of transportation used. Describing activities is more straightforward to convey and employs everyday language. In other words, the activity diary activates the memory of everything accomplished during a day, including the activities that require a trip to be initiated (Stopher, 1992). 11 The main effort of a time-use survey undertaken by the Government in Canada is the Time Use Survey implemented by Statistics Canada. This survey gathers data on how Canadians use their time and is conducted every five years. The most recent effort was in 2022, when they collected information on 55,000 households across 10 Canadian provinces. This survey's diary component is extensive, with 19 different categories and more than 100 specific sub-categories, making it a very time-consuming report for participants. It is estimated that the time to complete the survey was approximately 40 minutes (Statistics Canada, 2022, 2023c). Another issue is the small sample frame and the lack of regional information regarding where those 55,000 households are. 2.3. Challenges with Transportation Surveys Despite their importance, transportation surveys have faced many challenges recently. Some of these challenges are recruitment and data collection difficulties (Sharp & Murakami, 2011), reduced response rates and proxy response (Badoe & Steuart, 2002; Wargelin & Kostyniuk, 2013), acquiring a representative sample of the population (Bayart et al., 2009), growing concerns with data confidentiality and privacy (Cottrill et al., 2013; Roddis et al., 2019), and significant investment of resources (Bwambale et al., 2021; Nakamya et al., 2007). These challenges will be addressed in the coming paragraphs. Travel surveys typically aim to interview all household members of legal age of any surveyed household. The challenge lies in whether all members will individually respond to the questionnaire. In many cases, one household member takes it upon themselves to reply on behalf of all household members. This is called proxy response. In other words, an informant (self-respondent) fills in information about their and their housemate's trips. The housemates are often called non-informants or proxy respondents. It is dubious that one member would know all trips taken by their housemates, especially in large-size households, introducing the potential for underreporting trips. A study conducted by Badoe & Steuart (2002) shows a significant trip-rate discrepancy between self-respondents and proxy respondents for non-mandatory trips made by auto drivers and during off-peak hours. Furthermore, evidence suggests that the discrepancy increases with the number of vehicles owned by the household and the possession of driver’s 12 licenses by the proxy respondents. Similarly, Wargelin & Kostyniuk (2006) and Richardson (2006) also found that proxy respondents have lower trip rates than their counterparts. It was also found that trips reported by self-respondents were generally longer in distance and duration, indicating a possible underestimation of those features in proxy respondent trips (Richardson, 2006). Another challenge is the decline in response rates, which has been happening for decades. In the early 2000s, Stopher & Greaves (2007) reported that the standard response rate for telephone travel surveys in North America was 25%. Compared to more recent surveys, the response rate declined considerably. The TTS, for example, had a completion rate of 16.2% in 2016, the lowest in its history (Malatest, 2018). The literature suggests that the best response rate is found in in-person interviews (Richardson, 2006; Sharp & Murakami, 2011). This type of interaction also decreases proxy response and increases the data quality as the interviewer can encourage the interviewee to go down memory lane and remember all their trips (Richardson, 2006). Another factor contributing to the decline in response rates is that non-participating households tend to include above-average travellers and/or are typically larger. The perceived burden of the survey task is intensified for such households due to the larger number of members and the significant amount of travel that would need to be reported (Stopher & Greaves, 2007). The difficulty of obtaining a representative sample of the population, where every sub-group is well represented, has been identified as a challenge (Verreault & Morency, 2018). Studies done in previous rounds of NHTS and TTS suggest that individuals aged 18-24 are less inclined to participate directly in household travel surveys (Bose & Giesbrecht, 2004; Faghih-Imani et al., 2018). Moreover, Verreault & Morency (2018) identified an underrepresentation of students living on campus in the 2012 Sherbrooke Household Travel Survey. Growing concerns about data confidentiality and privacy have become a significant issue in transportation surveys. Cottrill et al., 2013 discuss how the adoption of smartphone-based travel surveys raises privacy concerns due to the detailed and continuous nature of data collection, which includes location tracking and personal travel behaviours. Respondents are increasingly wary of how their data will be used, 13 stored, and protected, leading to potential reluctance to participate. Similarly, Roddis et al., 2019 highlight that respondents' preferences in travel survey design are heavily influenced by their perceptions of privacy and confidentiality. The study indicates that while technology-based surveys can enhance data accuracy and convenience, they also exacerbate privacy concerns, necessitating robust measures to ensure data protection and build trust among participants. Addressing these concerns is crucial for the success of transportation surveys, as it affects response rates and the reliability of the collected data. Significant investment of resources is another major issue in transportation surveys, encompassing both financial and logistical challenges. Bwambale et al., 2021 emphasize the high costs associated with traditional travel surveys, which require an extensive workforce for data collection, processing, and analysis. Nakamya et al., 2007 also highlight the resource-intensive nature of data integration efforts, which aim to enhance the accuracy and comprehensiveness of travel behaviour indicators. Therefore, while these investments are crucial for obtaining high-quality and reliable data, which is essential for transportation planning and policy-making, they pose a substantial financial burden on organizations conducting these surveys (Bwambale et al., 2021; Nakamya et al., 2007). Typically, travel surveys are conducted during a few weeks in a particular year. Due to this, organizations face another challenge beyond their control: specificities of that particular period, such as economic or environmental crisis, transit strike, harsh weather, etc. (Verreault & Morency, 2023). For example, the 2018 Okanagan Travel Survey started on October 24th and encountered the first heavy snowfall on November 1st (Malatest, 2020a). 2.4. Methods of Recruitment and Data Collection Many challenges described above can be partially overcome by utilizing multiple recruitment and data collection methods. The idea is to give participants several options for collecting their data and to encourage participation (e.g., web-based questionnaire, smartphone app, call, mail-back, etc.) (Lawson et al., 2023). The mixed-use method increases the chance of getting a representative sample of the population while reducing the survey’s cost, but it can also have some adverse effects, such as biases (Chlond et al., 2015; 14 Dillman et al., 2009; Eisenmann et al., 2019). One example is mode-specific biases. For instance, respondents using different survey modes (e.g., online vs. face-to-face) might interpret questions differently or be influenced by the mode's unique context. This can result in inconsistencies in the data, making it challenging to compare responses across modes accurately (Chlond et al., 2015). Bonnel et al. (2015) assert four different types of biases introduced by mixing methods: coverage bias, measurement bias, non-response bias, and sampling bias. Integrating a mix of recruitment and data collection methods can yield significant benefits. However, it is essential to manage this integration process effectively. The most common recruitment and data collection methods are in-person, telephone, mail, and the Internet. In-person interviews are found to be the best in terms of response rate and quality of data. However, some downsides are the resources necessary to make it happen, monetary, time, human resources, and difficulty reaching a representative sample of the population (Sharp & Murakami, 2011). The lack of current transportation surveys using this method suggests that in-person interviews are no longer feasible. Telephone interviews have been a long-standing and prevalent method in the United States and Canada for several decades (Sharp & Murakami, 2011). Many travel surveys were conducted via telephone using a technique known as computer-assisted telephone interview (CATI). Most use the telephone as the primary or secondary method of collecting data, while others use calls to remind participants to complete the survey. In any case, calling people has become increasingly challenging. First, the number of houses with a landline has been falling for decades, making it hard to build a representative sample of the population, especially considering the younger population. Studies show that 60% of households with members aged 18 to 24 do not have a landline at home (Bourbonnais & Morency, 2018). Secondly, evidence suggests that people avoid marketing calls and calls from unrecognized numbers (Tuckel & O’Neill, 2002). Furthermore, collecting information about trips made in a day via telephone is burdensome for the interviewer and the interviewee. Studies conducted in the first decade of the 2000s reported a typical 20-30% omission of trips in travel surveys conducted by telephone, getting up to 60% of trips omitted in worst cases (Forrest & 15 Pearson, 2005; Wolf et al., 2003). Telephone interviewing can be ranked as intermediate compared to other methods regarding costs, response rate, and quality (Sharp & Murakami, 2011). The third method is mail. The complete questionnaire package might be mailed to households and returned by mail, or the agency can give the household the choice of replying to the survey via telephone or a website. This method has one of the lowest response rates and data quality, but it is one of the least expensive methods (Sharp & Murakami, 2011). Surprisingly, this is the most common method used in Europe, with a few offering telephone interviews (Aschauer et al., 2019). The Mobility Activity Expenditure Diary (MAED) survey implemented in Austria, for example, used phone calls only to motivate participants to complete the survey. However, the data collection method was only possible through mail-back (Aschauer et al., 2019). In recent years, many transportation surveys have shifted from the CATI method to computer-assisted web interviews (CAWI) (Bourbonnais & Morency, 2018). In this online surveying technique, the interviewee follows a script provided on a website. This is the fourth and last method described in this thesis. One of the most significant drawbacks of this method is that the participant loses the assistance presented in telephone and in-person interviews, given that in CAWI, they are left to complete the survey online on their own. In addition, while it might attract younger respondents, it can repel other sub-groups of the population, such as seniors and non-tech-savvy individuals (Bourbonnais & Morency, 2018). Since web-based questionnaires are more accessible to administer and less expensive to implement, they have become one of the most popular methods. However, this method has one of the lowest response rates, and the data quality is deemed intermediate (Bourbonnais & Morency, 2018; Sharp & Murakami, 2011). When given the choice of any of the above methods for data collection, participants frequently choose web-based surveys. For example, in the 2008 TransLink’s Trip Diary Survey in Metro Vancouver, roughly 62% of participants selected the web-based option, while 38% chose paper or telephone (Mustel Group, 2010). Similarly, the same survey was conducted in 2011, and participants were also given web-based or paper mail-back as data collection options. Of all households that registered for the survey, only under 8% 16 chose paper. Furthermore, the study showed that households that chose the web-based option were twice as likely to complete the survey than those who chose the mail-back option (Ipsos Reid, 2012). TTS incorporated an online completion method in 2011. Only 11% of the participants at that time chose this method. In 2016, however, 60% of all completions were completed online (Malatest, 2018). The choice of method depends on several factors, such as sample population, data needs, and financial constraints (Wolf et al., 2013). For example, the 2010-2011 Regional Household Travel Survey in the New York/New Jersey region used multiple methods to recruit and collect participant data: in-person interviews, website, telephone, mail, and GPS equipment. Participants could complete the survey using any of the methods mentioned (Wolf et al., 2013). In contrast, the Vancouver Panel Survey used only one data collection method: a web-based tool, while recruitment was done by e-mail and telephone calls (CH2MHILL, 2014). The 2016 TTS used mail, social media, and calls as recruitment methods. Furthermore, web-based and telephone calls were used as data collection methods (Malatest, 2018). The 2016 San Diego Association of Governments (SANDAG) household travel survey recruited participants via a mailed invitation letter and collected their data through a web-based questionnaire, telephone retrieval or smartphone app participation (RSG, 2017). In most cases, certain methods may be more effective at reaching specific types of households based on socio-demographic characteristics like age, income, education level, etc. (Eisenmann et al., 2019; Wolf et al., 2013). Although many benefits have been described in mixing recruitment and data collection methods, some agencies still rely solely on one method for each strategy. For example, the American Time Use Survey (ATUS) has been conducted every year since 2003 and uses mail as the recruitment method and telephone calls to collect data. An invitation letter and an information brochure are first mailed to the selected households to notify them about the survey and the date they will be contacted by telephone. The phone call is then placed to collect the data using CATI technology (U.S. Census Bureau, 2023). Since its inception in 1986, the Time Use Survey conducted by Statistics Canada has used mail as a recruitment method and telephone calls as a data collection method. Only the latest version of the survey 17 was updated: in 2022, the recruitment methods used were mail and e-mail, and the data was collected online and by telephone (Statistics Canada, 2022, 2023c). The most popular method appears to be contacting households through the mail (e.g., invitation letters) and motivating them to participate by telephone. Reminders are essential to increase response rates (Aschauer et al., 2019). A few studies investigated differences in response regarding different recruitment and data collection methods. A study, for example, compared three recruitment methods (random sample, web panel, and crowdsourcing) and two data collection methods (web-based and smartphone app) in terms of socio-demographic characteristics. The random sampling technique better matched the general population distribution by age, except for older groups. Furthermore, women were more likely to respond across all methods, but especially in the crowdsourcing method, and older individuals were more likely to use the web-based questionnaire and less likely to use the smartphone app (Silvano et al., 2020). 2.5. Alternative Methods of Recruitment and Data Collection Several alternative recruitment methods have not been extensively explored in transportation surveys, including short message service (SMS). In 2020, smartphone ownership in Canada reached 84%, with significant growth among seniors. Ownership was nearly universal among Canadians aged 15 to 44 (96%) and common among those aged 45 to 64 (87%). Among seniors, smartphone ownership increased from 43% in 2018 to 54% in 2020, highlighting a rising trend in this age group. Coupled with this, costs related to cellphone use have been falling. In 2022, Canadians paid an average of 5.2% less for cellular services than the previous year. This contrasts with the 6.8% increase in the all-items Consumer Price Index during the same period, indicating a decrease in the costs of calling, texting, and data services (Statistics Canada, 2023b). In addition, most telecommunication providers include unlimited Canada-wide calls and texts in their plans (Rogers Communications, 2024; TELUS, 2024). Although this method has not yet been widely used in transportation surveys, many health sector agencies worldwide have experimented with SMS to reduce non-response and boost survey participation. 18 For example, SMS has been confirmed to be an effective way to increase survey participation, especially among younger age groups (Tolonen et al., 2015), and decrease recruitment costs (Bracken et al., 2019). A study looked at the potential of SMS during the COVID-19 pandemic to recruit and collect survey data from young adults regarding their waterpipe smoking habits in Australia. They concluded that the text message approach is quick for data collection but has limitations, including the need for access to mobile devices and restrictions on response types – e.g., a limited number of characters. Despite this, it can offer timely information and enables longitudinal data collection without in-person interactions. Furthermore, it may benefit hard-to-reach populations, such as young people and culturally diverse communities, thereby supplementing traditional assessment methods (Chan et al., 2021). Another study conducted a Health Examination Survey, in which some participants received an SMS as a reminder about their participation, while others did not. Results show that the participation rate went up 25% for those who received the SMS, concluding that sending an SMS reminder effectively increased the participation rate, particularly among the younger age groups (Tolonen et al., 2014). One of the most significant limitations of SMS is its inability to address all areas of research, especially those requiring detailed inquiry. The questions are typically restricted to simple ordinal or categorical responses and are limited to 160 characters (Chan et al., 2021). SMS can still be utilized mainly as a cost-effective recruitment method for cases with more detailed information, such as transportation surveys. In 2007, a study conducted in Finland experimented with using SMS as a reminder for three mail surveys to increase participation. They created two groups: an experimental group (those who received an SMS as a reminder) and a control group (those who did not receive an SMS reminder). Both groups received a mail package with a questionnaire. The results show that the SMS increased participation more efficiently than the traditional mail-back approach (Virtanen et al., 2007). The use of SMS in transportation surveys has yet to be explored. In 2018, researchers in the sector were already pointing out the potential to improve response rates by sending SMS reminders for the next TTS 19 (Srikukenthiran et al., 2018). The latest TTS was implemented in 2022; however, no report has been released yet (Data Management Group, 2024). 