Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Hossain, Md. Shahadat
Master of Applied Science - MASc
University of British Columbia
Autonomous vehicle (AV) promises to change the transportation landscape such as reducing traffic congestion and emissions. The success of this emerging technology largely depends on how individuals will adopt it. This thesis investigates individuals’ longer-term preferences for AVs, and their effects on vehicle ownership, specifically focusing on the preferences towards different levels of vehicle automation, AV ownership (AVO) and AV sharing (SAV), and vehicle transaction decisions in an AV future. A life history-oriented approach is adopted to examine the effects of historical experiences and changes over the life course, such as historical exposure to technology, the evolution of household characteristics, vehicle ownership history, historical measures of accessibility and built-environment on AV adoption. In addition, variables representing attitudinal factors, travel attributes, and socio-demographics are accommodated in the study. Data for the thesis comes from the retrospective survey conducted for the Okanagan region of British Columbia. In the case of preferences for different levels of vehicle automation, a random parameter rank-ordered logit model is developed to accommodate rank-ordered preferences for vehicular automation levels and capture unobserved heterogeneity. For preferences towards AVO and SAV, a joint bivariate ordered probit model is developed to address the error correlation between decisions. The study confirms that a significant error correlation exists, which indicates that the unobserved factors jointly affect the choice alternatives. A latent class random parameter logit modelling technique is utilized to capture heterogeneity while modelling vehicle transaction decisions in the AV future. The model results confirm the existence of historical deposition effects on individuals’ preferences towards AVs. For example, individuals with historical exposure to vehicle technology, such as availability of lane assist, parking assist, and autonomous emergency stop, have a higher likelihood of adopting higher levels of vehicle automation. Besides, they are more likely to add AV to the current vehicle fleet. Similarly, the AVO and SAV model results suggest that individuals with a higher number of smartphone ownership over the life course are more likely to adopt both. The model results also confirm the existence of heterogeneity. Overall, the findings of the study provide important behavioural insights and have significant policy implications that might be used for targeted marketing to promote AVs.
MODELLING INDIVIDUALS’ LONGER-TERM PREFERENCES TOWARDS AUTONOMOUS VEHICLES AND THEIR EFFECTS ON VEHICLE OWNERSHIP by Md Shahadat Hossain B.Sc., Bangladesh University of Engineering and Technology (BUET), 2017 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 2021 © Md Shahadat Hossain, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: MODELLING INDIVIDUALS’ LONGER-TERM PREFERENCES TOWARDS AUTONOMOUS VEHICLES AND THEIR EFFECTS ON VEHICLE OWNERSHIP submitted by Md Shahadat Hossain in partial fulfillment of the requirements for the degree of Master of Applied Science in Civil Engineering Examining Committee: Dr. Mahmudur Fatmi, School of Engineering Supervisor Dr. Gordon Lovegrove, School of Engineering Supervisory Committee Member Dr. Zheng Liu, School of Engineering Supervisory Committee Member Dr. Mohammad Khalad Hasan University Examiner iii Abstract Autonomous vehicle (AV) promises to change the transportation landscape such as reducing traffic congestion and emissions. The success of this emerging technology largely depends on how individuals will adopt it. This thesis investigates individuals’ longer-term preferences for AVs, and their effects on vehicle ownership, specifically focusing on the preferences towards different levels of vehicle automation, AV ownership (AVO) and AV sharing (SAV), and vehicle transaction decisions in an AV future. A life history-oriented approach is adopted to examine the effects of historical experiences and changes over the life course, such as historical exposure to technology, the evolution of household characteristics, vehicle ownership history, historical measures of accessibility and built-environment on AV adoption. In addition, variables representing attitudinal factors, travel attributes, and socio-demographics are accommodated in the study. Data for the thesis comes from the retrospective survey conducted for the Okanagan region of British Columbia. In the case of preferences for different levels of vehicle automation, a random parameter rank-ordered logit model is developed to accommodate rank-ordered preferences for vehicular automation levels and capture unobserved heterogeneity. For preferences towards AVO and SAV, a joint bivariate ordered probit model is developed to address the error correlation between decisions. The study confirms that a significant error correlation exists, which indicates that the unobserved factors jointly affect the choice alternatives. A latent class random parameter logit modelling technique is utilized to capture heterogeneity while modelling vehicle transaction decisions in the AV future. The model results confirm the existence of historical deposition effects on individuals’ preferences towards AVs. For example, individuals with historical exposure to vehicle technology, such as availability of iv lane assist, parking assist, and autonomous emergency stop, have a higher likelihood of adopting higher levels of vehicle automation. Besides, they are more likely to add AV to the current vehicle fleet. Similarly, the AVO and SAV model results suggest that individuals with a higher number of smartphone ownership over the life course are more likely to adopt both. The model results also confirm the existence of heterogeneity. Overall, the