2.6. Transportation Survey in Metro Vancouver and Okanagan Regions of British Columbia Metro Vancouver is a region that encompasses 21 municipalities in the Lower Mainland in British Columbia, Canada. It extends from Bowen Island in the Northwest to Maple Ridge and Langley Township in the East, with a population of 2,642,825 (Metro Vancouver, 2024; Statistics Canada, 2023d). For this thesis, the Okanagan region is considered to be the region extending from the City of Vernon in the North to Peachland in the South. It comprises the City of Vernon, the District of Lake Country, the City of Kelowna, the City of West Kelowna, Westbank First Nation, and the District of Peachland. The total area has a population of 266,681 (Statistics Canada, 2023a, 2023e). Table 2.1 illustrates information on transportation surveys implemented in Metro Vancouver and Okanagan regions, as defined above. Recruitment methods used in these surveys are very similar, with a combination of invitation letters sent through mail, telephone and email. The data collection methods range from paper, web-based or telephone retrieval, with many offering a combination of these options. However, the most commonly used method for data collection was web-based. For most of these reports, the response rate is not detailed. Most do not specify the number of envelopes sent or recruitment calls made. Others used different methods while reporting the completion rate. For some, the completion rate is calculated by the ratio of completes to the number of households or participants who received communication through the listed recruitment methods, including households that were contacted but never attempted to participate in the survey. TransLink uses a different technique. For example, in 2008, the company documented a completion rate of nearly 52% in its final report. However, this completion rate was calculated based on the number of households which attempted to complete the survey, including those who did not finish, rather than the total number of households contacted (Mustel Group, 2010). Completion rates are described below when the information is provided in the official 20 reports. However, this limitation should be considered when comparing these surveys. The following sessions will describe further information on each travel survey. Table 2.1 - Transportation Surveys in Metro Vancouver and Okanagan Title Year Recruitment Method Data Collection Method Metro Vancouver Regional Trip Diary Survey 20041 Telephone Paper, Online 20082 Telephone Telephone, Online, Paper 20113 Mail, Telephone Paper, Online 20174 unknown Online, Mobile app North Shore Transportation Survey 20195 Mail, Telephone Online, Telephone 20206 Email, Telephone Online, Telephone 20217 Mail, Telephone Online, Telephone Vancouver Panel Survey 20138 Telephone, Talk Vancouver Online 20149 Email, Telephone, Talk Vancouver, Mail Online 201510 Email, Telephone Online 201611 Email, Telephone Online 201712 Email, Telephone Online 201813 Email, Telephone Online 201914 Email, Telephone Online 202015 Email, Telephone Online 202116 Email Online 202217 Email, Mail, Social Media Online, Telephone Okanagan Travel Survey 201318 Mail, Social Media Online, Telephone, Paper 201819 Mail, Telephone Online, Telephone Citations on each row come from: 1 (TransLink, 2004). 2 (Mustel Group, 2010). 3 (Ipsos Reid, 2012). 4 (TransLink, 2019). 5 (Malatest, 2020b). 6 (Malatest, 2021). 7 (Malatest, 2022a). 8 (CH2MHILL, 2014). 9 (CH2MHILL, 2015). 10 (CH2MHILL, 2016). 11 (McElhanney, 2017). 12 (McElhanney, 2018). 13 (McElhanney, 2019). 14 (McElhanney, 2020). 15 (McElhanney, 2021a). 16 (McElhanney, 2021b). 17 (Malatest, 2023). 18 (Acuere, 2015). 19 (Malatest, 2019). 2.6.1. Metro Vancouver Regional Trip Diary Survey While trip diary surveys have been conducted in Metro Vancouver since the 1970s, only reports since 2004 are publicly accessible. During the 1970s, trip diary surveys were conducted only for specific municipalities, such as Vancouver and North Vancouver. The first regional survey was conducted only in 21 1985 when the Expo SkyTrain Line was under construction. In the 1990s, travel surveys were conducted in 1992, 1994, and 1999 (TransLink, 2004). The recruitment methodology used in 2004 was telephone calls only. Information on how many households were contacted by telephone is not available. However, there is data on the number of households that agreed to participate, which stands at 10,200. For this reason, the reported completion rate is high: 47.3%, with 4,824 completed surveys. Moreover, the region was divided into eight sub-areas for analysis purposes: Vancouver/UEL, Burnaby/New Westminster, North Shore, Northeast Sector, Richmond, Surrey/Delta/White Rock, Pitt Meadows/Maple Ridge, and Langley (TransLink, 2004). For the 2008 travel survey, the region was divided into 18 sub-areas: West Vancouver, North Vancouver, Vancouver CBD, Rest of Vancouver/UEL, Burnaby, New Westminster, Richmond, Port Moody, Coquitlam, Port Coquitlam, Delta, Surrey, White Rock, Maple Ridge/Pitt Meadows, Langley, Mission/Fraser North, Abbotsford and Chilliwack/Fraser South. The recruitment method was telephone calls, with approximately 245,000 calls being placed. Out of those, 40,826 households agreed to participate. The final results show that 21,052 households completed the survey. The reported completion rate for this survey was 52% (Mustel Group, 2010). However, considering the number of households contacted (245,000), the completion rate falls to 8.6%. The 2011 TransLink Trip Survey was conducted in all of Metro Vancouver, including Bowen Island and the Fraser Valley Regional District, from Lion’s Bay in the Northwest to Hope in the East. The region was divided into 52 sub-regions for this survey, much higher than the previous two surveys described above. The region was divided into sub-regions to ensure a more realistic sample for each municipality. This year, 124,553 invitation letters were sent, and phone calls were placed only for households with a listed number that did not complete the survey. In the end, 22,848 households completed the survey (Ipsos Reid, 2012), for a completion rate of 18.3%. 22 TransLink Trip Survey was also conducted in 2017 and 2023. Unfortunately, no published reports about the methodology used in either of those years exist. For the 2017 survey, TransLink only reported the approximate number of households that completed the survey (28,000), with no information on recruitment methods or the number of households contacted (Tung, 2019). Compared with previous years, the 2017 survey gathered information from a significant number of households. Regarding incentives, 175 prizes were offered as a prize draw in the 2008 survey. One prize was valued at C$1,000, four at C$500, ten at C$100, and the remaining 160 units were C$25 gift cards (Mustel Group, 2010). In 2011, every completed household received a C$10 gift card and could enter a prize draw to win one grand prize of C$1,000, eighteen C$100 cash prizes, six iPads or 80 C$25 gift cards (Ipsos Reid, 2012). 2.6.2. North Shore Transportation Survey The first North Shore Transportation Survey was implemented in 2019 and was an initiative of the City of North Vancouver, District of North Vancouver, and District of West Vancouver (Malatest, 2020b). Given that the North Shore region is included in the Metro Vancouver Regional Trip Diary Survey deployed by TransLink discussed above, it is essential to understand the differences between these two efforts. The North Shore survey differs from TransLink's in several key aspects. First, it was designed as a yearly longitudinal study, where participants provided similar information regarding their trip habits and changes every year, making it possible for the municipalities involved to better understand residents’ travel patterns. Secondly, this survey only collected information on one household member over 15 – while the TransLink counterpart collected information on all. There is no information about the number of residents contacted for the first survey in 2019. In 2020, no additional recruitment was done. Invitations were only sent to returning participants (1,571). Of those, 1,081 individuals completed the survey, resulting in a 69% return rate. In 2021, although new recruitment efforts were initiated, there is no available information regarding the number of new households contacted. The number of completions, however, is available: 2,131 completions (Malatest, 2020b, 2021, 2022a). 23 The 2019 survey offered prize draws of five C$100 and sixty C$25 gift cards. In 2020, the quantities changed to three and thirty-five, respectively (Malatest, 2020b, 2021). 2.6.3. Vancouver Transportation Panel Survey The Vancouver Transportation Panel Survey also differs fundamentally from TransLink’s trip diary survey. The effort aims to monitor advancements toward targets for transportation mode share and serves as a tool for evaluating the distance travelled by residents using vehicles in the City of Vancouver. It also aids in identifying the most successful transportation investments and policies that promote sustainable travel options, aiming to identify factors that influence changes in mode share. In the panel survey, participants are solely accountable for their trips, reducing the likelihood of underreporting (CH2MHILL, 2014). The region was divided into nine sub-regions for sampling purposes, and the target sample was 2,500 participants each year. This target was surpassed yearly (CH2MHILL, 2014, 2015, 2016; Malatest, 2023; McElhanney, 2017, 2018, 2019, 2020, 2021a, 2021b). The first survey was implemented in 2013 and has been running yearly, even during the pandemic. The last survey was implemented in 2023, between September and December (City of Vancouver, 2023a), although the report has not been published yet. Only two years provided data on the number of participants contacted: in 2013, with a completion rate of 44% (2,787 participants), and in 2021, with 76% (2,035 participants).(CH2MHILL, 2014; McElhanney, 2021b). The recruitment method used in the first couple of years was rather unique. A small part of the participants was recruited via Talk Vancouver, an online community of residents run by the City of Vancouver. The City invites members who registered online to participate in several consultation surveys (City of Vancouver, 2023b). In 2013, for example, of the 6,354 people recruited, approximately 9.5% came from the Talk Vancouver panel. This method was discontinued in 2015 due to reported self-selection bias (CH2MHILL, 2016). Recruitment also involved a commonly used technique – i.e., random probability telephone sampling. (CH2MHILL, 2014). 24 The recruitment for panels following the original from 2013 was made in two ways: a) email invitations to people who participated in prior years and b) random probability telephone sampling for target groups which were underrepresented (CH2MHILL, 2015, 2016). In 2021, amidst the COVID-19 pandemic, the agency implemented a much smaller summer panel survey, inviting only participants who completed the survey in 2020. The overall participation rates ranged from 3,382 in 2022 to a minimum of 2,035 in 2021 (McElhanney, 2021b). In 2022, the panel survey incorporated a few changes. First, telephone calls were introduced as an additional data collection method, whereas historically, web-based was the only available method. Furthermore, the recruitment method also changed. Participants from prior years were still contacted via email. However, an address-based sample was used instead of a random telephone sample, sending invitation letters to randomly selected addresses across the city. Also, a social media campaign was implemented to capture the younger population (Malatest, 2023). The incentive campaign evolved and changed over the years. From 2013 to 2015, the incentives were given as passes (e.g., City of Vancouver Flexipass, VanDusen Botanical Gardens and PNE Playland). The first year's chances of winning were 1-in-30, and the following years were 1-in-15 (CH2MHILL, 2014, 2015, 2016). In 2016, the agency introduced a more attractive incentive program to help increase the number of returning participants. One hundred C$100 gift cards were added to the passes. Considering all the prizes, there was a one in nine chance to win. The changes were incredibly effective: in 2015, only 29% of the original 2013 panel completed the survey, while in 2016, the retention rate went up to 70% (McElhanney, 2017). The incentive program continued to change in the following years. In 2017 and 2018, participants had a 1-in-10 chance of winning 245 cash prizes of various values (going from C$50 to C$1,000). In 2017, the retention rate went up to 72% (McElhanney, 2018), and in 2018, it went back to 70% (McElhanney, 2019). In 2019 and 2020, bike passes were added to the 245 cash prizes, and every participant between 18 and 24 25 years old received a guaranteed C$20 prize. In 2019, the retention rate hit the highest ever at 77% and went down to 74% in 2020 (McElhanney, 2020, 2021a). In 2021, the summer survey incentives were modest: ten C$100 cash prizes. Finally, in 2022, participants could win one of 110 prizes ranging from C$25 to C$100 (Malatest, 2023; McElhanney, 2021b). 2.6.4. Coquitlam Household Travel Survey In 2022, the City of Coquitlam hired Malatest to run its first travel survey (Malatest, 2022b). It was conducted from mid-October to mid-December 2022, and the recruitment method was invitation letters through a random address-based sample with phone calls made to households with listed numbers (City of Coquitlam, 2024). This survey was not included in Table 2.1 due to the limited information. 2.6.5. Okanagan Travel Survey The only travel survey conducted in the Okanagan region was the Okanagan Travel Survey, implemented in 2007, 2013 and 2018. However, no information regarding the 2007 survey could be found, so it was not included in Table 2.1. The survey covered various communities: Vernon, Lake Country, Kelowna, West Kelowna, Westbank, Peachland, Regional District of Central Okanagan, and the Okanagan Indian Band lands within these bounds. Like other travel surveys, the goal of the Okanagan Travel Survey was to understand and monitor the travel patterns of residents for transportation planning and policy purposes, as well as to support the regional transportation demand model (Acuere, 2015; Malatest, 2019). The survey asked for a report on all household members' (5 years or older) travel habits for 24 hours. The recruitment methods used in 2013 were invitation letters, social media, radio ads, road signs, and others, and information from 3,057 households was collected. Considering that invitation letters were sent to 92,636 addresses, the completion rate was 3.3%. The incentive program included one C$5,000, nine C$1,000, eight C$500, twenty C$100, and sixty C$25 gift cards, as well as entertainment passes (e.g., Big White, hockey season, etc.) (Acuere, 2015) 26 In 2018, recruitment was done by mailing invitation letters and calling the ones with a listed phone number. This year, 4,886 households completed the survey. The invitation letters were sent to 53,678 addresses for a completion rate of 9.1%. Furthermore, the incentives were one C$500 grand prize and one hundred C$25 gift cards (Malatest, 2019). 2.7. Concluding Remarks The chapter examined transportation surveys and the challenges they currently face. It discussed the significance of HTS data, particularly in the context of four-stage travel demand forecasting models. Time-use surveys were introduced to gather data for newer travel demand models, such as activity-based models. These models provide a more detailed understanding of travel behaviour by considering the sequence of activities individuals engage in and the travel choices made for each activity. Furthermore, various recruitment and data collection methods used in transportation surveys were explored, emphasizing their potential to address the identified challenges. The mixed-use of methods is deemed to be a cost-effective way to reach better sample representation. However, many agencies still rely on a few conventional methods, such as mail and telephone. Moreover, the lack of exploration of alternative methods was also discussed. In particular, SMS was extensively discussed as an effective recruitment method in many surveys in the health sector. Finally, the chapter discusses the current state of transportation data in British Columbia, noting a lack of time-use data in the region. This underscores the need to better understand the current use of transportation data in BC. To address this need, the next chapter will explore the findings of an expert workshop. Transportation engineers and regional planners were invited to share their experiences with HTS data. 27 3. Chapter III – Expert Workshop on Transportation Survey and Recruitment Methods 3.1. Introduction Accurate data and analysis are essential for evidence-based transportation planning and decision-making. In the era of big data, transportation experts can use a variety of datasets from many sources, such as smart cards, GPS, cellphone, etc. Despite all of this, it is argued that transportation surveys provide a unique and meaningful data set that cannot be found anywhere else (Stopher & Greaves, 2007). This is particularly interesting when contrasted with reality in the 1980s when many argued that household travel surveys were obsolete (Stopher, 1992). Despite that, an interest in collecting travel survey data reemerged in the 1990s with works such as Murakami & Watterson (1990), Purvis (1990), Stopher (1992), and many others. This chapter describes findings from an expert workshop that aimed to understand the need for surveys to collect transportation data nowadays. Moreover, the workshop covered recruitment and data collection methods that agencies in British Columbia are using. The experts who shared their knowledge were transportation engineers and planners working in municipalities, government agencies, and private companies in Okanagan and Metro Vancouver. Moreover, other transportation professionals joined, including representatives from Modo and Lime. The main objectives of this workshop were to: a) provide a forum for transportation survey discussion, including perspectives on current and future methods, challenges and benefits of each method, and future research on the topic; b) introduce the British Columbia Activity Time Use Survey (BC ATUS) and exchange feedback from stakeholders on the survey; and c) develop relationships for exchanging knowledge, promote collaboration, and learn from each other. The workshop was part of a five-year research project led by Dr. Mahmudur Fatmi and funded by Environment and Climate Change Canada titled “Integrated Development of Transport Data, Model & Community Outreach Tool for Urban & Rural Regions”. The objective of this project is to contribute to strengthening Canada’s knowledge base in order to decarbonize the passenger transportation sector. The 28 scope includes developing unique transportation data, innovative travel demand and emissions models and creating a network of academics and practitioners. The study area covers two distinct regions of British Columbia: Metro Vancouver and Okanagan, including the City of Vernon. The workshop was conducted in two phases. The first phase was held in person on May 31st, 2023, at UBC Okanagan for stakeholders in the Okanagan region. The second phase was held online via Zoom on June 7th, 2023, for stakeholders in the Metro Vancouver region. For both occasions, the workshop structure was divided into four similar sessions, during which presenters highlighted different themes and proposed a few questions, followed by an open discussion among participants. The first session highlighted new trends in travel behaviour, discussing transportation, technology, and climate changes. It also discussed behavioural changes due to the COVID-19 pandemic and its impact on the transportation sector. Participants were asked about new trends in travel behaviour and modes, challenges and opportunities caused by these new trends, and finally, what data can be used to evaluate the new trends. During the second session, the significance of transportation surveys in light of the emergence of big data was emphasized. The session aimed to understand how stakeholders use transportation surveys in their organizations and if big data substitutes or complements them. Finally, participants discussed the future of surveys in response to evolving travel behaviour, emerging travel modes, and the advent of big data. They explored innovative information and data collection methods that should be integrated into the next generation of surveys. The third session focused on different transportation survey methods. The presenters discussed transportation surveys in Okanagan and Metro Vancouver. Participants discussed the opportunities and challenges with various data collection methods used in such surveys, such as phone calls, web-based online surveys, smartphone apps, social media, and other methods, such as public interviews at events and grocery stores. 29 Finally, the British Columbia Activity Time Use Survey (BC ATUS) was presented in the fourth session. Participants provided feedback regarding the questionnaire, the survey instrument and its recruitment methods. These sessions were run in the following scheme: First, an expert gave a 15-minute presentation about the theme to be discussed. Then, the participants were divided into groups and were asked to discuss a set of questions related to the theme using an app called Slido. One group member entered the summary of the discussion in the app. In addition, a notetaker was assigned to each group. The content of this chapter is a summary of the presentations and the expert discussions during the workshop. For the in-person sessions, the groups were formed based on each participant's preference. The room was set up with round tables and eight chairs each. For the online workshop, groups were formed randomly using the automatic setup contained in Zoom. Figure 3.1 is the agenda for the in-person workshop and Figure 3.2 is the agenda for the online workshop. In the sections to come, each session is described, starting with a summary of each presentation, followed by a synthesis of the discussion related to the questions. At the end of each question, a word cloud illustrates the words that composed the raw answers, in which the size of each word indicates its frequency or importance. 30 Figure 3.1 - In-person Workshop Agenda 31 Figure 3.2 - Online Workshop Agenda 32 3.2. Session 1 - Changing Times: New Trends in Travel Behaviour This session discussed the current dynamic landscape of transportation, exploring emerging trends influenced by factors such as Information and Communication Technology (ICT), vehicle technology, COVID-19, and climate emergency. The discussion highlighted the evolving nature of travel habits and the impact of these critical factors on how people move. The global pandemic has significantly altered travel behaviour, introducing new considerations and priorities. The workshop discussed how social distancing, remote work, and housing price spikes have influenced travel preferences. It also discussed the rise of alternative travel modes, including car-sharing, shared micro-mobility (such as e-scooters and bikes), and the growing popularity of electric vehicles. These new trends are illustrated in Figure 3.3. Figure 3.3 - New Trends in the Urban Context 33 In the group discussion, participants discussed three questions about new trends in travel behaviour and modes, the challenges and opportunities these trends present, and the types of data that can be used to evaluate these changes. Below is a summary of their discussion. 3.2.1. What are the new trends in travel behaviour and travel modes? The trends in travel behaviour and modes include a significant increase in micro-mobility use, such as skateboards, rollers, e-scooters, and e-bikes, provoking a surge in demand for cycling and active mode infrastructure. Moreover, transit flexibility (e.g., on-demand transit), connectivity in cars (autonomous vehicles), real-time information, and changes in delivery patterns due to online shopping are emerging trends. The post-pandemic landscape sees a rise in work-from-home practices and a shift in travel patterns, leading to strategies to avoid congestion, such as off-peak travelling. There is also a trend toward flexible work hours. While transit usage is recovering, vehicle trips remain lower than pre-pandemic levels. Ride-hailing services are gaining traction as short-distance alternatives, and goods movement is gradually shifting towards micro-mobility. However, challenges persist, including rising living costs, safety concerns regarding active transportation, cultural attachment to cars as status symbols, and hesitancy to relinquish them. Figure 3.4 illustrates the words used in the discussion for this question. Figure 3.4 - Word Cloud for Session 1, Question 1 34 3.2.2. What challenges and opportunities come with these new trends and travel modes? The challenges and opportunities of these new travel trends and modes encompassed several vital areas. Challenges included the need for sustainable modes covering entire routes, slow implementation processes for changes in infrastructure due to bylaws, lack of progressive policies in smaller cities, and issues like internet connectivity in rural areas, which affect work-from-home arrangements. Additionally, other challenges involving vehicles were changing the car-centric culture, safety aspects and privacy concerns related to autonomous vehicles, safety concerns with vulnerable road uses, promotion of low-emission modes, security and maintenance issues with micro-mobility (e.g., bike theft), finding designated areas for shared scooters, and maintaining paths and lanes in all weather conditions. Inflexible job structures, accessibility for differently-abled individuals, affordability concerns, and improving flexibility in lower-paying jobs must also be addressed. On the flip side, opportunities are emerging from these trends. They include the potential for mixed land use developments, more investment in active transportation networks catering to new user groups, increased demand for safety infrastructure, educational opportunities regarding micro-mobility, and avenues for differently abled individuals regarding job opportunities and improved services. Figure 3.5 shows the most frequent words used in the discussion for this question. Figure 3.5 - Word Cloud for Session 1, Question 2 35 3.2.3. What data can be used to evaluate these changes? Various types of data are crucial in assessing the impact and effectiveness of these changes in transportation. This includes GPS and cellular data, speed data, and video data, although some information might not be publicly accessible. Transportation surveys, census data, and mode split data provide essential insights, while vehicle, pedestrian, and cycle counts contribute to understanding transportation patterns. Gathering detailed information about micro-mobility rides and employing eco-counter data for active modes is crucial. Furthermore, transit card monitoring for transit ridership is also essential. Other sources like apps through APIs, self-reported data, street light data, portable detectors, mental health records from work-from-home situations, and autonomous car data can also be utilized. Neighbourhood-level surveys and data on micro-mobility ownership and usage also contribute to a comprehensive evaluation. However, several challenges persist, such as infrequent major surveys not capturing rapid trends and the resistance of goods movement carriers to share data. Figure 3.6 shows the most frequent words used in the discussion for this question. 3.3. Session 2 – Transportation Survey in the Era of Big Data This session discussed the evolving landscape of transportation data sources, from traditional transportation surveys to the emergence of big data sources. Big data are passively collected data, sourced from diverse platforms such as cellphone usage, navigational apps, smartphone services, transit smart cards, Figure 3.6 - Word Cloud for Session 1, Question 3 36 and vehicle telematics, presents substantial information for analyzing travel behaviour. Table 3.1 summarizes the types of big data sources discussed and the characteristics included in each type. Travel surveys are self-reported data and the only method that can provide the most information, such as socio-demographics, mode of transport, and origin-destination matrix. Table 3.1 - Characteristics of Big Data Types and Transportation Surveys Data Type Sample Size Socio-Demographics Origin - Destination Mode of Transport Purpose of Travel 7-day Data Big Data Call Records Large No No No No Yes Cellular Data Very Large No Yes No No Yes Cellular Signaling Very Large No No No No Yes Navigation App Very Large No No No No Yes Smart Cards Moderate No No Yes No Yes Transportation Surveys Very Low Yes Yes Yes Yes No The comparison between big data and transportation surveys highlights the complementary nature of these methodologies. Big data provides extensive, real-time information from various digital sources, while transportation surveys offer detailed insights into socio-demographics and travel behaviour. The discussion was to utilize both approaches synergistically to gain a comprehensive understanding of travel patterns and preferences for effective transportation planning. The in-person workshop was followed by a presentation from Cameron Noonan from the City of Kelowna, who highlighted the pivotal role of transportation surveys in the era of big data and explained them as a comprehensive "census for transportation". The City of Kelowna performed three travel surveys, the first dating back to 2007 (Malatest, 2019). These surveys offer insights into potential changes in travel 37 behaviour by different socio-demographic groups over time and provide city planners with data unavailable from other sources. The city relies heavily on this data, using it as the primary input for its regional model, setting mode split targets for the 2040 Transportation Plan, and aiding ongoing planning endeavours. The survey data displays valuable findings, such as the population's travel patterns across age groups, mode split and travel distance (in Kelowna, for example, the data emphasizes the viability of walking and biking for short trips under 1 km), and the average distance driven. Finally, the speaker compared the merits of traditional travel surveys with big data. Travel surveys provide detailed insights even with small sample sizes, allowing for more control and transparency. In contrast, big data offers a broader but less detailed view, drawing inferences from multiple sources but often being more opaque and more complex to control. Ultimately, this session underlined the enduring relevance of transportation surveys despite the advent of big data, emphasizing their depth and clarity in providing specific, detailed insights into travel behaviour—a crucial asset for informed decision-making in transportation planning. For the online session, Nino Maclang from the City of Vancouver, Andreas Rose from Malatest and Monique Beaudry from Associated Engineering shared their experiences regarding new trends and transportation surveys in the Metro Vancouver area. In this discussion, presenters argued that a wealth of information was collected in surveys to understand residents' travel patterns. These surveys covered various data, including household characteristics (e.g., location, household income, etc.), demographics (e.g., age, gender, etc.), weekday travel snapshots, access to various transportation modes, parking situations, commuting habits, perceptions about neighbourhoods, and cycling comfort. This data complements big data from sources like cell phone traces, offering a more human-focused understanding of travel behaviour, motivations, and barriers. The presenters compared travel behaviour from the COVID-19 period, showing changes in daily trips and mode share influences, providing insights into potential mode shifts for more sustainable transportation options. For instance, in Vancouver, survey data shows that many car trips are within walkable (7%) or bikeable (13%) distances, suggesting the potential for mode shifts towards more active transportation methods (Malatest, 2023). 38 In the group discussion, participants discussed three questions related to their use of transportation survey data, their relationship with big data, and their future. Below is a summary of the discussion. 3.3.1. How do you use transportation survey data in your organization? Transportation survey data collected by organizations serves multiple purposes across various domains. Firstly, it aids in optimizing transportation resources by analyzing existing patterns and predicting future scenarios. This involves its paramount need to feed transportation models for demand forecasting and testing policies. Furthermore, the data assists in creating origin-destination matrices, equity in transit studies for route choices, and travel behaviour analysis. It is also used in modelling housing demand, real estate development, and land use. This information is crucial for analyzing how to meet targets and justifying funding for transport facilities. Moreover, the data plays a pivotal role in emissions modelling, especially for zero-emission vehicle (ZEV) planning and informing regulations, programs, and policies. It is also employed to distinguish between probability and non-probability samples and understand the factors influencing mode share. Figure 3.7 shows the most frequent words used in the discussion for this question. Figure 3.7 - Word Cloud for Session 2, Question 1 39 3.3.2. Does big data substitute or complement transportation surveys? Opinions did not vary widely in assessing the relationship between big data and transportation surveys. Big data presents advantages in larger sample sizes, reduced bias, cost-effectiveness, and flexibility in data resolution. However, concerns exist regarding the lack of specific details, such as socio-demographics. Most see big data as a complement to surveys, acknowledging their strengths and advocating for their combined use, while others have never used big data. A few believe big data can substitute surveys in future, emphasizing the need for quality assurance. The prevailing sentiment leans towards viewing big data and surveys as complementary rather than mutually exclusive. Figure 3.8 shows the most frequent words used in the discussion for this question. 3.3.3. Given the changes in travel behaviour and availability of new travel modes, what new information should we collect in transportation surveys? Given the evolving landscape of travel behaviour and the emergence of new modes of transportation, there is a collective call for enriched data in transportation surveys. This includes details on micro-mobility, such as specific bike types and mobility services (scooters, taxis, ride-hailing), and evaluating potential shifts from private vehicles to other modes. Insights on latent preferences, barriers to mode adoption Figure 3.8 - Word Cloud for Session 2, Question 2 40 (affordability, distances, transit availability), travel companions, and decisions impacted by remote work or online shopping are also sought. New data collection methods were highlighted, incorporating demographics, understanding choices and preferences, and ensuring longitudinal approaches for mode choices and equity considerations. Additionally, there is a push to capture usage patterns for e-scooters, e-bikes, autonomous and electric vehicles. Other essential aspects include the elasticity of trips, willingness to share vehicles, preferences if remote work options are available, and understanding barriers to sustainable transport. Challenges such as survey bias, underrepresentation of specific demographics, and privacy concerns persist, leading to a call for improved data accuracy, brevity, and privacy. Nevertheless, survey data remains crucial for tracking progress, modelling, understanding mode shifts among different socio-demographics groups, and the impact of transportation networks and delivery services on traditional travel. Further data collection is suggested on several key topics. These include working from home, online shopping, goods delivery, incentivizing sustainable mode shifts, exploring peer-to-peer car-sharing, addressing language barriers for immigrants, and evaluating opinions on micro-mobility and related infrastructure. There is a need for comprehensive and updated surveys to capture the evolving dynamics of modern travel behaviour and preferences. The word cloud below (Figure 3.9) shows the most frequent words used in the discussion. Figure 3.9 - Word Cloud for Session 2, Question 3 41 3.4. Session 3 – Transportation Survey Methods This session introduced different recruitment and data collection methods, including mail-in, phone calls, social media, mail-back, web-based online surveys, smartphone apps, etc. During the in-person workshop, Cameron Noonan from the City of Kelowna briefly discussed the 2018 Okanagan Travel Survey. The 2018 Okanagan Travel Survey, encompassing the Central Okanagan and the City of Vernon, targeted 50,000 households through mailed invitations and achieved a final sample of 4,800 respondents. The survey primarily focused on participants' trip diary on the preceding weekday. It encountered practical hurdles such as declining response rates compared to prior years and seasonal factors such as weather, evident from the survey starting on October 24th, 2018 and encountering the first snowfall on November 1st, 2018, which likely affected the response rate and participants behaviour. Additionally, there were concerns about the potential underreporting of short trips in the collected data. In the online version of the workshop, Andreas Rose from Malatest shared their experiences in the Metro Vancouver area regarding travel survey methodology, specifically their various recruitment strategies and challenges. Traditional methods, such as directory-listed phone samples with introductory letters, have shifted due to the decline in landlines and increased reliance on cell phones, which now have lower response rates. Address-based sampling, while comprehensive, faces challenges in reaching specific demographics and regions. Convenience samples, such as transportation or civic panels, commercial panels, emails, open surveys, and social media recruitment, are used. However, their usage raises concerns regarding representativeness and potential biases. Maintaining a representative sample is crucial. However, declining response rates, especially among younger populations and those less engaged civically, pose challenges. Older individuals participate more frequently, but generational shifts might impact future response rates. Factors, such as remote work and varying levels of trust in government, add complexity to sample representation. To counter these challenges, communication strategies must engage potential participants and highlight the survey's significance. Incentives, altruism, and support from sponsors play crucial roles in encouraging participation. 42 In the group discussion, participants discussed one question regarding recruitment and data collection methods. Below is a summary this discussion. 3.4.1. What are the opportunities and challenges with the following recruitment and data collection methods in these changing times? Phone call: While response rates, particularly on both landline and mobile, are declining and scam concerns persist, phone surveys offer efficiency and completeness, eliciting quick responses and providing an avenue to reach older demographics effectively. Incorporating translation services can enhance accessibility, especially for diverse populations. Verifying callers and ensuring availability during survey calls remains difficult. However, phone surveys offer the advantage of immediate participation and reduce the likelihood of respondents forgetting to participate. There is a preference for text-based interactions due to concerns about scams and a reluctance to spend time on calls. Furthermore, reliability issues with cellphone data for location tracking and the associated selection biases pose the challenge of accurately representing those who frequently travel. Web-based online survey: These surveys are good at targeting younger demographics and ensuring convenience, allowing respondents to participate at their own pace. However, difficulties arise in capturing data spanning multiple days and addressing the oversaturation of advertisements in online spaces, potentially hindering outreach efforts. The constraints of mobile devices often limit surveys to fewer questions, possibly underrepresenting less tech-savvy demographics. Incorporating translation services improve accessibility, yet issues persist with forgetfulness, survey length, and the need for more transparent graphics to enhance comprehension. While cost-effective and flexible, the preference for shorter surveys and the importance of incentivizing participation poses challenges. Internet access remains a requirement, although mobile-friendly websites can enhance accessibility compared to dedicated apps, catering to design flexibility and ease of sharing. Ultimately, motivating participation is a critical factor in the success of web-based surveys amidst these opportunities and challenges. 