findings of the study provide important behavioural insights and have significant policy implications that might be used for targeted marketing to promote AVs. v Lay Summary To adequately assess the impacts of AVs on the transport network and environment, it is critical to understand individuals’ longer-term preferences towards this technology and the corresponding effects on their household vehicle fleet. This thesis investigates preferences for AVs by exploring individuals’ preferences towards different levels of vehicle automation, adoption of AVs as personal and shared vehicles, and vehicle transaction decisions in an AV future. Advanced econometric models are adopted to explore the existence of correlations among decisions and unobserved heterogeneity among individuals. A life history-oriented approach is adopted to confirm the effects of historical exposure to technology (e.g. advanced technology in the vehicle), household characteristics (e.g. home-ownership and dwelling type), and accessibility measures (e.g. historically lived away from the urban core) on AV adoption. Further testing reveals the influence of individuals’ attitudes, socio-demographics, and travel attributes. The results provide important behavioural insights that might help in effective AV-related policymaking. vi Preface The majority of the contents of the thesis are under review for publication. Chapter 4 is under 2nd stage review and chapter 5 is under final stage review in peer-reviewed journals. Chapter 6 is submitted for publication in a peer-reviewed journal. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ xi List of Figures .............................................................................................................................. xii List of Abbreviations ................................................................................................................. xiii Acknowledgements .................................................................................................................... xiv Dedication ................................................................................................................................... xvi Chapter 1: Introduction ................................................................................................................1 Longer-term Effect of AVs on Mobility ......................................................................... 1 Objectives ....................................................................................................................... 4 Outline of the Thesis ....................................................................................................... 4 Chapter 2: Literature Review .......................................................................................................6 Introduction ..................................................................................................................... 6 Simulation-based Studies ................................................................................................ 7 Survey-based Analysis on Consumers’ Perception and Intended Adoption of AVs ...... 8 Modelling AV Adoption ............................................................................................... 10 Effect of Socio-demographic Characteristics ........................................................... 12 Effect of Latent Attitudinal Factors .......................................................................... 12 Effect of Travel Attributes ........................................................................................ 14 viii Effect of Land Use and Built-environment Attributes .............................................. 15 Research Gaps ............................................................................................................... 15 Research Questions ....................................................................................................... 17 Summary ....................................................................................................................... 17 Chapter 3: Conceptual Framework and Data...........................................................................19 Introduction ................................................................................................................... 19 Theoretical Context ....................................................................................................... 19 Life History-oriented Approach ................................................................................ 19 Exposure to Technology ........................................................................................... 20 Conceptual Framework of the Study ............................................................................ 21 Data Sources ................................................................................................................. 22 Primary Data Source ................................................................................................. 22 Secondary Data Sources ........................................................................................... 24 Summary ....................................................................................................................... 24 Chapter 4: Individuals’ Preferences towards Different Levels of Vehicle Automation ........26 Introduction ................................................................................................................... 26 Data ............................................................................................................................... 26 Methodology: Random Parameter Rank-ordered Logit Model .................................... 27 Independent Variables Considered ............................................................................... 