43 Smartphone apps: Instead of self-reporting through traditional phone calls and web-based tools, smartphone apps offer passive data collection. Targeting younger demographics is feasible. Constraints on mobile surveys limit questions and can exclude non-tech-savvy groups who may feel inconvenienced by downloading apps. Privacy concerns are another barrier and may lead to the potential underrepresentation of certain age groups. However, tapping into a larger pool due to widespread cellphone use is advantageous. Gamification and integrating existing apps, such as Strava can potentially increase participant response. As well, clear communication on data usage aims to alleviate privacy concerns. Educating on app usage and purposes, especially among older demographics, remains crucial amid these opportunities and challenges. Social media: Social media platforms offer cost-effective ways to distribute surveys among a larger sample, but concerns arise regarding representativeness. Additionally, the oversaturation of advertisements on these platforms may hinder outreach to the younger demographic. Constraints on mobile surveys limit participation from non-tech-savvy groups poses a significant challenge. This is compounded by personalized ad settings, potentially hindering survey exposure even further. The uneven distribution of demographics across platforms poses challenges in targeting specific groups, potentially limiting survey visibility. Despite these challenges, social media is a good entry point, enabling integration with web-based surveys and providing a platform for equitable outreach. Social media is effective in increasing survey awareness, especially among the younger population, yet faces challenges related to representation, privacy concerns, and the evolving dynamics of social media platforms. Other methods: Alternative methods, such as public interviews at different events or grocery stores, hold promise but demand significant resources. This highlights the need for hybrid approaches and inclusive strategies for less-engaged demographics. Challenges emerge due to resource intensiveness, missing demographics, and public indifference toward survey importance. Strategies involving outreach to diverse locations, such as concerts or grocery stores, aim to engage people where they are, seeking quick feedback and integrating reward programs to incentivize participation. While data collection efforts at locations like shopping centers hold promise, challenges arise due to time constraints, and the choice of data collection 44 tool. Justifying participation through options such as tax credits and emphasizing the purpose served by the collected data can help address these challenges and enhance engagement. The word cloud below shows the most frequent words used in the discussion for this question (Figure 3.10). 3.5. Session 4 – Demo of the British Columbia Activity Time-Use Survey (BC ATUS) The BC ATUS is a longitudinal time-use survey with three waves of data collection over three consecutive years (2023-2025) that will be deployed in Metro Vancouver and Okanagan regions. A panel approach has been adopted to collect data from the same individuals in all three waves, with new people being invited throughout the second and third waves to tackle attrition. This longitudinal panel will give us an understanding of the changes in travel-related behaviour over time after the pandemic. The first wave of this data collection happened in 2023, with a comprehensive description provided in Chapter IV. The data collection method adopted a web-based survey tool which collected time-use data regarding activities on the previous weekday, including in-home and online activities (e.g., work-from-home and online shopping) and travel activities (e.g., travel mode and destination). The survey also collected information on vehicle Figure 3.10 - Word Cloud for Session 3, Question 1 45 ownership and the socio-demographic characteristics of households and individuals. In addition to the web-based survey, participants were invited to provide data using a smartphone app which automatically logged the data for seven consecutive days. Figure 3.11 summarizes the survey outline. Following the presentation on the survey and the platform used to develop it, participants tested the web-based survey tool and provided valuable feedback to the team. The session ended with an open discussion on this feedback. 3.5.1. Recommendations for improving BC ATUS The audience’s feedback on the British Columbia Activity Time Use Survey effort can be summarized in four points: 1. Web-based survey: Enhance user engagement by implementing positive reinforcement strategies to make the survey more engaging and visually appealing, encouraging users to complete it. Incorporate QR codes to invite family members into the survey process seamlessly. Implement a feature indicating that information filled out for a family member is an estimate, ensuring accuracy in reporting. Include Outlook Calendar-type functionality within the survey to simplify the entry of activities and increase user convenience. Introduce a percentage completion bar to provide users with a visual indicator of their progress, encouraging them to continue with the survey. Consider making in-home activities less specific and more general to increase participation and relevancy Figure 3.11 - BC ATUS Survey Outline 46 for a broader audience. Incorporate more questions addressing how potential changes or technological improvements might influence people's mode choices, ensuring a forward-looking approach. 2. Incentives: Consider collaborating with private partners to offer cash incentives, providing a tangible reward for participants' time and encouraging broader involvement. Recognize that monetary incentives may not appeal to high-income households; explore alternative methods to attract participation. 3. Provide context and importance to participants: Enhance the survey with additional context, emphasizing the importance of participants' contributions in addressing issues such as social commitment, climate change, and modelling. Develop a mechanism to provide participants with understandable insights and survey outcomes, creating a feedback loop to highlight the value of their contributions. 4. Create Informative Video: Produce a video explaining the survey's purpose, the impact of collected data, privacy measures, and the option for participants to provide less data for privacy reasons. Address concerns about collecting personal information by providing detailed explanations in video form, fostering transparency and trust. 3.6. Concluding Remarks The chapter highlighted the importance of transportation surveys, emphasizing their pivotal role in understanding travel patterns and planning transportation. However, the consensus among participants is that traditional transportation survey methods must evolve to stay relevant in the face of emerging trends. Acknowledging the decline in participation in self-responded surveys, the workshop brainstormed actionable strategies to revitalize engagement. The proposal to explore satellite surveys, especially in public events, and the emphasis on enhancing incentives, personalization, and trust-building measures are essential steps in addressing this challenge. The workshops were divided into four sessions and conclusions for each session are described below. 47 Session one explored the evolving nature of travel habits, particularly emphasizing the significant influence of social distancing, remote work, online shopping, and changing housing dynamics brought about by the global pandemic. The rise of alternative travel modes, including car-sharing, micro-mobility options, and the growing adoption of electric vehicles, was discussed as pivotal elements shaping the future of transportation. The group discussion further enriched the discourse by elucidating emerging travel behaviour and modes trends, highlighting the challenges and opportunities associated with these shifts. The complexity of evaluating these changes was underscored, emphasizing the diverse range of data sources, from GPS and cellular data to surveys and mode split data, necessary to assess the impact and effectiveness of evolving transportation trends comprehensively. Session two provided a comparison between traditional surveys and big data sources. While big data offers a broader but shallower perspective, transportation surveys contribute detailed insights into the travel behaviour of different socio-demographic groups, playing a crucial role in informed decision-making for transportation planning. The group discussed the various uses of survey data, from transportation modelling and optimizing transportation resources to emissions modelling and real estate development. The discussion on the relationship between big data and surveys revealed a consensus that they are complementary. Additionally, participants emphasized the need for enriched data in surveys to capture the nuances of evolving travel behaviour, new modes of transportation, and factors influencing mode adoption. Challenges such as survey bias and privacy concerns persist. However, there is a collective call for comprehensive, updated surveys to adapt to the dynamic nature of modern travel preferences and behaviour. Overall, the session emphasized the ongoing significance of transportation surveys in the era of big data, emphasizing their role as indispensable tools for shaping the future of transportation planning. Session three discussed the challenges and opportunities of different methods of data collection, ranging from traditional approaches like mail-in and phone calls to modern techniques such as social media and smartphone apps. Key considerations included declining response rates, demographic shifts, privacy concerns, and the evolving dynamics of technology and social media platforms. The importance of 48 maintaining representative samples, incentivizing participation, and adapting communication strategies emerged as common themes. The session highlighted the need for a flexible and multi-faceted approach to sample collection and recruitment, recognizing the dynamic nature of technology, societal preferences, and participant demographics in shaping effective transportation planning and decision-making. Finally, session four introduced the BC ATUS, a three-year longitudinal study to capture changes in travel-related behaviour post-pandemic in Metro Vancouver and Okanagan. The participants completed the survey using the web-based tool developed by the team and provided important feedback. The audience's feedback highlighted enhancing the visual appeal and engagement of the web-based survey, considering various incentive strategies, providing context and importance to participants, and creating an informative video to address privacy concerns. These recommendations underscore the collaborative effort to optimize data collection efficiency, encourage broader participation, and establish transparency, emphasizing the significance of participants' contributions to address societal issues and inform future transportation planning. In summary, the workshop has reaffirmed the significance of transportation surveys and laid a roadmap for their evolution. By adopting progressive methodologies and implementing strategies to enhance participation, the future of these surveys appears promising, ensuring their continued utility as practical tools for capturing the dynamic and evolving nature of travel behaviour. 49 4. Chapter IV – BC ATUS Recruitment Methodology 4.1. Introduction This chapter describes the recruitment and data collection of the British Columbia Activity Time Use Survey (BC ATUS). The BC ATUS is part of the Integrated Development of Transport Data, Model & Community Outreach Tool for Urban & Rural Regions project, led by Dr. Mahmudur Fatmi and five other faculty members from UBC Okanagan and Vancouver and funded by Environment and Climate Change Canada (ECCC). This interdisciplinary project focuses on adopting an integrated approach to collect newer data and developing a novel modelling system to better predict travel behaviour, congestion and transportation-related GHG emissions. Specifically, the focus is to develop an agent-based transportation tool that will be capable of simulating every agent (e.g., individual and household) in Okanagan and Metro Vancouver and their decisions related to land use (e.g., residential location choice), travel tool ownership (e.g., vehicle ownership), daily activities including in-home activities (e.g., work-from-home and online shopping) and out-of-home activities (travel to work and shopping), and lastly, demographic dynamics (e.g., age, income and gender). The reason for developing such a large-scale integrated urban model is the inter-dependencies among several transportation-related decisions. For example, where to live directly impacts the decisions of how many vehicles to own, which consequently influences what mode to travel with, the distance travelled daily and, eventually, the carbon footprint. Furthermore, recent advancements in information and communication technology, such as smartphones, fast data services, and the COVID-19 pandemic, have increased the choice-making context. This includes replacing or complementing many out-of-home activities with in-home/online activities (e.g., instead of travelling to work daily, one might be able to work from home twice a week). Therefore, given the inter-dependency between in-home and out-of-home activity decisions, the model must better accommodate in-home activities to predict travel and emissions. 50 Developing a disaggregate-level model that operates for each individual in a region and at the micro-spatial resolution of the parcel (i.e., property lot) requires fitting disaggregate-level data to represent agents' behaviour accurately. The BC ATUS survey is motivated to generate this disaggregate-level behavioural data by utilizing an activity-based time-use 24-hour diary. The BC ATUS is a longitudinal survey collecting data in three waves over three consecutive years. A panel approach has been adopted to collect data from the same individuals in all three waves, with new invitations also taking place in the subsequent years. This longitudinal panel will give us an understanding of the changes in travel-related behaviour over time. This thesis describes the development and implementation of the first wave, which was administered in 2023. The primary method for data collection was a web-based tool which mainly collected time-use data regarding activities on the previous weekday, including in-home/online activities (e.g., work-from-home and online shopping) and travel activities (e.g., travel mode and destination). The survey also collected information on vehicle ownership and the socio-demographic characteristics of households and individuals. Information on all household members aged 15 years and above was collected. However, the primary respondent had to be 18 years old and above. This person was responsible for replying to all questions regarding the household and their activity log. They could either get the other members' consent to respond on their behalf or invite them to respond themselves about their daily activities’ information only. There are limited sources of similar data in the regions of the study, such as the Okanagan Travel Survey, the Vancouver Panel and the TransLink Trip Diary Survey. However, they all follow the trip-based approach. Additionally, most of these datasets were not available due to privacy policies. For example, TransLink, the Transportation Authority responsible for the Metro Vancouver area, has conducted a trip diary survey since 2004. However, these datasets are not available to the public. The 2018 Okanagan Travel Survey results are available but outdated, and it would be difficult to develop models considering post-pandemic trends. Moreover, these surveys did not include activity diaries, which are essential to create disaggregate-level behavioural data. Any other data sources outside the study regions, such as the Toronto 51 Transportation Tomorrow Survey, were discarded because their samples were not local, and their utilization would result in issues with spatial transferability. Using data from other cities, regions, or countries to develop transportation models for a particular place is questionable due to significant variability in transit systems, transportation policies, culture, and attitudes toward transportation modes (Roorda, 1998). Finally, census data was also deemed unusable due to the lack of household-level information in a more disaggregated manner and the lack of activity time-use data. Other initiatives discussed in Chapter II, such as the North Shore and Vancouver Panels, targeted specific cities and thus would not be appropriate for this research. 4.2. Study Area and Sampling Technique The survey area included Metro Vancouver and Okanagan. For this study, the Okanagan region (Figure 4.1) includes Vernon, Lake Country, Kelowna, West Kelowna, Westbank First Nation, and Peachland. Metro Vancouver region (Figure 4.2) includes North Vancouver District, North Vancouver City, West Vancouver, Vancouver, Burnaby, New Westminster, Coquitlam, Port Coquitlam, Port Moody, Maple Ridge, Pitt Meadows, Richmond, Delta, Surrey, White Rock, Langley Township and Langley City. Addresses were drawn from a database of mailable addresses in the study area. The final sampling plan included a mixed sampling approach involving address-and-phone and address-only samples. This approach was deemed the optimal means of maximizing the representativeness and response rate while keeping within a budget, given that providing a phone call to every prospect would be costly. The address-only sample was purchased from Canada Post, and the address-and-phone sample was purchased from the Canadian-based survey sampling firm ASDE. Two standard procedures for sampling phone numbers are directory sampling and random digit dialing. Directory sampling is not available for cellphone numbers, and given that landlines are in decline, it was essential to have a cellphone sample. In 2019, nearly half of Canadian households had only a cellphone and no landline, a significant increase from 2010 (Statistics Canada, 2023b). This trend is most pronounced 52 among younger age groups, with almost 85% of households under 30 reporting no landline. In contrast, only 15% of households with members aged 65 and older reported having only a cell phone. Figure 4.1 - Okanagan Region Figure 4.2 - Metro Vancouver Region Westbank First Nation 53 The second option involves dialling random numbers until a working number is found. This approach is very time-consuming, and even when a working number is found, it is hard to determine if it belongs to someone living in the study area. ASDE was deemed a good option in this context as they generate samples of cellphone numbers using a provided list of dedicated cell phone exchanges with a connection rate of 55%. Moreover, residential addresses were associated with each number, allowing the invitation letter to be sent before any phone call. However, it is hard to know if those addresses were updated (ASDE, 2024). For this reason, a thorough analysis was performed to confirm if all addresses sent by ASDE were residential. In total, 11,617 cellphone numbers with addresses belonging to Metro Vancouver or Okanagan were used. The advantage of the address-based (including address-only and address-and-phone) sampling approach is that it increases dwelling coverage, mainly providing better coverage of apartments (multi-family units). The samples were drawn randomly within the survey geographies, organized by postal code forward sortation area (FSA) or postal codes for areas requiring more precision. The samples were further stratified into apartment and non-apartment buildings to meet targets by dwelling type according to census distributions. In addition, self-registrants had access to the survey by pre-registering on the website. These participants mostly came from social media outlets, where posts were made every week on Instagram, Facebook and LinkedIn, inviting the community in the study area to participate. After a quality control process, e-mails with unique access codes were sent to these participants. 4.3. Survey Instrument The web-based survey tool used for the 2023 BC ATUS cycle was developed in-house and followed the CAWI technique or computer-assisted web interview. This was the only data collection method, and the participant followed a script in the tool. The tool supported both computers and mobile devices, providing flexibility to the participants. The necessity of developing a survey tool is twofold: a) a growing 54 need to reduce respondent burden to increase response rates; b) the available resources (e.g., Qualtrics) did not have enough features to create an easy and interactive activity diary. Extensive work was undertaken to integrate user-friendly tools, such as auto-fill, address lookup and map lookup. This tool was tested by many graduate researchers and during two expert workshops, as described in Chapter III. Many of the feedback received during the workshops were added to this survey instrument, such as incorporating QR codes as an invitation method to seamlessly involve family members in the survey process. Participants could log in and complete the survey online via a secure website developed and hosted internally at UBC (https://bcatus.ok.ubc.ca/). The website was mobile-friendly and also had Frequently Asked Questions (FAQs), information about the survey, and contact information for additional questions or support completing the survey. The questionnaire was divided into four sections, as described below and in Figure 4.3. a) household characteristics, including place of residence, dwelling type, household size, and household income, among others; b) household mobility tools, including automobiles, bicycles, scooters or their electric forms, preferences for electric vehicles, etc. c) individual member's details, including age, gender, education, main occupation, personal income, workplace address, work arrangement, etc. d) a 24-hour activity log, where participants reported their daily activities performed on a previous typical day, including in-home (e.g., working from home, online yoga classes) and out-of-home (grocery shopping, work, etc.) activities. Regarding privacy issues, the data was stored exclusively in a password-protected MySQL database in the UBC server, housed in a secured-locked facility at UBC Okanagan. The only access point to the server was through SSL-encrypted web pages (survey app). Access was logged and monitored. The server was 55 updated and maintained regularly using industry best practices. Internal network access was only available to the software developer and IT Services members on campus or through VPN into the university network (encrypted SSH access). The web-based survey tool was designed to prevent bots and malicious scripts from attempting to manipulate the application. The access codes also function as an additional layer of safety, preventing unauthorized submissions. 4.4. Survey Implementation Careful design and management were necessary for a survey of this magnitude to be successful. The initial plan and design started several months before the launch. Table 4.1 provides a timeline for the process. Table 4.1 - BC ATUS Implementation Timeline Task Dates Study design January – October 2023 Web-based tool January – October 2023 Ethics Application May – October 2023 Recruitment and Data Collection October – December 2023 Figure 4.3 - BC ATUS Questionnaire Framework 56 The first task focused on the initial planning and design of the entire study, including the hiring of a mail provider, a marketing company and a telemarketing company, the development of the questionnaire and the development of all study materials (e.g., invitation letter, communication with participants, social media posts ideas, etc.). Any research involving humans conducted by UBC personnel must be reviewed and approved by the UBCO Research Ethics Board (REB). The REB certificate number is included in the Preface. 4.4.1. Consent Form The survey's cover page contained information about the administrator, incentives, instructions, and the consent form. As Chapter III stated, privacy and consent concerns are among the most significant challenges. For this reason, an extensive consent form was catered for this survey. The cover page, including the consent form, can be found in Appendix A. Before starting the questionnaire, participants were asked to provide their e-mail addresses and phone numbers. This contact information was used for reminders to complete the survey for those households that started the survey but did not complete it and to invite them to participate in the subsequent survey waves. 4.4.2. Incentives Given the high response burden caused by the large amount and the complexity of the information required, an incentive strategy was implemented. Following the end of the questionnaire, participants were asked to add their e-mails if they wished to participate in prize draws to win gift cards. Participants had a 1-in-20 chance of winning a C$100 gift card. In addition, two C$2,000 gift cards were given to two participants (one for Metro Vancouver and the other for Okanagan). 4.4.3. Recruitment and Data Collection The recruitment and data collection were conducted between October 26th and December 22nd, 2023. Figure 4.4 illustrates the framework. The recruitment was made in two steps: the initial recruitment was made via mail and self-registration, and the second was a series of reminders via call, e-mail and SMS. The survey was invitation-only, and participants with a unique access code could respond. Residents could be 57 invited by receiving an envelope through mail or self-registering. Once registrants had an access code, calls, SMS, and e-mails were used as reminders whenever possible. These reminders were important to improve the response rate. Calls were placed to households where a phone number was available. The same happened to SMS and e-mail. Sending SMS was always preferred over e-mails. These methods happened concomitantly. Calls started on November 1st, 2023, and ended on December 5th, 2023; SMS started on November 1st, 2023 and ended on December 14th, 2023; and e-mails were sent between November 4th, 2023 and December 18th, 2023. The only data collection method used was online. An invitation letter and an information brochure were mailed to the selected households for the address-based recruitment. The invitation letter contained the web link to the online tool and a unique access code for the survey. It also contained a phone number that participants could call if they had any questions or wanted to verify the source of the survey. The information brochure contained more specific information about the survey, security, prizes, etc. A focus was given to the appearance of the envelope to avoid being mistaken for junk mail. The envelope contained a UBC logo to increase the chances of prospective participants opening the mail. Figure 4.4 - BC ATUS Framework: Recruitment and Data Collection Efforts 58 Appendixes B and C contain the invitation letter and brochure templates for Okanagan residents. We also had versions for Metro Vancouver residents, with minor changes in the text and images. All letters were addressed to ‘resident’ rather than the name associated with the address because it was understood the name could be incorrect given that residents could have moved out recently. The intention behind the invitation letter was to succinctly outline the study's purpose and rationale for the respondent. It aimed to prepare the household for the upcoming questions, emphasize the strict confidentiality of the gathered data, and notify the household that participation made them eligible to win a C$2,000 or a C$100 cash prize upon completing the survey. Perhaps the most critical information in the invitation letter was an access code. Each household had a different access code, and the survey was only available upon receiving one. The letter also included a link to the online survey and a QR code that participants could read to access their questionnaire page. Additionally, the letter included contact information for the survey administrator, offering a channel for any inquiries about the survey. A brochure was included in the envelope with the invitation letter, and explanations about the survey and an FAQ session were included. Often, such address-based and landline-based samples have a low response rate from some specific groups of the population, such as younger adults. Social media (e.g., Facebook, LinkedIn, Instagram, and Twitter) targeted these demographic groups to fill this gap. A marketing expert company was hired to create posts and boost engagement on social media outlets. Moreover, an informative video was created following feedback from the Expert Workshop described in Chapter III. The video covered the survey's purpose, the impact of collected data, privacy measures and how to participate. The key goal of all social media communication efforts was to generate excitement, compel participation, and ensure a sense of security and privacy for participants. Many other agencies have utilized social media, which yielded good results. For example, the Maricopa Association of Governments (MAG) hired an expert company to implement a strategic social media campaign. They first identified the target population and created a campaign to appeal to it. The logo, for example, features a dog inside a car, appealing to the fact that many residents in the region enjoy the company of their pets while riding in their vehicles (Lawson et al., 2023). 59 For people recruited through social media, they were asked to pre-register. In the registration process, home addresses, e-mail addresses, and basic socio-demographic information, such as age and gender, were asked. At this stage, asking for location and other information is done to prevent people from the same household from filling out the survey twice. Once the information was verified, a unique code access was provided via e-mail. In addition, partner agencies were encouraged to promote the survey through their existing social media accounts (i.e., Facebook, Instagram) and other platforms such as e-newsletter lists. Although all partners received communication, only BC Transit actively shared posts on their Instagram account. Research evidence shows that a single invitation to prospective respondents is not sufficient. Repeated invitations, or reminders, can greatly influence the likelihood of replies (Gray & Guppy, 1994). The recruitment followed a multi-step approach. The first step, detailed above, was followed by a series of calls, SMS, and e-mail reminders to increase the chances of a household completing the survey. The number of reminders, whichever type, was limited to five (i.e., a household would only receive up to five attempts at communication). Figure 4.5 illustrates the recruitment process. A telemarketing company was hired to make the calls, which were made on weekdays and weekends, from 10 am to 8 pm. For this process to work effectively, the team had effective communication between the mail provider and the telemarketing company so a call would never be placed to a household that did not receive the mail or had already participated in the survey. The callers would attempt to contact households and ask them to complete the survey. This telemarketing company was also responsible for the hotline prepared for participants who had any questions or wanted to verify the source of the survey. 60 The hotline was open also from 10 am to 8 pm. Outside this window, a virtual machine informed callers they could call back for inbound support between 10 am and 8 pm daily. In addition, households with listed contact information were sent reminders via SMS and e-mails. These reminders included the link for the survey with the access code embedded for easiness of use. Figure 4.6 illustrates an example of an SMS sent to participants. SMSBCATUS: You should have received a letter from UBC inviting you to participate in the British Columbia Activity Time Use Survey.Please consider completing the survey online at [LINK WITH ACCESS CODE] for the chance of winning a $100 gift card and a grand prize of $2,000.If you have any questions, please send us an e-mail at uitr.lab@ubc.caFigure 4.6 - Example of an SMS Sent to Participants Figure 4.5 - Recruitment Setup Flowchart 61 4.5. Concluding Remarks The first wave of the BC ATUS was developed based on standard practices discussed in the literature review. For example, the data collected contained aspects such as household details (e.g., household income and location), individual characteristics (e.g., age and gender), and a 24-hour activity log. Feedback from the workshop described in Chapter III was also incorporated. For example, the survey implemented a mixed-method approach in terms of recruitment methods. Many methods were incorporated: mail, online, phone calls, e-mail, and SMS. Also, an informative video was developed and incorporated into the social media campaign to create awareness and give a sense of security of privacy matters. Many features to engage participants and facilitate the data collection were implemented, such as QR codes and a calendar-style activity log. Furthermore, a robust incentive campaign was put into place to encourage participation. 62 5. Chapter V – Modelling the Efficacy of Survey Recruitment Methods 5.1. Introduction This chapter analyzes the recruitment methods deployed in the British Columbia Activity Time Use Survey. The survey was conducted online, utilizing various recruitment channels, including mail, SMS, phone calls, email, and social media. The study area was Metro Vancouver and Okanagan. This chapter is divided as follows: first, a descriptive analysis of the data collected including different methods of recruitment/reminders is presented. Next, an ordered logit model is presented to explore the attributes influencing the required number of communications for a household to complete the survey. Finally, a hazard model explores the relation between attributes and the duration for households to complete the survey without any reminders. 5.2. Descriptive Analysis 5.2.1. Dataset For the BC ATUS survey, the primary method for sending invitation was using mail services. Two lists of addresses were purchased: one with addresses only (n=38,131) and another with addresses and matched phone numbers (n=11,617). Invitation letters with a unique access code were sent to 49,748 households in the study area in multiple drops, as described in Table 5.1. Table 5.1 - Mail Drops Drop Date Number of letters Okanagan Metro Vancouver 1 2023-10-26 4,122 1,638 2,484 2 2023-11-02 5,102 2,177 2,925 3 2023-11-02 10,000 4,000 6,000 4 2023-11-09 10,000 4,000 6,000 5 2023-11-16 14,672 5,798 8,874 6 2023-11-29 2,393 964 1,429 7 2023-12-11 3,459 1,287 2,172 Totals 49,748 19,864 29,884 63 The survey gathered information on 2,067 households across both regions after cleaning. Of those, 43.2% (892) were in the Okanagan region and 56.8% (1,175) were in Metro Vancouver. In the Okanagan, as expected, more than 60% of households were situated in Kelowna and 16% in Vernon, the two most populated cities in the region. In Metro Vancouver, more than 42% of households were located in Vancouver and 12% in Surrey. Table 5.2 reports the distribution of the locations of the participating households. Table 5.2 - Household Locations Region Counts % Okanagan Total 892 100.0% Kelowna 569 63.8% Vernon 144 16.1% West Kelowna 104 11.7% Lake Country 41 4.6% Peachland 26 2.9% Westbank First Nation 8 0.9% Metro Vancouver Total 1,175 100.0% Vancouver 496 42.2% Surrey 142 12.1% Burnaby 102 8.7% North Vancouver 70 6.0% Richmond 64 5.4% Coquitlam 56 4.8% Delta 43 3.7% Langley 41 3.5% Others 161 13.7% Table 5.3 summarizes the overall data collection by the recruitment source (mail and self-registrant). The best source was Address Only, with 65.9% of completed households. This is easily explained by the number of addresses purchased for Address Only (38,131) vs Address & Phone (11,617). However, the completion rate is slightly higher for the Address & Phone list (4.06%) than the Address Only list (3.57%). Approximately 90% of the completed households originated from mailing, indicating that it is an effective 64 recruitment method. Nevertheless, self-registrants proved to be a helpful tool. In total, 569 self-registrants were invited to the survey, with 233 of them completing it, totalling a completion rate of 40.95%, which is the ratio of the latter to the former. Table 5.3 - Completes by Source and Region Source Total (n=2067) Okanagan (n=892) Metro Vancouver (n=1175) Counts % Counts % Counts % Address & Phone 472 22.8% 240 26.9% 232 19.7% Address Only 1362 65.9% 618 69.3% 744 63.3% Self-registration 233 11.3% 34 3.8% 199 16.9% Many agencies calculate completion rates differently. TransLink, for example, only considers the households that attempted to complete the survey; those that were invited but never attempted to complete it were unaccounted for (Mustel Group, 2010). The best way to calculate completion rates using TransLink’s methodology would be to look for all access codes that were never activated. In total, 47,691 access codes remained inactive. This means that 47,691 households did not even attempt to log in to the survey tool. Therefore, only 2,626 households attempted to complete the survey, indicating a completion rate of 78.7% (ratio of 2,067 to 2,626). Considering that self-registrants are likely from the social media campaign it is coherent that the most significant sum of participants from this source is from Metro Vancouver. In fact, 80% of social media interactions came from Metro Vancouver, indicating that residents of that region are more active on social media platforms (e.g., Facebook, Instagram and LinkedIn). Socio-demographics are discussed in Table 5.4, with counts and percentages for the total dataset and Okanagan and Metro Vancouver separately. Household size and household gross income showed mostly balanced results across both regions. Regarding household size, Okanagan had a slightly higher percentage of 2-person households than Metro Vancouver (+8.1%). Homeownership presented some variability across the regions, with Okanagan having a higher rate of homeowners than Metro Vancouver (+8.7%). The category with the most variability was dwelling types. Almost half of the respondents in Okanagan reported 65 living in single detached houses, while in Metro Vancouver, only 30.6% reported single detached houses as their dwelling type. In fact, in Metro Vancouver, the rates are well distributed between single detached houses, apartments with fewer than five stories, and apartments with five or more stories. In contrast, in Okanagan, single detached houses and apartment buildings with fewer than five stories account for almost 80% of households. Table 5.4 - Households' Socio-demographics Information Attributes Total (n=2067) Okanagan (n=892) Metro Vancouver (n=1175) Counts % Counts % Counts % Household Size One person 626 30.3% 251 28.1% 375 31.9% Two persons 853 41.3% 409 45.9% 444 37.8% Three persons 286 13.8% 113 12.7% 173 14.7% Four persons 216 10.4% 87 9.8% 129 11.0% Five or more persons 86 4.2% 32 3.6% 54 4.6% Dwelling Type Single detached house 785 38.0% 426 47.8% 359 30.6% Semi-detached house 53 2.6% 28 3.1% 25 2.1% Row house or townhouse 168 8.1% 73 8.2% 95 8.1% Apartment building with fewer than five stories 722 34.9% 274 30.7% 448 38.1% Apartment building with five stories and more 311 15.0% 75 8.4% 236 20.1% Apartment in a duplex 16 0.8% 6 0.7% 10 0.9% Movable dwelling (mobile home) 9 0.4% 8 0.9% 1 0.1% Household Ownership Status Owner 1355 65.6% 629 70.5% 726 61.8% Renter 709 34.3% 261 29.3% 448 38.1% Household Gross Income Under $10,000 39 1.9% 19 2.1% 20 1.7% $10,000-$29,999 159 7.7% 69 7.7% 90 7.7% $30,000-$49,999 217 10.5% 105 11.8% 112 9.5% $50,000-$79,999 375 18.1% 166 18.6% 209 17.8% $80,000-$99,999 249 12.0% 98 11.0% 151 12.9% $100,000-$149,999 432 20.9% 199 22.3% 233 19.8% $150,000-$199,999 211 10.2% 85 9.5% 126 10.7% $200,000 and over 202 9.8% 71 8.0% 131 11.1% 66 A comparison with census data is displayed in Table 5.5. For Metro Vancouver, Statistic Canada refers to the region as Greater Vancouver and includes all regions going from West Vancouver to Langley plus Bowen Island. This region encompasses a population of 2,642,825 (Statistics Canada, 2023d). Given that the survey only captured a significant percentage of residents of major cities (e.g., Vancouver, Surrey, Burnaby, and North Vancouver), the comparison with the census has limitations. Even with this limitation, the survey captured a representative population sample. Most variables closely aligned with the census distribution (within ±3%). Household sizes of 2-person and five or more represented significant differences of +6% and -4.8%, respectively. Lastly, there was an underrepresentation of households with a gross income of $30,000-$49,999 (-3.3%). Table 5.5 - Comparison of Households’ Socio-Demographics Information with Census 2021 Attributes Okanagan Metro Vancouver Survey Census Dif. Survey Census Dif. Household Size One person 28.1% 29.3% -1.2% 31.9% 29.0% 2.9% Two persons 45.9% 39.8% 6.1% 37.8% 31.8% 6.0% Three persons 12.7% 13.2% -0.5% 14.7% 15.7% -1.0% Four persons 9.8% 11.3% -1.5% 11.0% 14.0% -3.0% Five or more persons 3.6% 6.5% -2.9% 4.6% 9.4% -4.8% Dwelling Type Single detached house 47.8% 49.1% -1.3% 30.6% 27.8% 2.8% Semi-detached house 3.1% 5.3% -2.2% 2.1% 2.1% 0.0% Row house or townhouse 8.2% 7.0% 1.2% 8.1% 10.2% -2.1% Apartments 39.8% 34.8% 5.0% 59.1% 59.5% -0.4% Movable dwelling (mobile home) 0.9% 3.8% -2.9% 0.1% 0.4% -0.3% Household Ownership Status Owner 70.5% 70.0% 0.5% 61.8% 62.1% -0.3% Renter 29.3% 30.0% -0.8% 38.1% 37.8% 0.3% Household Gross Income Under $10,000 2.1% 1.7% 0.4% 1.7% 2.7% -1.0% $10,000-$29,999 7.7% 11.1% -3.4% 7.7% 9.7% -2.0% $30,000-$49,999 11.8% 17.2% -5.4% 9.5% 12.8% -3.3% $50,000-$79,999 18.6% 24.8% -6.2% 17.8% 19.0% -1.2% $80,000-$99,999 11.0% 13.0% -2.0% 12.9% 11.1% 1.8% $100,000-$149,999 22.3% 19.7% 2.6% 19.8% 20.2% -0.4% $150,000-$199,999 17.5% 12.5% 5.0% 10.7% 11.5% -0.8% $200,000 and over 11.1% 13.0% -1.9% 67 The comparison was made for the Okanagan region considering data from Central Okanagan and Vernon City, totaling a population of 266,681 (Statistics Canada, 2023a, 2023e). Similar to Metro Vancouver, most variables aligned with the census distribution (within ±3%). In income brackets from $10,000 to $79,999, all were underrepresented within the range of 3-6%. In addition, there was a 5% overrepresentation of households making $150,000 and over annually. Lastly, 2-person households were overrepresented by 6.1%. 