30 Result and Discussion of the RPROL Model ............................................................... 33 Goodness-of-fit Measures ......................................................................................... 33 Parameter Estimation Results ................................................................................... 34 Summary ....................................................................................................................... 40 ix Chapter 5: Individuals’ Preferences for Ownership and Sharing of AV ...............................42 Introduction ................................................................................................................... 42 Data ............................................................................................................................... 42 Methodology: Joint Bivariate Ordered Probit Model ................................................... 45 Independent Variables Considered ............................................................................... 48 Results and Discussion of the JBOP Model ................................................................. 51 Goodness-of-fit Measures ......................................................................................... 51 Parameter Estimation Results of the AVO Component ............................................ 53 Parameter Estimation Results of the SAV Component ............................................ 55 Summary ....................................................................................................................... 61 Chapter 6: Modelling Vehicle Transaction Decisions in the AV Future ................................65 Introduction ................................................................................................................... 65 Data ............................................................................................................................... 66 Methodology: Latent Class Random Parameter Logit Model ...................................... 66 Independent Variables Considered ............................................................................... 69 Results and Discussion of the LCRPL Model .............................................................. 72 Goodness-of-fit Measures ......................................................................................... 72 Segment Allocation Component ............................................................................... 73 Parameter Estimation Results of the Vehicle Transaction Decisions Component ... 74 Summary ....................................................................................................................... 78 Chapter 7: Conclusions ...............................................................................................................81 Summary of the Study .................................................................................................. 81 Contributions of this Thesis .......................................................................................... 85 x Future Research ............................................................................................................ 86 References .....................................................................................................................................88 Appendices ....................................................................................................................................98 Appendix A : Travel Technology and Mobility Survey (TTMS) ............................................. 98 Appendix B : Comparison of Different Attributes between TTMS and Census Statistics ..... 108 xi List of Tables Table 4.1 Factor Analysis Result Showing Attitude towards Travel Modes. ............................... 32 Table 4.2 Factor Analysis Result Showing Attitude towards Land Use and Environment. ......... 33 Table 4.3 Summary Statistics of the Variables Retained in the Final Model. .............................. 35 Table 4.4 Parameter Estimation Results of the RPROL model. ................................................... 37 Table 5.1 Cross-tabulated summary statistics of the preference towards AVO and SAV. .......... 43 Table 5.2 Factor Analysis Result Showing Attitude towards Travel Modes, Land Use and Environment. ................................................................................................................................. 50 Table 5.3 Summary statistics of the variables retained in the final model. .................................. 52 Table 5.4 Parameter estimation results of the BOP model. .......................................................... 56 Table 5.5 Marginal effects of the variables retained in the joint BOP model. ............................. 59 Table 6.2 Summary statistics of the variables retained in the LCRPL model. ............................. 70 Table 6.3 PCA Results Showing Attitude towards AVs and different travel modes. .................. 71 Table 6.4 PCA Results Showing Attitude towards Land Use and Environment. ......................... 71 Table 6.5 Comparison of goodness-of-fit measures. .................................................................... 73 Table 6.6 Segment allocation component of LCRPL model. ....................................................... 73 Table 6.7 Vehicle Transaction decisions component results of the LCRPL model. .................... 77 xii List of Figures Figure 3.1 The Conceptual Framework of the Study. ................................................................... 21 Figure 5.1 Preferences for AVs for different Home Ownership Types Over the Life Course. .... 44 Figure 5.2 Preferences for AVs for Google Home Ownership in the Household. ....................... 44 Figure 5.3 Preferences for AVs for Smartphone Ownership per Person in the Household. ......... 