5.2.2. Grouping Households Based on the Methods of Recruitment and Reminders Three groups of households that completed the survey were created based on the method of recruitment and reminders. The purpose is to analyze the effectiveness of recruitment methods and reminders – call, SMS, and e-mail. Group 1 contemplates self-registrants and includes households which received a reminder and some that did not; Group 2 includes households from the Address Only and Address & Phone lists that never got a reminder; and Group 3 contains households from the Address Only and Address & Phone lists which received at least one reminder. A few households from the Address & Phone list ended up in Group 2 due to not receiving a reminder because some phone numbers were not in service, while others were on hold for an extended period, and the call was never completed. Group 1 is the only one that contemplates both households receiving reminders and households not receiving reminders. Figure 5.1 describes the percentage of households in each group. Figure 5.1 - Households per Group 68 Table 5.6 discusses the socio-demographic characteristics of each group. While Groups 2 and 3 kept the rates of Okanagan and Metro Vancouver around 40 to 50%, Group 1 had a significant overrepresentation of Metro Vancouver residents. This indicates that the social media campaign worked better in Metro Vancouver. Most in Group 1 (59.7%) live in one—to two-person households, while 42.9% live in a single detached house. The majority have an income higher than $50,000 and own a vehicle. Most of Group 2 live in a 1 to 2-person (72.9%) apartment (55.9%), with an income higher than $80,000 (54%). They are more likely to be homeowners than Group 1. Lastly, Group 3 are primarily 2-person households (49.6%) living in single detached houses (63.3%), which they most likely own (84.1%). Most (55.8%) have an income over $80,000, making them the highest earners among the three groups. Primary respondents' attributes are discussed in Table 5.7. In terms of gender, while, in general, there is a preponderance of women in all three groups, the most skewed gender distribution is found in Group 1, the self-registrants, where more than 60% were women. This could indicate that women are more active on social media. The group that best represents the 2021 census is Group 2: 51.4% women in Okanagan and 51% women in Metro Vancouver (Statistics Canada, 2023a, 2023d, 2023e). In terms of age, groups 1 and 3 show an overrepresentation of opposite age groups: while Group 1 shows an overrepresentation for the age groups 25-44 (56.2%), Group 3 overrepresents the 60-84 age group (53.1%). These age groups are more balanced in Group 2: 42% for the 25-44 group and 30.2% for 60-84. Compared to the census, the age group of 25 to 44 is significantly overrepresented in groups 1 and 2 and underrepresented in Group 3: 25.2% in the Okanagan and 29.8% in Metro Vancouver (Statistics Canada, 2023a, 2023e, 2023d). Regarding the age group of 60 to 84, according to the census, Metro Vancouver’s rate is 21.6%, and Okanagan's is 30.5%. Groups 1 and 2 are the closest to those numbers, while Group 3 significantly overrepresents the older population (Statistics Canada, 2023a, 2023d, 2023e). 69 Table 5.6 - Comparison of Households’ Socio-Demographics Information Among Groups Attributes Group 1 (n=233) Group 2 (n=1608) Group 3 (n=226) Counts % Counts % Counts % Region Okanagan 34 14.6% 735 45.7% 123 54.4% Metro Vancouver 199 85.4% 873 54.3% 103 45.6% Household Size One person 75 32.2% 495 30.8% 56 24.8% Two persons 64 27.5% 677 42.1% 112 49.6% Three persons 52 22.3% 207 12.9% 27 11.9% Four persons 30 12.9% 164 10.2% 22 9.7% Five or more persons 12 5.2% 65 4.0% 9 4.0% Dwelling Type Single detached house 100 42.9% 542 33.7% 143 63.3% Semi-detached house 14 6.0% 34 2.1% 5 2.2% Row house or townhouse 26 11.2% 123 7.6% 19 8.4% Apartments 92 39.5% 899 55.9% 58 25.7% Movable dwelling (mobile home) 1 0.4% 7 0.4% 1 0.4% Household Ownership Status Owner 131 56.2% 1034 64.3% 190 84.1% Renter 102 43.8% 571 35.5% 36 15.9% Household Gross Income Under $10,000 6 2.6% 31 1.9% 2 0.9% $10,000-$29,999 28 12.0% 121 7.5% 10 4.4% $30,000-$49,999 25 10.7% 172 10.7% 20 8.8% $50,000-$79,999 53 22.7% 284 17.7% 38 16.8% $80,000-$99,999 24 10.3% 200 12.4% 25 11.1% $100,000-$149,999 40 17.2% 336 20.9% 56 24.8% $150,000-$199,999 12 5.2% 171 10.6% 28 12.4% $200,000 and over 23 9.9% 162 10.1% 17 7.5% Household Vehicle Ownership Zero vehicle 59 25.3% 212 13.2% 17 7.5% One vehicle 121 51.9% 799 49.7% 97 42.9% Two vehicles 44 18.9% 477 29.7% 85 37.6% Three vehicles 7 3.0% 100 6.2% 23 10.2% Four or more vehicles 2 0.9% 20 1.2% 4 1.8% Household Bike/E-Bike Ownership Zero bikes 84 36.1% 537 33.4% 63 27.9% One bike 48 20.6% 340 21.1% 45 19.9% Two bikes 66 28.3% 344 21.4% 49 21.7% Three bikes 11 4.7% 142 8.8% 17 7.5% Four or more bikes 24 10.3% 245 15.2% 52 23.0% 70 Table 5.7 - Comparison of Primary Respondents’ Socio-Demographics Information Among Groups Attribute Group 1 Group 2 Group 3 Counts % Counts % Counts % Primary Respondent Age 18 - 24 years 16 6.9% 127 7.9% 4 1.8% 25 - 44 years 131 56.2% 676 42.0% 45 19.9% 45 - 59 years 44 18.9% 291 18.1% 54 23.9% 60 - 74 years 38 16.3% 341 21.2% 73 32.3% 75+ years 4 1.7% 173 10.8% 50 22.1% Primary Respondent Gender Man 81 34.8% 747 46.5% 92 40.7% Woman 144 61.8% 826 51.4% 130 57.5% Non-binary person 4 1.7% 14 0.9% 1 0.4% Primary Respondent's Marital Status Married or Living common law 135 57.9% 879 54.7% 148 65.5% Not living common law 93 39.9% 664 41.3% 70 31.0% Primary Respondent Driver's License Ownership Yes 195 83.7% 1498 93.2% 216 95.6% No 38 16.3% 110 6.8% 9 4.0% Primary Respondent Transit Pass Ownership Yes 93 39.9% 244 15.2% 26 11.5% No 140 60.1% 1364 84.8% 199 88.1% Primary Respondent Education Level No certificate, diploma, or degree 2 0.9% 24 1.5% 3 1.3% High school certificate or equivalent 21 9.0% 191 11.9% 32 14.2% Apprenticeship or trade certificate or diploma 9 3.9% 81 5.0% 20 8.8% College, CEGEP or other non-university certificate or diploma 38 16.3% 231 14.4% 43 19.0% University certificate or diploma below the bachelor level 31 13.3% 144 9.0% 10 4.4% Bachelor's degree or higher 130 55.8% 914 56.8% 115 50.9% Primary Respondent Employment Status Full-time (30 and above hours/week) 132 56.7% 794 49.4% 90 39.8% Part-time (<30 hours/week) 24 10.3% 165 10.3% 18 8.0% Unemployed and seeking a job 7 3.0% 46 2.9% 4 1.8% Unemployed and not seeking a job 8 3.4% 37 2.3% 6 2.7% Retired 32 13.7% 405 25.2% 95 42.0% Student 13 5.6% 85 5.3% 6 2.7% Other 11 4.7% 50 3.1% 5 2.2% Primary Respondent Employment Arrangement Everyday remote 30 12.9% 178 11.1% 21 9.3% Everyday travel to work 50 21.5% 408 25.4% 49 21.7% Half and half 32 13.7% 119 7.4% 13 5.8% Mostly remote 30 12.9% 108 6.7% 13 5.8% Mostly travel to work 14 6.0% 146 9.1% 11 4.9% Not applicable 71 30.5% 619 38.5% 116 51.3% 71 In Metro Vancouver, the marital status is divided as follows: 56.5% were married or living common-law, while 43.5% were not married and not living common-law. In Okanagan, the rates were 58.3% and 41.7% respectively. The group that deviates the most on these rates is Group 3, where 65.5% were married or living common-law individuals. All groups showed a high number of respondents holding a driving license, with Group 1 having the lowest rate at 83.7%. Interestingly, this was the group holding the highest transit pass ownership rate. Group 1 also had the highest rate of households with no vehicles. This suggests that Group 1 was more inclined to use transit among the three groups. Regarding education, all three groups had a higher rate of bachelor's degrees or higher (over 50%). There is a significant overrepresentation of the education level compared to the census, where only 21.6% of the population in the Okanagan and 35.2% in Metro Vancouver hold a bachelor's degree or higher (Statistics Canada, 2023a, 2023e, 2023d). Lastly, Group 3 had the highest rate of retired people (42%), which became evident under employment arrangements, where 51% replied that it was “not applicable”. This category also accounts for students and unemployed people. More than half of Group 1 worked full-time, with Group 2 following closely at 49.4%. Everyday travel to work was the most common arrangement across the three groups. Their different sources partially explain these differences among the three groups. While Group 1 was made of self-registrants from social media, Groups 2 and 3 came from two different address-based databases: 82.6% of Group 2 came from the Canada Post address-only sample. In contrast, 84.9% of Group 3 came from the ASDE address-and-phone sample. 5.2.3. Group 1: Self-registrants A total of 233 self-registered households completed the survey. Approximately 60% of these households completed the survey within a day of receiving the access code. This demonstrates the high motivation of this group to complete the survey. 72 Regarding recruitment methods, almost 74% received at least one reminder. This rate can be explained by the fact that, for self-registrants with a matched phone number, an SMS was sent right after the e-mail with an access code to remind them about the survey. In addition, 72% of households received at least one SMS and 2% received at least one e-mail. Furthermore, no household in this group received a phone call. Only one household received communications via two methods (e-mail and SMS). Table 5.8 summarizes all this information. Table 5.8 - Group 1 Information Attributes Counts % Region Okanagan 34 14.6% Metro Vancouver 199 85.4% Time for completion after getting the access code Completed the same day 138 59.2% Completed the next day 19 8.2% Completed two days after getting the access code 12 5.2% Completed three days after getting the access code 6 2.6% Completed after more than four days of getting the access code 58 24.9% Number of households per reminder method Call 0 0.0% SMS 168 72.1% E-mail 5 2.1% None 61 26.2% Table 5.9 summarizes the number of reminders by household counts. Almost half of these households (n=114) received two reminders. Furthermore, all of these reminders were sent via SMS. More than 70% of them (n=82) completed the survey within 24 hours of receiving an SMS. This indicates that SMS is an efficient method of communication with self-registrants. 73 Table 5.9 - Group 1 Reminder Frequency Number of households per frequency of reminders Counts % Zero reminder 61 26.2% One reminder 14 6.0% Two reminders 114 48.9% Three reminders 19 8.2% Four reminders 24 10.3% Five reminders 1 0.4% Table 5.10 summarizes the completion time for households that received at least one reminder to finalize the survey. This analysis further proves that SMS is a successful method for self-registrants, with 69% of households completing the survey the same day they received an SMS. In fact, 40.5% of them completed the survey within 1 hour of receiving the SMS. Due to the low number of e-mails sent, it is hard to determine the efficacy of this method for this particular group. Table 5.10 - Group 1 Time for Completion per Reminder Method Attributes Counts % Time for completion after getting an SMS Completed the same day 116 69.0% Completed the next day 20 11.9% Completed two days after getting the SMS 12 7.1% Completed three days after getting the SMS 5 3.0% Completed after more than four days of getting the SMS 15 8.9% Time for completion after getting an e-mail Completed the same day 2 40.0% Completed six days after getting the email 3 60.0% 5.2.4. Group 2: Address-based without reminders Group 2 comprises 1,608 households, 45.7% in the Okanagan region and 54.3% in Metro Vancouver (Figure 5.2). Communication with these households was impossible because the members had no phone numbers or e-mails listed before completing the survey. 74 Table 5.11 provides information on the duration of the survey completion. The start point was the day the envelope was delivered to a Canada Post agency in Metro Vancouver. It was expected that Metro Vancouver addresses would receive the envelope faster than Okanagan households. This hypothesis was confirmed in the analysis since no household in Okanagan completed the survey within two days of posting the mail. Table 5.11 - Group 2 Time for Completion After the Mailing Date Time for completion after the mail was sent Total Okanagan (n=735) Metro Vancouver (n=873) Counts % Counts % Counts % Completed the next day 104 6.5% 0 0.0% 104 11.9% Completed two days after the mail was sent 104 6.5% 14 1.9% 90 10.3% Completed three days after the mail was sent 77 4.8% 14 1.9% 63 7.2% Completed four days after the mail was sent 191 11.9% 93 12.7% 98 11.2% Completed five days after the mail was sent 205 12.7% 117 15.9% 88 10.1% Completed six days after the mail was sent 152 9.5% 86 11.7% 66 7.6% Completed seven days after the mail was sent 108 6.7% 59 8.0% 49 5.6% Completed more than eight days after the mail was sent 667 41.5% 352 47.9% 315 36.1% Figure 5.2 - Group 2: Region of Residence 75 Figure 5.3 and Figure 5.4 show the counts of households completed by days after the mailing date for Okanagan and Metro Vancouver, respectively. There is a clear difference between the two regions: while Okanagan peaked on day 5, Metro Vancouver reached it on day 1, indicating that Metro Vancouver received the letters faster than Okanagan. Furthermore, the first six days in the Metro Vancouver region presented a higher rate pattern, while Okanagan had high rates only from day 4 to day 6. For the rest of the period, both regions presented very similar completion behaviour, with many households completing more than 20 days after the mail was sent. In Metro Vancouver, 11% of households completed the survey more than 20 days after the posting date, while 13% in the Okanagan. Figure 5.5 shows how many days all households from Group 2 took to complete the survey. Although the number of completions fell considerably after day 20, there was a considerable amount of household finishing between days 21 and 57. In that period, 179 households completed the survey. This indicates that is beneficial to maintain the survey open for a long period. In this case, the survey was opened for 57 days. Figure 5.3 - Counts of Households per Duration in Days (Group 2 - Okanagan) 76 Figure 5.4 - Counts of Households per Duration in Days (Group 2 - Metro Vancouver) Figure 5.5 - Counts of Households per Duration in Days (Group 2) 77 5.2.5. Group 3: Address-based with reminder A total of 226 households originating from Address Only and Address & Phone lists received reminders. In terms of regions, similar to Group 2, this group is balanced between 54% from Okanagan and 46% from Metro Vancouver (Table 5.12). Table 5.12 - Group 3 Region of Residence Region Counts % Okanagan 123 54.4% Metro Vancouver 103 45.6% Total 226 100.0% The completion rates by days after the posting date differ from Group 2. Firstly, in Group 3, no household completed the survey within a day of the mailing date. In Metro Vancouver, the first households to complete it was two days after that date; in Okanagan, it was four days. Figure 5.6 and Figure 5.7 show these results for Okanagan and Metro Vancouver, respectively. Moreover, this group did not present significant peaks of completions. Another difference from Group 2 is related to the late completions. While in Group 2, 47.9% and 36.1% of households took more than a week to complete for Okanagan and Metro Vancouver, respectively, in Group 3, 80.6% of households in Metro Vancouver and 79.7% of households in Okanagan took more than a week to complete (Table 5.13). Figure 5.6 - Counts of Households per Duration in Days (Group 3 - Okanagan) 78 Table 5.13 - Group 3 Time for Completion After the Mailing Date Time for completion after the mail was sent Total Okanagan Metro Vancouver Completed two days after the mail was sent 3 1.3% 0 0.0% 3 2.9% Completed three days after the mail was sent 1 0.4% 0 0.0% 1 1.0% Completed four days after the mail was sent 4 1.8% 3 2.4% 1 1.0% Completed five days after the mail was sent 10 4.4% 6 4.9% 4 3.9% Completed six days after the mail was sent 15 6.6% 9 7.3% 6 5.8% Completed seven days after the mail was sent 12 5.3% 7 5.7% 5 4.9% Completed more than eight days after the mail was sent 181 80.1% 98 79.7% 83 80.6% Regarding reminder methods, almost 78% received at least one call, close to 30% received at least one SMS and 24.8% received at least one e-mail. Most households received only one reminder (65%), while over 20% received two reminders. Table 5.14 summarizes this information. Figure 5.7 - Counts of Households per Duration in Days (Group 3 - Metro Vancouver) 79 Table 5.14 - Group 3 Reminder Counts and Frequency Attributes Counts % Number of households per reminder method Call 176 77.9% SMS 66 29.2% E-mail 56 24.8% None 0 0.0% Number of households per frequency of reminders One reminder 147 65.0% Two reminders 48 21.2% Three reminders 15 6.6% Four reminders 12 5.3% Five reminders 4 1.8% Table 5.15 summarizes the completions per reminder method. Like Group 1, SMS appears to be the most efficient method for reminders, with 47% of households completing the survey the same day an SMS was sent. The analysis also indicates that e-mail is a close second: 41.1% of households completed the survey on the same day as receiving an e-mail. Two control groups were created to further analyze the efficiency of SMS and e-mails: households that received an e-mail but never received an SMS (n=4) and households that received an SMS but never an e-mail (n=14). All four households that received an e-mail completed the survey the same day as the e-mail. Of those, only two also received calls. One household completed 13 days after getting the call, indicating that the completion most likely did not happen due to the call, and one household completed two days after getting the call. In this case, it can be argued that the call placed before the e-mail could have helped with the completion. Of the households that received an SMS and no e-mail, 35.7% (n=5) completed the survey on the same day as the SMS, while 21.4% (n=3) completed the next day. No other method can be attributed to these 14 completions. This analysis indicates that SMS and e-mail are suitable to boost the response rate. 80 Table 5.15 - Group 3 Time for Completion per Reminder Method Time for completion after getting a call Counts % Completed the same day 22 12.5% Completed the next day 22 12.5% Completed two days after getting the call 18 10.2% Completed three days after getting the call 10 5.7% Completed after more than four days of getting the call 104 59.1% Time for completion after getting an SMS Counts % Completed the same day 31 47.0% Completed the next day 10 15.2% Completed two days after getting the SMS 8 12.1% Completed three days after getting the SMS 6 9.1% Completed after more than four days of getting the SMS 11 16.7% Time for completion after getting an e-mail Counts % Completed the same day 23 41.1% Completed the next day 5 8.9% Completed two days after getting the e-mail 4 7.1% Completed three days after getting the e-mail 4 7.1% Completed after more than four days of getting the e-mail 20 35.7% The costliest reminder method was telephone calls. For this reason, a deeper analysis of the efficiency of calls was conducted (Table 5.16). Of the 22 households that completed the survey the same day as receiving a call, none received reminders through another method. The same thing can be said for the 22 households that completed the next day: the only communication method used with them was a telephone call. In fact, of the 176 households that received a call, 156 only received a reminder through the telephone, representing 7.5% of the total completions (2,067). Although it is not a high rate, this method should not be ignored. Many address-and-phone lists (e.g., Canada Post lists) contain only landline numbers, making telephone calls the only way to reach these households. 