45 Figure 6.1 Distribution of the dependent variables. ...................................................................... 67 xiii List of Abbreviations AV = Autonomous Vehicle SAV = Shared Autonomous Vehicle AVO = Autonomous Vehicle Ownership GHG = Green House GAS MNL = Multinomial Logit OL = Ordered Logit OP = Ordered Probit BOP = Bivariate Ordered Probit JBOP = Joint Bivariate Ordered Probit LCRPL = Latent Class Random Parameter Logit ROL = Rank Ordered Logit RPROL = Random Parameter Rank Ordered Logit BC = British Columbia AIC = Akaike Information Criteria BIC = Bayesian Information Criteria LUI = Land Use Index GPS = Global Positioning System xiv Acknowledgements First and foremost, all praise to the Almighty for blessing me with the opportunity and strength to study at the University of British Columbia Okanagan (UBCO) and continue my research for the past two years. I would like to offer my enduring gratitude to a number of people for the tremendous support, encouragement, and guidance throughout my tenure as a graduate student at UBCO. I would like to express my sincere gratitude to the most influential person during my tenure at UBCO, my supervisor Dr. Mahmudur Fatmi. I am grateful for the relentless support, guidance, and overwhelming encouragement from him. I feel very proud to be a part of his research group and discuss and share research ideas with him. I am grateful to him for always being available for me when I am stuck in my research and continuously motivating me to produce more and more publications. His mentorship has helped significantly improve both my academic and interpersonal standards. I would like to thank my supervisory committee members, Dr. Gordon Lovegrove and Dr. Zheng Liu. Thank you for your time to review my research and valuable comments, which have helped me to significantly improve the thesis. I would like to thank my colleagues at the Center for Transportation and Land Use Research (CeTLUR), specifically Bijoy, Muntahith, and Shivam. Thanks for those conversations and meetings, which have helped me a great deal to learn the basics and improve my skills. I would like to thank my parents, brother, sister and my in-laws for providing me tremendous support and encourage me to pursue my dream. I would like to acknowledge the significant role xv of my wife, Binti, in my graduate life. Without her constant support and presence behind the scene, it would be impossible to fulfill my dream. xvi Dedication Dedicated To My Grandfather, My Childhood Mentor Late Md Abdul Aziz Bhuiyan 1 Chapter 1: Introduction Longer-term Effect of AVs on Mobility Autonomous vehicle (AV) technology is significantly changing the modern era of transportation. Besides, it might make driving easier and offer more productive use of time while travelling (Lavieri et al., 2017; Nazari et al., 2018). Higher levels of vehicular automation will be a potential mobility solution for many in the future as it might be a safer travel option due to its ability to reduce human errors while driving, such as distracted and impaired driving (Lee et al., 2020). According to Fagnant & Kockelman (2015), the fatal crash rate can be reduced by almost 40% if AVs are to replace the conventional vehicles on the road where considering a minimal malfunction of the automated features. AVs hold the potential to reshape the urban transportation landscape by significantly reducing traffic congestion, GHG emission, and the requirement of parking areas while improving the overall traffic safety and road network performance (Bansal & Kockelman, 2017; Menon, 2017; Shabanpour et al., 2018). Since AVs will not require any driver, ownership of an AV as a personal vehicle will provide the opportunity to engage in different activities - ensuring productive use of travel time such as socializing, working, sleeping, and multitasking (Nazari et al., 2018). In addition, a certain group of individuals who are unable to drive because of their physical, mental, and other conditions (e.g. elderly population, children, disabled persons) may highly be benefited from vehicle automation. The shared mobility services such as car-sharing (e.g. MODO, ZipCar) and ride-hailing (e.g. Uber, Lyft) companies can further leverage this technology to provide on-demand mobility 2 services with a smaller fleet of shared autonomous vehicles (SAV) (Lavieri et al., 2017). Besides, SAVs also have the potential to facilitate multimodal transportation systems by becoming the last-mile mode of transportation, such as from transit stops to the destination (Menon et al., 2019). Moreover, the requirement for carsharing parking lots can be significantly reduced as well as more innovative and compact parking facilities can be designed, which will ensure efficient use of land areas (Fagnant & Kockelman, 2015; Nourinejad et al., 2018). Although AV promises a lot of advantages, it might have some negative consequences on the transportation network and environment. For example, the ownership of AVs might contribute to producing more empty vehicle kilometres travelled (Lavieri et al., 2017), as individuals might send back their AVs to chauffer the family members after reaching their destinations. Thus, AVs might significantly contribute to GHG emissions based on the fuel type. Besides, excessive AV ownership might eventually lead to increased congestion on the road network. The overall success of AVs largely depends on individuals' perceptions of adopting this technology. The shorter-term usage of AVs, such as using them for shopping trips, will be dictated by the longer-term adoption of AVs, such as preference towards different levels of automation (e.g. partially automated and fully automated vehicles) and method of adoption (e.g. own it or use it as a vehicle in the shared fleet). In addition, the increased accessibility and other benefits of AVs may trigger the need for adjustment of the household vehicle fleet and consequently reduce congestion and vehicle-related emissions. For example, the ownership of AVs may eventually lead the households to own fewer vehicles as they are capable of pick-up and drop-off the household members without the need for someone to drive the vehicle. Besides, SAVs may encourage individuals to reduce the hassle of owning and maintaining a vehicle. 