81 Table 5.16 - Group 3 Time for Completion After Receiving a Call Time for completion after getting a call Counts Other methods Completed the same day 22 No Completed the next day 22 No Completed two days after getting the call 17 No Completed two days after getting the call 1 E-mail Completed three days after getting the call 9 No Completed three days after getting the call 1 SMS and e-mail Completed four days after getting the call 12 No Completed five days after getting the call 9 No Completed six days after getting the call 10 No Completed seven days after getting the call 7 No Completed seven days after getting the call 2 SMS and e-mail Completed more than a week after getting the call 48 No Completed more than a week after getting the call 13 SMS and e-mail Completed more than a week after getting the call 2 SMS Completed more than a week after getting the call 1 E-mail 5.3. Cost Analysis One of the largest budget expenses for a municipality in a metropolitan region involves the implementation of transportation surveys (National Academies of Sciences, Engineering, and Medicine, 2008). The present survey was initially quoted at approximately C$385,000 by a consulting firm, with the first wave alone estimated at C$235,000. These costs did not account for participant incentives. The original funding available for the first wave was C$90,000. By employing multiple recruitment methods and managing the survey in-house, the final cost was kept under budget at C$85,358.16. Table 5.17 summarizes the costs by category, excluding e-mails, which were free of charge. More than half of the budget was allocated to mailing services. A mail service provider was contracted to handle this, which included printing invitation letters and brochures, using envelopes with a UBC logo, and mailing. A total of 49,748 envelopes were sent, resulting in a cost per unit of C$0.93. 82 The second highest expense was telephone calls. A telemarketing company was hired, making a total of 9,040 calls (excluding incomplete calls, such as numbers not in service). The cost per unit for this method was C$2.59. Table 5.17 - BC ATUS Wave 1 Total Cost Category Cost (CAD) % Address Only and Address & Phone Lists $1,516.50 1.78% Marketing and Social Media $13,565.00 15.89% Mailing $46,367.17 54.32% Telephone Call $23,431.84 27.45% SMS $477.65 0.56% Total $85,358.16 100.00% SMS was the most economical option, with a total cost of C$477.65. A cloud communications company provided a programmable tool for sending and receiving text messages via web service APIs. In total, 12,939 SMS messages were sent, resulting in a cost per unit of only C$0.04. Lastly, about 15% of the budget was spent on marketing and social media. This included the development of a logo, artwork for brochures and posters, two videos, and paid advertisements. The cost per click ranged from C$0.66 for the Facebook/Instagram campaign to C$2.34 for LinkedIn. In conclusion, the strategic use of multiple recruitment methods and in-house survey management proved effective in significantly reducing the overall costs of the transportation survey. The allocation of resources, particularly towards cost-effective options like SMS, allowed the team to stay under budget while still achieving comprehensive outreach. This approach not only demonstrates fiscal responsibility but also underscores the importance of flexible and innovative survey strategies in managing large-scale transportation studies. Future surveys can benefit from these insights by leveraging a mix of traditional and digital recruitment methods to optimize budget efficiency and maximize participant engagement. 83 5.3.1. Cost per Completion per Recruitment Method In total, 1834 completed households received the invitation letter through the mail. The cost per completion for this method was C$26.11 per completed household. In total, 176 completed households got at least one call, most coming from the address and phone list. The cost per completion for this method can be calculated by the ratio of the total telephone cost to the number of completed households. This cost was C$133.14 per completed household. Similarly, 234 completed households received at least one SMS. The cost per completion for SMS was C$2.04. Finally, 233 completed households can be attributed to the social media campaign (self-registrants). The cost per completed household for self-registrants was C$58.22. Table 5.18 below summarizes this information. This analysis assumes that all self-registrants were recruited through social media. The total cost includes paid ads, management fees and the creation of the artwork for the ads. Table 5.18 - Cost per Completion per Recruitment Method Method Number of Households Completed Total Cost (CAD) Cost per Completion Mail 1834 $47,883.67 $26.11 Call 176 $23,431.84 $133.14 SMS 234 $477.65 $2.04 Self-registrants 233 $13,565.00 $58.22 Total $85,358.16 The mail costs can be divided between address & phone and address-only lists. For the address-only list, the cost of the list was C$500 and for the address & phone, it was C$ 1,016.5. In total 1362 completed households came from the address-only list and 472 came from the address & phone list. Thus, the equivalent cost per completed household for the address-only list was C$25.65 and for the address & phone list was C$27.44. Table 5.19 summarizes this information. 84 Table 5.19 - Cost per Completion per Mailing Lists Method Number of Households Completed Total Cost (CAD) Cost per Completion Address-Only 1362 $34,934.07 $25.65 Address & Phone 472 $12,949.60 $ 27.44 Total $47,883.67 5.4. Modelling the Frequency of Communication to Complete the Survey To further understand the efficiency of reminders, this study adopts an ordered logit modelling technique to analyze which groups of households need more frequent communication to complete the survey. This analysis offers valuable insights for optimizing survey administration strategies. Particularly examining socio-demographic attributes, the study seeks to enhance the effectiveness of recruitment methods in the context of transportation surveys. 5.4.1. Modelling Method The ordered logit model is a regression model for an ordinal response variable 𝑦𝑖 represented as an underlying continuous response, 𝑦𝑖∗. The basic representation of this model is as follows: 𝑦𝑖∗ = ∑𝛽𝑘𝑥𝑘𝑖 + 𝜀𝑖𝐾𝑘=1 (1) Where k is the number of explanatory variables; 𝑥𝑘𝑖 is the value of the 𝑘th explanatory variable; 𝛽𝑘 is the estimated coefficient corresponding to each explanatory variable, and 𝜀 represents a random error term. As 𝑦 is an ordinal variable, 𝑦𝑖∗ is discretized through its relations with threshold values of µ𝑜, µ1, µ2… µ𝑐where C represents the number of categories in the ordinal variable. This way, 𝑦𝑖 = 𝑦𝑐 only if µ𝑐−1 < 𝑦𝑖∗ ≤ µ𝑐. In this example, the communication frequency is recorded as the following six discrete categories: 85 from zero reminders to 5 reminders. The observable components can be interpreted as: 𝑦𝑖 = { 0; 𝑖𝑓 𝑦𝑖∗ ≤ µ0 (𝑧𝑒𝑟𝑜 𝑟𝑒𝑚𝑖𝑛𝑑𝑒𝑟𝑠)1; 𝑖𝑓 µ0 ≤ 𝑦𝑖∗ ≤ µ1 (𝑜𝑛𝑒 𝑟𝑒𝑚𝑖𝑛𝑑𝑒𝑟)2; 𝑖𝑓 µ1 ≤ 𝑦𝑖∗ ≤ µ2 (𝑡𝑤𝑜 𝑟𝑒𝑚𝑖𝑛𝑑𝑒𝑟𝑠)3; 𝑖𝑓 µ2 ≤ 𝑦𝑖∗ ≤ µ3 (𝑡ℎ𝑟𝑒𝑒 𝑟𝑒𝑚𝑖𝑛𝑑𝑒𝑟𝑠)4; 𝑖𝑓 µ3 ≤ 𝑦𝑖∗ ≤ µ4 (𝑓𝑜𝑢𝑟 𝑟𝑒𝑚𝑖𝑛𝑑𝑒𝑟𝑠)5; 𝑖𝑓 𝑦𝑖∗ > µ5 (𝑓𝑖𝑣𝑒 𝑟𝑒𝑚𝑖𝑛𝑑𝑒𝑟𝑠)(2) The following equation calculates the probabilities: 𝑃𝑟𝑜𝑏[𝑦𝑖 = 𝑐] = 𝑃𝑟𝑜𝑏[𝑦𝑖∗ 𝑖𝑛 𝑡ℎ𝑒 𝑐𝑡ℎ 𝑟𝑎𝑛𝑔𝑒] (3) The model is estimated by the maximum log-likelihood method and can be formulated by: 𝐿𝐿 = ∑ ln𝑃(𝑦 = 𝑐) 𝑀𝑚=1(4) 5.4.2. Results The data input for this model came from Groups 1 and 3 (n = 459). Group 1 comprises self-registrants, and Group 2 comprises households that received an invitation letter through the mail and received at least one reminder. The dependent variable is the number of reminders each household received to complete the survey, from zero to five. The summary statistics of the variables retained in the final model are presented in Table 5.20, and the model’s results are presented in Table 5.21. The model confirms the effects of socio-demographics, such as dwelling type, age and income, work arrangements, transit pass, and other household attributes. Results are discussed below according to their categories. 86 Table 5.20 - Descriptive Statistics of the Variables Used in the Ordered Logit Model Variables Description % or Mean Std. dev. Socio-Demographic Attributes Age: < 35 Dummy; If respondent is younger than 55 years old = 1, else = 0 25.71% n/a Age: > 60 Dummy; If respondent is older than 60 years old = 1, else = 0 35.95% n/a Gender: Male Dummy; If respondent identifies as male = 1, else = 0 37.69% n/a Marital Status: single Dummy; If respondent is not married and does not live in common law = 1, else = 0 35.51% n/a Household Attributes Income < C$80,000 Dummy; If annual gross household income is less than C$80,000 = 1, else = 0 39.65% n/a Self-registrant Dummy; If respondent is self-registrant = 1, else = 0 50.76% n/a Apartment Dummy, If respondent lives in an apartment = 1, else = 0 32.68% n/a Household size = 1 Dummy; If respondent lives alone = 1, else = 0 28.54% n/a Monthly Rent Household Monthly Rent $1,537.67 $815.32 Mobility and Work Arrangement Attributes Employment: Teleworker Dummy; If respondent’s current employment arrangement is every day remote = 1, else = 0 20.48% n/a Transit Pass Dummy; If respondent owns a transit pass = 1, else = 0 25.93% n/a 5.4.2.1. Socio-Demographic Attributes The variable representing individuals younger than 35 years old had a negative sign. This indicates that households with a primary respondent younger than 35 are less likely to require frequent reminders. In contrast, results indicate that individuals older than 60 are more likely to need regular communication. Because this survey was 100% online, this could be explained by the fact that younger people tend to be more tech-savvy. Several calls and e-mails from individuals asking for help using the web platform attributed age to their difficulties. Many of these e-mails also described their frustration with the 24-hour activity log. 87 One interesting result from the model is that male-identifying individuals are more likely to receive fewer reminders to complete the survey than their female counterparts. This could be attributed to female-identifying individuals being responsible for more household activities and thus having less time to invest in surveys (Hochschild & Machung, 2012). Lastly, single individuals appear to need more frequent communication. Due to different schedules, this could be related to single individuals living with roommates and requiring more time to complete the survey. Table 5.21 - Ordered Logit Model Results Variables Coefficient t-stat Constant 2.59*** 11.37 Socio-Demographic Attributes Age: < 35 -0.67*** -2.60 Age: > 60 0.54** 2.47 Gender: Male -0.45** -2.40 Marital Status: single 0.93*** 3.76 Household Attributes Income < C$80,000 -0.79*** -3.99 Self-registrant 0.88*** 4.34 Apartment 0.54*** 2.65 1-person -1.10*** -4.15 Monthly Rent 0.0004*** 4.60 Mobility and Work Arrangement Attributes Employment: Teleworker -0.49** -2.19 Transit Pass -1.24*** -5.18 Threshold Variables µ0 1 - 2 reminders 2.28*** 19.56 µ1 2 - 3 reminders 4.28*** 31.29 µ2 3 - 4 reminders 5.02*** 29.48 µ3 4 - 5 reminders 7.25*** 16.02 Note: *** and ** are significant at 1% and 5% levels, respectively. 88 5.4.2.2. Household Attributes The results demonstrate that households with an income of less than C$80,000 might require fewer reminders to complete the survey. This result might indicate that these respondents are more inclined to participate due to the incentive campaign (Singer & Kulka, 2002). Moreover, results indicate that the higher the monthly rate, the higher the likelihood of requiring frequent communication, which corroborates with the first indication regarding income. Results suggest that the variable representing self-registrant shows a positive sign. This indicates that self-registered households required more frequent communication to finalize the survey. This might be due to the time between the date they registered and received the access code to participate in the survey. They might have registered during their leisure time, and by the time they got the access code and link, they might have lost interest and needed to be remaindered to participate in the survey. This can also be explained by the fact that, right after sending the access code to their e-mail, an SMS was sent to those with a listed phone number to inform them about the e-mail, reminding them of the survey they self-registered. Households living in apartments demonstrate the need for more frequent reminders, while one-person households require less communication. This finding is consistent with earlier studies on the effect household size has on survey participation: the higher the number of residents, the less likely the household is to participate in transportation surveys due to the burden of completing a diary for every member of the household (Stopher & Greaves, 2007). 5.4.2.3. Mobility and Work Arrangement Attributes As expected, individuals who are teleworkers every day or most days also need less frequent communication. This indicates they might have more flexibility to complete the survey during the day. The negative coefficient of the transit pass also supports the income discussion above, as the literature shows that lower-income might impact the frequency of transit use (Miller et al., 2018). 89 5.4.3. Elasticity Results The parameter estimates of explanatory variables do not directly indicate the magnitude of the variables' impacts on the frequency of communication. For this, an elasticity analysis is performed. This chapter estimates the elasticities to evaluate the magnitude of the impact of the independent variables on the probability of frequency of communication. Elasticity values are averaged over the sampled households and show the effect of a unit increase in an explanatory variable on the frequency of communication outcome probabilities (Greene, 2016b). These results are shown in Table 5.22. Table 5.22 - Elasticity Results Variables Frequency of communication (reminders) Zero 1 2 3 4 5 Apartment -0.03 -0.10 0.05 0.03 0.04 0.01 1-person Household 0.08 0.19 -0.12 -0.06 -0.07 -0.01 Self-registrant -0.06 -0.15 0.11 0.05 0.05 0.01 Income < $80,000 0.04 0.14 -0.07 -0.05 -0.06 -0.01 Monthly Rent 0.00 0.00 0.00 0.00 0.00 0.00 Age: < 35 0.04 0.12 -0.06 -0.04 -0.05 -0.01 Age: > 60 -0.03 -0.09 0.04 0.03 0.04 0.01 Gender: Male 0.03 0.08 -0.05 -0.03 -0.03 0.00 Employment: Teleworker 0.03 0.09 -0.06 -0.03 -0.03 0.00 Transit Pass 0.09 0.21 -0.13 -0.07 -0.08 -0.01 Marital Status: single -0.06 -0.16 0.09 0.05 0.07 0.01 Results suggest that the variables representing respondents residing in an apartment, self-registrants, adults older than 60, and single show a significant positive impact on the need for more frequent communication (more than two reminders). For instance, the likelihood of self-registrants to complete the survey after receiving two reminders increases by 0.11%. The group of population that require less frequent communication are small-sized households, households with income lower than C$80,000, individuals 90 younger than 35 years old, males, teleworkers, and transit pass holders. For example, transit pass holders show a 0.21% increase in the likelihood of completing the survey after receiving just one reminder. 5.5. Modelling the Time to Complete the Survey Without Communication A hazard-based duration modelling technique is adopted to investigate the attributes that influence the duration of completion without any communication other than the first mail sent with the invitation letter. It follows an accelerated failure time (AFT) technique. In this technique, the nonnegative random variable t represents the time until an event happens, which can be understood as the end of an event. Sometimes, the end is when something fails, so it is often referred to as “failure time”. However, t can also represent the time until something else happens, like recovering from an illness, the time until a merger occurs, or, in the case of this study, the time until a household completes a survey (Greene, 2016a). In these cases, “time until transition” seems more fitting than “failure time” and sounds more positive. 5.5.1. Modelling Method The dependent variable is the duration (in days) between the day the mail was sent and the day the household completed the survey. Here, T is a continuous, nonnegative random variable representing a household's time to complete the survey. The hazard failure time models can be defined by the density function, g(t), the cumulative distribution function, G(t), the survival function, S(t), and the hazard function, h(t). G(t) shows the probability of finalizing the survey of a particular household before time t. However, survival function S(t) demonstrates the probability of completion greater than time t (Greene, 2016a; Orvin & Fatmi, 2022). The hazard function, H(t), indicates the instantaneous likelihood per unit time for survey completion of a household, provided that the household has survived until time t. Thus, H(t) represents the instantaneous rate of transition at time t and takes the following form: 𝐻(𝑡) =𝑔(𝑡)1 − 𝐺(𝑡)= 𝑔(𝑡)𝑆(𝑡)= −𝑑 𝑙𝑜𝑔𝑆(𝑡)𝑑𝑡(5) 91 Considering a parametric hazard modelling approach that assumes a distribution for the baseline hazard, equation (4) can be stated as: 𝐻(𝑡, 𝑋) = 𝐻0(𝑡) exp[𝑋(𝑡)] (6) Here, X(t) = observed time-invariant explanatory variables. H0(t) = baseline hazard. The parametric hazard model adopts an accelerated failure time (AFT) approach to simplify the interpretation of the effects of explanatory variables on the hazard function. This approach assumes the impact of explanatory variables on survival time is direct and impacts the time to ‘transition’ from non-completion to completion. It assumes a linear relationship between the log of survival time T and a vector of explanatory variables X, and can be stated as (Orvin & Fatmi, 2022): 𝐿𝑛(𝑇) = 𝛽𝑐𝑋 + 𝜏 𝜀 (7) Here, 𝛽𝑐 = vector of the parameter of the explanatory variable X (to be estimated). ε = random error term scaled by 𝜏 The hazard model uses the Weibull parametric distribution technique. The probability density function is expressed by two parameters: shape parameter (𝜑) and scale parameter (𝜓). In this case, H(t) is: 𝐻(𝑡) = 𝜓𝜑(𝜓𝑡)𝜑−1 (8) The likelihood function provided that the household j (j =1, 2, … N) with explanatory variables X can be written as: 𝐿(𝑗) = [𝐻(𝑡𝑗, 𝑋𝑗)] [𝑆(𝑡𝑗, 𝑋𝑗] (9) Finally, the log-likelihood function can be expressed as, where N represents the total number of households in the sample: 92 𝐿𝐿 = ∑ log 𝐿(𝑗)𝑁𝑗=1 (10) This study used uncensored data. Interpreting an AFT model without censoring involves understanding how each explanatory variable influences the time to event, considering both the magnitude and direction of the effect. The interpretation of coefficients remains the same as in the AFT model with censored data. 5.5.2. Results The data input for this model came from Group 2, households that received an invitation letter through the mail and did not receive any other reminder (n=1608). Thus, this model utilizes data based only on mail recruitment. The dependent variable is the duration (in days) between the day the mail was sent and the day the household completed the survey. The range of this variable goes from one day to 57 days. The summary statistics of the variables retained in the final model are presented in Table 5.23, and results from the hazard-based duration model are shown in Table 5.24. Table 5.23 - Descriptive Statistics of the Variables Used in the Hazard-based Duration Model Variables Description % or Mean Socio-Demographic Attributes Age: > 55 Dummy; If respondent is older than 55 years old = 1, otherwise = 0 38.18% Gender: Male Dummy; If respondent identifies as male = 1, else = 0 46.45% Marital Status: single Dummy; If respondent is not married and does not live in common law = 1, else = 0 41.34% Location Attribute Metro Vancouver Dummy; If respondent’s residence is in Metro Vancouver region = 1, else = 0 54.29% Household Attributes Household size ≤ 2 Dummy; If dwelling has two or one member = 1, else = 0 72.88% Owner Dummy; If respondent’s own the dwelling = 1, else = 0 64.30% Work Arrangement Attributes Employment: Full-time worker Dummy; If respondent’s current employment status is full-time worker = 1, else = 0 49.38% Employment: Teleworker Dummy; If respondent’s current employment arrangement is every day remote = 1, else = 0 11.1% 93 Table 5.24 - Hazard-based Duration Model Results Variables Coefficient t-stat Constant 2.48*** 40.99 Socio-Demographic Attributes Age: > 55 -0.10** -2.23 Gender: Male -0.07* -1.83 Marital Status: single 0.12*** 3.07 Location Attribute Metro Vancouver -0.22*** -5.65 Household Attributes Household size ≤ 2 -0.05 -1.04 Owner -0.08** -1.98 Work Arrangement Attributes Employment: Full-time worker 0.13*** 3.15 Employment: Teleworker -0.06 -0.97 Note: ***, **, and * are significant at 1%, 5%, and 10% levels, respectively. The model confirms the effects of socio-demographics, such as age and marital status, work arrangements, and other household attributes. Results are discussed below according to their categories. 