3 Therefore, it is critical to understand whether and how individuals are adjusting their household vehicle fleet size. AV is one of the most disruptive technologies, and the longer-term adoption of this technology might be associated with the retrospective changes and experiences over the life course of an individual. For example, individual’s both past and present experience with similar technology such as driver-assistive features in the vehicles might attract individuals to adopt more advanced technology like AVs in the future. Similarly, past ownership and usage of different technologies, such as smartphones, computers, google home etc., might further encourage people to embrace the new technology. Therefore, the effects of individuals' historical exposure to technology in different life domains such as technology in the past and present vehicles as well as the ownership of other technology such as smartphone, computer, and google home over the life course on the longer-term adoption of AVs need to be investigated. In addition, effects of factors including attitudinal constructs such as pro-AV, pro-car, pro-environment etc., accessibility measures such as distance to the urban core and nearest points of interest from the residence, and built-environment attributes such as land use mix, requires to be tested. In the case of modelling preference towards AVs, there might be heterogeneity among individuals; for example, the individuals with higher exposure to vehicle technology, high yearly income, pro-car attitude, and multiple travel tools. Furthermore, AV adoption decisions might be interdependent. For instance, individuals’ preferences towards owning and sharing AVs might be correlated. Overlooking the potential correlation and heterogeneity might lead to inaccurate estimation of the model. Therefore, appropriate econometric models need to be adopted to address such heterogeneity and inter-dependencies among decisions. 4 Objectives The goal of the thesis is to investigate individuals’ life-history effects on their longer-term preference and adoption of autonomous vehicles. The specific objectives of the thesis are as follows: • To investigate individuals’ preferences for different levels of vehicle automation A random parameter rank-ordered logit modelling technique is utilized to account for unobserved heterogeneity. • To examine individuals’ preferences for AV ownership and sharing A bivariate ordered probit model is adopted to jointly model individuals’ preference for AV as a privately-owned vehicle and a vehicle in the shared service. • To investigate individuals’ vehicle transaction decisions in the AV future The effects of AV adoption are investigated on a household’s vehicle fleet size assuming vehicle ownership as a dynamic decision of vehicle transactions, such as acquisition and disposal of vehicles. A latent class random parameter logit model is utilized to capture heterogeneity among the sample individuals. Outline of the Thesis The thesis comprises seven chapters. The organization of the thesis is as follows: Chapter 2 includes a comprehensive literature review on autonomous vehicles, their intended adoption, and their potential impact on individuals’ travel behaviour and vehicle ownership. This chapter discusses different approaches to explore the adoption decisions of AVs and the underlying factors affecting their preferences. Finally, the gaps in the existing literature and the contribution of the study are discussed in this chapter. 5 Chapter 3 discusses the data and the conceptual framework of the study. This chapter also provides a comprehensive discussion on different variables that were considered for modelling purposes. Chapter 4 presents the methodology and analysis results of the RPROL model to investigate individuals’ preferences for different levels of vehicle automation. Chapter 5 focuses on developing the JBOP model for investigating individuals’ preference for AV ownership and sharing. The chapter discusses the parameter estimation results and the elasticity estimates of the variables. Chapter 6 discusses the vehicle transaction model followed by the parameter estimation results. Finally, Chapter 7 concludes the thesis by summarizing the key findings, providing policy measures, and discussing the limitations and future researches. 6 Chapter 2: Literature Review Introduction Technological advancement in recent years has introduced newer vehicle technologies such as autonomous vehicles (AVs) and mobility services such as car share and ride-hailing services. AVs are considered to be the most disruptive technology of this era (Nazari et al., 2018). According to the SAE, fully autonomous vehicles (i.e., standard automation level 5) are expected to be on the roads very soon (National Highway Traffic Safety Administration, 2017). The availability of such technology on a large scale, both in the form of PAV and SAV, might significantly affect individuals’ travel behaviour and vehicle ownership. Therefore, it is critical to understand the adoption decisions of AV in order to understand the impact on mobility and vehicle ownership. The primary objective of the study is to examine how experiences in different life domains along the life course, such as historical exposure to technology in daily life, historical exposure to technology in the vehicle fleet, and historical change in household demographics, affect the adoption of AVs. In addition, the study analyzes the effect of individuals’ attitudinal factors, travel characteristics, accessibility, and socio-demographic attributes on the adoption of AVs. This next section of this chapter includes a comprehensive literature review on different survey-based studies and empirical analysis-based studies regarding the adoption of AVs and the potential impact of AVs on household vehicle ownership. The following section discusses the research gaps in terms of behavioural analysis, historical deposition effects, and the contribution 7 of the study by addressing the research questions. Finally, the chapter is ended with concluding remarks. A wide array of studies have been conducted to understand the potential adoption of different levels of vehicle automation and the adoption of AVs. Among these studies, some contribute to the literature by applying simulation techniques to investigate the effect of AVs on road network performance, GHG emission, and household vehicle ownership. Several studies conducted statistical analysis to understand people’s perception towards AVs, their interest in different levels of vehicle automation and the intended adoption of AVs. Finally, some studies investigated the AV adoption decisions by developing econometric modelling methodologies and providing more behavioural insights. The following sections discuss an overview of the earlier researches conducted to investigate AV adoption decisions by categorizing them into simulation-based, econometric modelling-based, and survey-based studies. Simulation-based Studies A large body of the existing literature on AVs has focused on testing the impact of AV adoption on the transportation network and the environment. For example, Li and Wagner (2019) developed a microsimulation model to test the effect of AVs on the transportation network of Auckland, New Zealand. Stogios (2018) used microsimulation and emission modelling techniques to evaluate the performance of AVs and electric vehicles on GHG emission reduction as well as traffic flow improvement. The findings of this study suggested that AVs significantly improve traffic operations while reducing GHG emissions. As discussed above, the majority of the previous studies have adopted a microsimulation modelling approach and assumed the adoption rates of AVs on an ad hoc basis. The adoption of AVs is one of the most critical inputs for the simulation tools, which has a significant impact on the resulting transportation and 8 environmental benefits. To improve the accuracy of these microsimulation models and generate a more realistic impact of AVs, it is important to develop models that represent individuals’ behaviour towards AV adoption. Such understanding of individuals’ preferences towards AV adoption and usage is critical for effective transportation and land use planning. Survey-based Analysis on Consumers’ Perception and Intended Adoption of AVs Due to the recent advancement in vehicle autonomy, it is expected that the role of drivers is going to be changed significantly. With the increase in different advanced driver-assistive features, the responsibility of the driver has a higher likelihood of changing from actively controlling the vehicle to passively monitoring the road environment and assist the autonomous system if necessary (Naujoks et al., 2016; Scherer et al., 2015). Therefore, it is critical to understand how individuals are adapting to these changes in driving responsibility and their preferences for different levels of vehicle automation. Several studies have conducted stated preference survey for collecting respondents’ perceptions about AVs and intended adoption of this technology (Abraham et al., 2014, 2018; Ahangar et al., 2021; Das et al., 2020; Lee et al., 2018, 2020; Schoettle & Sivak, 2014; Wu et al., 2020). For example, Abraham et al. (2014) conducted an online survey in the USA where the respondents were recruited nationwide. Only the individuals who own a vehicle and are old enough to legally drive are included in the data. In the survey, respondents were asked to give information regarding their satisfaction with the technology available in their currently owned vehicle, their preference towards driving alternatives and AV technology. They found that younger individuals aged 16 to 24 are highly interested in full and higher levels of automation. Overall, the proportion of the respondents who are comfortable with full automation are significantly lower than the preference for partial automation for the older individuals. Another study by Abraham et al. (2018) in the same study 9 area shows that the majority (59%) of the respondents are comfortable with partially automated vehicles where the preference for AV is only 13%. Lee et al. (2020) conducted an online survey in the USA from 2017 to 2018 to understand the trends of consumers’ perception and trust towards different levels of vehicle automation. The findings suggest that all age groups show increased interest in full automation except the individuals aged 75 or more. The advancement of vehicle technology and more familiarity with similar types of technology are identified as the reason behind this increased interest. Hence, it is critical to understand the longer-term AV adoption decisions to better understand the success of this technology. Apart from age, analysis of other socio-demographic characteristics such as gender, level of education, and household income are the significant factors that affect individuals’ interest in different levels of vehicle automation. For example, males are found to be more interested in AVs than females (Abraham et al., 2014; Lee et al., 2018). Individuals living in a very high-income household (yearly household income ≥ $150,000) are more interested in full automation than the other income groups (Lee et al., 2018). Kyriakidis et al. (2015) conducted a worldwide online survey across 109 countries to investigate the interest and acceptance of partially and fully automated vehicles among the respondents. The survey collected information regarding individuals’ age, income, frequency of driving, mileage, and