5.5.2.1. Socio-Demographic Attributes The variable representing adults older than 55 has a negative sign, indicating that this age group is more likely to require a shorter time to complete the survey. This suggests that older adults often exhibit reliability, responsibility, and routine characteristics that contribute to their likelihood of completing surveys quickly without reminders. The model also indicates that people identifying as males are more likely to require less time to complete the survey without reminders. This finding is consistent with the ordered logit model, which indicated that males need fewer reminders to complete the survey. 94 Finally, results show that single participants are more likely to require a longer time to complete the survey. This could signify that these individuals have variable routines and, as such, require more time to complete surveys. 5.5.2.2. Location Attribute Results show that the variable representing Metro Vancouver has a negative sign. This suggests that residents of Metro Vancouver have a higher likelihood of taking a shorter time to complete without reminders. This can indicate that Metro Vancouver residents are more used to completing transportation surveys, probably because TransLink and larger cities in the region conduct such surveys more frequently than in the Okanagan. 5.5.2.3. Household Attributes Results on household size are also consistent with the ordered logit model, which indicated that 1-person households require fewer reminders to complete the survey. In the hazard model, households with one or two members are more likely to complete without reminders in a shorter time; however, the coefficient is not statistically significant at conventional levels. Household owners are also associated with a negative coefficient, indicating a shorter completion time. This can be explained by the fact that they usually have more residence stability and longevity than renters. This stability may lead to a greater familiarity with survey requests and a higher likelihood of participation. They also may have more flexible schedules or autonomy over time, allowing them to respond to survey requests promptly. 5.5.2.4. Work Arrangement Attributes The variable representing full-time workers indicates a positive sign, suggesting that this work arrangement is connected with a higher likelihood of longer time to complete without reminders. This could indicate that these individuals are more likely to have busier schedules and, as such, require more time to 95 complete surveys. Moreover, results show that respondents working remotely daily are more likely to complete the survey faster. However, its coefficient is not statistically significant at conventional levels. 5.6. Concluding Remarks For the descriptive analysis, the analysis of socio-demographics showed most variables aligned with the census distribution within ±3%. Regarding recruitment, SMS is the best cost-effective method for communication with participants. For the ordered logit model, the analysis of socio-demographic, household, and mobility/work arrangement attributes provides valuable insights into the factors influencing survey completion behaviour. Younger respondents and those with lower incomes are less likely to need frequent reminders, possibly due to greater familiarity with technology and incentive campaigns. Conversely, older respondents and those with higher incomes may require more reminders, possibly due to less tech-savviness or busier schedules. Gender differences also play a role, with males needing fewer reminders than females, which could be linked to differing household responsibilities. Household attributes such as size and dwelling type also impact completion behaviour, with larger households and apartment dwellers needing more reminders. Additionally, work arrangement and mobility factors, such as daily teleworking and transit pass ownership, influence completion behaviour, indicating the importance of considering these factors in survey design and outreach strategies. These findings underscore the need for targeted and flexible recruitment methods to accommodate respondents' diverse needs and circumstances. For the hazard model, the analysis of socio-demographic, location, household, and work arrangement attributes reveals significant insights into the completion times of transportation surveys. Older adults, males, and residents of Metro Vancouver tend to complete surveys more quickly, while single individuals and full-time workers may require more time. Household size and ownership status also play a role, with smaller households and owners completing surveys faster. These findings highlight the importance of considering diverse demographic and contextual factors when designing and implementing transportation surveys to improve efficiency and effectiveness. 96 6. Chapter VI – Conclusion The overall objective of this thesis was to provide important insights and recommendations for various challenges related to the practice of collecting transportation data, with an emphasis on methods of recruitment. To achieve this objective, this research: a) examined the significance of transportation data derived from transportation surveys alongside the challenges and prevailing practices among experts in the field; b) developed and implemented a transportation survey in British; and c) investigated the efficiency of recruitment methods used in the time-use survey aforementioned. The Expert Workshop confirmed the importance of transportation surveys. This data serves multiple purposes across various domains, such as travel forecasting, optimizing resources and investments, and emission modelling. It also confirmed the need for these surveys to evolve to stay relevant in emerging trends (e.g., the rise of micro-mobility, car-share, working from home, etc.). The experts further confirmed the need for mixed recruitment and data collection methods. Where some methods fail, others can be utilized successfully. For example, phone calls might not effectively reach the younger population, but social media can be an effective alternative. Acknowledging the decline in participation in these surveys, the workshop brainstormed actionable strategies to revitalize engagement. The proposal to explore satellite surveys, especially in public events, and emphasize enhancing incentives, personalization, and trust-building measures are essential steps in addressing this challenge. This thesis analyzed the recruitment methods used in the British Columbia Activity Time Use Survey from October 26th to December 22nd of 2023. The findings underscore the importance of tailoring communication strategies to specific demographic and behavioural segments to maximize engagement and effectiveness. For example, the difference in completion rates for Address Only (3.57%) and Address & Phone (4.06%) indicates that having a phone number to communicate with participants through calls or SMS can increase the likelihood of completion. These rates could indicate that mail might not be a very effective method to recruit people for transportation surveys. However, more than 88% of completions came from the mail. Conversely, self-registrants had a significantly higher completion rate (40.95%), 97 although this group only accounted for 11.3% of households. The fact is, these methods complement each other. Those households that participated in the survey due to receiving mail invitations might not have been able to participate if the only recruitment method was self-registration, and vice-versa. In general, results indicate that SMS was the most effective reminder method. For self-registrants, 69% of households that received at least one SMS finalized the survey on the same day they got the SMS, with 40.5% completing the survey within 1 hour of receiving the SMS. For the address-based group, 47% of households completed within the same day. E-mail was also effective for this group: 41% of households that received an e-mail finalized the survey within the day of delivery. Only the address-based households that received an invitation letter through the mail were contacted by phone. Among these households, 12.5% completed the survey the same day they received the call, while another 12.5% completed it the following day. Regarding representativeness, the final dataset was similar to the census distribution, with most variables aligning within ±3%. This indicates that the spatial sampling technique successfully captured a representative population sample. As expected, self-registrants were mainly young female adults (25-39 years old). Social media proved useful for capturing this portion of the population, blending well with mail to create a representative sample. To further understand the efficiency of reminders, this study adopted an ordered logit modelling technique to analyze explanatory variables’ effect on a household’s need for frequent communication to complete the survey. It was found that self-registrants and older respondents need more frequent communication to finalize the survey. Self-registrants must complete a quality control process before receiving the access code to initiate the survey. The duration between registering and being granted access might be causing the need for more frequent reminders. It is suggested that the quality control process be streamlined to lower the frequency of reminders for such participants. Older participants (e.g., aged 60 and above) might require more frequent communication. This might indicate that the user interface needs to be 98 more user-friendly, specifically for the older population. It can also indicate that this group might prefer to have their data collected through another method altogether. Incorporating phone calls as a data collection method is recommended to enhance participation from individuals who may not be tech-savvy or lack internet access. Additionally, in-person interviews can be included if budget and human resources permit. An accelerated failure time model explored the duration to complete the survey without reminders. Given that more than 40% of households took more than eight days to complete the survey, this analysis shed light on which attributes contribute to the longer duration of completion. Overall, the results suggest that factors such as being in Metro Vancouver, owning a household, being older than 55 and being male are associated with a higher likelihood of shorter completion time without reminders. In comparison, full-time workers and single individuals are more likely to take longer to complete. Given that this group comprised the majority of households, it is recommended that a preference is given to addresses with a matched number for the next surveys because call and SMS are efficient methods to increase the likelihood of faster completions. 6.1. Contributions The main contributions of this thesis are threefold: the creation of a transportation survey forum via the expert workshop; the assessment of the efficiency of different recruitment methods; generation of newer insights on the effectiveness of different recruitment methods. The expert workshop on transportation surveys made a significant contribution to the literature by bringing together a diverse group of transportation engineers and planners to engage in a comprehensive dialogue about the current state and future directions of transportation surveys. This study confirms the importance of tailoring communication strategies to specific demographic and behavioural segments to maximize engagement and effectiveness. Employing multiple recruitment methods increases the likelihood of reaching a diverse population. For instance, while social media may not effectively engage older individuals, mail invitations remain a reliable method. 99 Mail has long been established as a robust recruitment tool in transportation surveys, with extensive coverage in existing research. This study reaffirms its importance in reaching segments of the population that might be inaccessible through other means. Notably, this research may be among the first large-scale transportation surveys to utilize SMS as a recruitment method. While the health sciences literature highlights numerous successful applications of SMS for participant recruitment, this study demonstrates its potential in transportation surveys. SMS proved to be valuable for sending reminders, thereby enhancing response rates. 6.2. Limitations This thesis did not discuss the questionnaire design. It is recommended that another study covers this part, especially regarding the 24-hour activity log. As many studies suggest, the activity diary is essential in decreasing respondents' perceived burden, and user-friendly features should be included (Aschauer et al., 2019; Stopher, 1992). The data-cleaning process used in this work was basic and primarily focused on socio-demographic information. Recommendation on cleaning regarding the activity log is advised. The process will probably be a complex one, including checking for missing and incomplete values, ensuring that the timestamps in the activity log are valid and consistent, looking for outliers in the data, such as unusually long or short activity durations, and deciding whether to keep or remove them based on the context. It is also recommended that consistency checks be performed to ensure that the activities reported in the log align with the survey's objectives and definitions. For example, it is important to ensure that activities are correctly categorized and that travel times make sense. Guaranteeing that data formats (e.g., time formats, activity categories) are consistent and standardized across the dataset is also important. The most important limitation is the single method of data collection used in the survey. The only way to complete the survey was via the website. This can make it hard for some socio-demographic groups to participate, such as the older population and people without internet access. It is important to note that this 100 survey also collected data via a smartphone app. To participate in the smartphone survey, participants needed to first complete the web-based survey. This thesis did not include the methodology of the smartphone app data collection. This should be explored in another work. There are also two limitations related to socio-demographics. As shown in the Census comparison, most people who participated in the survey are highly educated. Also, the fact that BC Transit shared the survey on their social media outlets might have created a bias towards public transit users. 6.3. Recommendations and Future Work Given the conclusions reached, a set of recommendations is listed below. If possible, these suggestions should be considered for the subsequent two waves of the British Columbia Activity Survey and any other transportation surveys deployed by the team. For the first wave, only web-based data collection was used. Some people might not be tech-savvy and have difficulty accessing a computer or internet. It would be best to have at least one more data collection method, such as telephone, paper or in-person interview. Many respondents sent e-mails to the administrator, vocalizing their struggles with the web-based tool and asking for another data collection method. The author recommends using telephone or in-person interviews as a secondary method due to the possibility of the interviewer using the web-based tool as a script during the interview. This should decrease what Eisenmann et al. (2019) presented as measurement effects, indicating trip-rate biases caused by mixing data collection methods (in their case, web-based and mail-back). Improvements in the activity diary should be considered, as many participants complained about being frustrated with the tool. A quick instructional video might help participants and increase the quality of data recorded. Furthermore, a study on the interview duration should be proposed, where the analysis could show the percentage of prospective respondents who gave up on completing the survey. It is expected that most of these drop-outs happened during the 24-hour activity log. 101 The survey started late in the year, running through November and December. With the drop in temperature and the subsequent snowfall, travel behaviour changes drastically in the region, with active transportation trips reaching deficient numbers. For the subsequent waves, it is recommended that the survey period starts as early as August or September to capture a better snapshot of how people move in the regions. Streamlining the pre-registration process is beneficial and important for self-registrants. Since these people registered independently, self-registrants' completion rates should be higher than 40.95%. However, it is also important to balance the restriction, given that the absence of it would implicate an extremely complex cleaning process. Strategies for dealing with households that received the invitation letter but never attempted to participate in the survey should be addressed. As discussed during the expert workshop, incorporating more outreach information, e.g., in-person campaigns at grocery stores, gatherings, parks, etc., might help boost the response rate. Another strategy could be to purchase only addresses with matched phone numbers, as calls and SMS can increase the response rate. Partner agencies should provide better support for the subsequent waves, especially given that many of them have a powerful presence in social media outlets. They should be encouraged to promote the survey through their social media accounts, increasing the likelihood of self-registrants and promoting better awareness. This might help especially for cities/districts, such as Peachland, Lake Country, Delta, Burnaby, and many others, which did not have a representative sample. Partners can also provide resources for the incentive campaign. It is also recommended pilot tests with and without incentives to experiment on the effectiveness of incentives. Results suggest that teleworkers need more reminders. It might be an interesting line of research to explore if an increase in the share of people working from home is associated with an increase or decrease in the response rate. 102 Finally, the survey described in this thesis is considered a core survey, aiming to collect the base data for state-of-the-art activity-based forecasting models. The literature argues that small surveys, called satellite surveys, are needed to complement the base data. These satellite surveys aim to collect specific behavioural attitudes, especially from groups that generally do not participate in transportation surveys, and they can also serve as validation for the core survey. Goulias et al., 2013 describe a series of satellite surveys and their purposes. 103 7. Bibliography Acuere. (2015). 2013 Okanagan Travel Survey Report 1: Survey Design and Conduct (p. 50). 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Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Fatmi, Mahmudur Rahman
Analyzing the efficacy of transportation survey recruitment methods
Text