preference for different levels of vehicle automation. The study suggests that higher-income individuals and those who drive more are most likely to adopt higher vehicle automation. Furthermore, the study predicted that past experience such as driving vehicles with advanced technology features is a significant factor of future adoption of vehicle automation. Payre et al. (2014) conducted a semi-directive interview among French drivers in order to investigate the intention to use and the acceptability of the AVs. Respondents were provided with a short 10 description of the AVs for better demonstration and collected information regarding their interest in AVs and their preferred use. The majority of the drivers (68.1%) were positive about using AVs, and the preferred usage was for highway driving, during traffic congestions, and for parking. Another study by Schoettle & Sivak (2014), conducted survey in the USA, UK, and Australia to analyze individuals’ perception towards AVs where majority of the respondents were positive about riding a driverless car. However, majority of them were also concerned about the safety while riding whereas they were optimistic about the potential benefits of the vehicle automation. Modelling AV Adoption Vehicle technology is rapidly evolving. Adopting and usage of the higher level of vehicle automation and AVs will be a paradigm shift towards adopting major technology. Adoption of a disruptive technology like AV is a significant decision that might be associated with the long-term as well as short-term effects of individuals’ socio-demographic, latent attitudinal factors, travel characteristics, land use, and built-environmental attributes. A large body of literature exists that examined the cross-sectional effect of these attributes by developing econometric modelling methodologies. For instance, Lavieri et al. (2017) developed a generalized heterogeneity data model (GHDM) using the 2015 Puget Sound Regional Travel Study (PSRTS) survey data to understand individuals’ adoption of AV as a privately owned or a shared vehicle. Privately owned AV refers to the ownership and inclusion of AVs in the household’s vehicle fleet, which might add flexibility to chauffer household members. In the case of AVs as shared vehicles, this technology might be acquired by shared mobility service providers such as car-sharing and taxi companies. The GHDM model has two components; one is the structural equation model, and the other one is the measurement equation model. The study investigates 11 individuals’ interest in AVs based on four discrete choices such as no interest, interested in SAV, interested in AVO, and both. Several studies used ordered modelling techniques to account for the ordered preferences of the individuals towards different forms of AVs. For example, Menon et al. (2020) developed a random parameter ordered probit model to understand the adoption of AVs of different market segments, which captures the unobserved heterogeneity among the respondents. The study utilized the data from a web-based survey conducted among the faculty, students, and the staff of the University of South Florida and the members of the American Automobile Association of the southwest US. An MNL model is developed to identify the factors affecting the membership of different market segments. Nair et al. (2018) applied a rank-ordered probit modelling technique, using the 2015 PSRTS data to investigate the relative preference for AVs. The study considered four choices regarding AVs, such as AV as a taxi without a backup driver, AV as a taxi with a backup driver, AVO, and AV car-share. Few studies have explored the unobserved error-correlation among the AV preferences by developing joint modelling techniques. For example, Nazari et al. (2018) utilized a multivariate ordered probit model to examine the factors affecting individuals’ interest in AVO and different forms of shared usage. The modelling method is capable of revealing the unobserved error correlation that might jointly affect individuals’ preferences while accommodating the ordered preferences. The key factors that significantly affect individuals AV adoption decisions identified by different studies are broadly discussed below: 12 Effect of Socio-demographic Characteristics The majority of the studies have investigated the socio-demographic characteristics of the individual and household while modelling the adoption decisions of AVs. Among the socio-demographics, age, individual and household income, gender, number of adults and children, level of education, and employment status are the significant factors, which affect the choice of AVs. High-income (yearly income ≥ $100,000) households are more likely to prefer SAV, whereas the effect of income was not significant for AVO (Nair et al., 2018). Similarly, younger individuals are more likely to prefer SAV (Lavieri et al., 2017; Nazari et al., 2018) as well as AVO (Nazari et al., 2018). Males are found to be more interested in AVs relative to females (Lavieri et al., 2017; Menon et al., 2019; Nair et al., 2018; Nazari et al., 2018). In the case of level of education, individuals with a bachelor's as well as graduate degree are more interested in both AVO and SAV (Lavieri et al., 2017; Menon et al., 2019; Nair et al., 2018). Full-time workers are more likely to use the SAV, whereas self-employed individuals are more likely to own AVs as well as use SAV (Nazari et al., 2018). In the case of the presence of children in the household, different studies have suggested both positive and negative effects on the preference for both AVO and SAV. For instance, Haboucha et al. (2017) found that the presence of children leads to a lower likelihood of choosing SAV. However, the majority of the studies suggested that the number of children in the household has a positive effect on the choice of owning AV (Nair et al., 2018) and as a shared vehicle (Haboucha et al., 2017; Menon et al., 2019; Nazari et al., 2018). Effect of Latent Attitudinal Factors Several studies have found significant effects of attitudinal factors on the adoption of AV both as a private and shared vehicle. Haboucha, Ishaq, and Shiftan (2017) derived several latent 13 attitudinal variables using factor analysis such as Pro-AV, enjoy-driving, and environmental concern. Pro-AV attitude refers to the individuals who believe that AV might make life easier by reducing the hassle associated with parking, increasing mobility, and are comfortable with the pick-up or drop-off of children by AVs. On the other hand, individuals who love to drive and feel safe while driving themselves are considered to have an enjoy-driving attitude. Attitude representing a more serious concern about GHG emission and global warming is identified as an environmental concern attitude. The findings of the study suggest that the pro-AV attitude significantly impacts the choice of using both PAV and SAV, whereas the enjoy-driving attitude leads to choose the conventional vehicle. Environment-concerned individuals are more interested in SAVs. Using a structural equation modelling framework, Lavieri et al. (2017) derived latent lifestyle variables such as technological savviness and green lifestyle. Technological savviness variable is derived from the attributes such as the number of smartphone ownership, frequency of smartphone application usage for traffic-related information, and frequency of using GPS while travelling. Furthermore, the green lifestyle variable is generated by analyzing the frequency of transit use, the importance of living in a walkable neighbourhood, close to transit stops and living close to the work location. The findings of the study suggest that tech-savvy individuals have a higher likelihood of adopting AVs both as privately-owned and shared vehicles, whereas a green lifestyle leads to the choice of using SAV. Similarly, Nazari et al. (2018) derived two latent variables termed mobility-savviness and green travel pattern, using the same modelling technique. The mobility-savviness is associated with smartphone ownership, app-based ridesharing and ride-sourcing usage, which represents individuals’ familiarity in using mobility-on-demand services. The model results revealed a significant positive relationship of this 14 variable with all the alternatives, which further unfolded the significant effect of technology on AV adoption decisions. The green travel pattern corresponds to frequent use of sustainable transport such as transit, walk and bike, and living in a walkable neighbourhood. This variable shows a similar effect on the potential adoption of SAV, as suggested by Haboucha, Ishaq, and Shiftan (2017). Furthermore, safety concern associated with the AVs is another significant latent variable, which affects the adoption of this technology (Nazari, 2019). Effect of Travel Attributes Several studies have investigated the effect of travel attributes such as mobility tool ownership, vehicle ownership, commute mode, commute distance and frequency, and travel time on the future adoption of AVs. Mobility tools correspond to the tools which facilitate the usage of different travel modes (Beige & Axhausen, 2006; Fatmi et al., 2014). For example, mobility tools include the ownership of vehicles, driver’s licence, seasonal or monthly transit pass, subscription to rideshare etc. Ownership of driver’s licence is found to positively affect the interest in using SAV, whereas the effect is not significant in the case of owning AV (Nazari et al., 2018). Individuals who drive alone or carpool to work s are inclined towards AVO, whereas transit riders are more interested in SAV (Nair et al., 2018). Individuals’ prior experience in using ride-sourcing or carsharing may lead to use the shared services provided by AVs and, therefore, lower interest in owning AV (Lavieri et al., 2017). Similarly, ownership of no vehicle or fewer vehicles leads to a higher likelihood of using SAV AV (Krueger et al., 2016; Lavieri et al., 2017). Haboucha et al. (2017) and Nazari et al. (2018) suggested a positive relationship of higher travel time with the propensity of using SAV and PAV as using AVs may ensure more productive use of travel time. 15 Effect of Land Use and Built-environment Attributes Land use mix diversity and built-environment characteristics are significant factors that might impact the adoption decision of AV. Higher land use mix enables an individual to access different points of interest by making shorter trips. Individuals might choose to use SAV for making such short trips in these urban areas with high residential density and higher parking constraints (Lavieri et al., 2017; Nazari et al., 2018). On the other hand, Nazari et al. (2018) suggested that suburban dwellers are less interested in both AVO and SAV because of the lower accessibility to different land use. Research Gaps AV is considered one of the most disruptive technologies of this era. The adoption of different levels of vehicle automation as well as the adoption of AV in the form of owned and shared vehicles might be associated with the retrospective changes and experiences of individuals’ life course. Specifically, individuals' perception towards AV might be influenced by their previous exposure to technology, such as availability of technologies (e.g. lane departure warning, autonomous emergency stop etc.) in their existing or previous vehicle fleet. Besides, individuals’ historical household demographics, such as dwelling type, ownership type of the house and household income over the life course, might also influence the longer-term decisions and preferences for AVs. Furthermore, the historical accessibility measures and built-environment characteristics of the residence, such as distance from residence to the urban core and the nearby points of interest (e.g. school, restaurant, shopping center, healthcare center etc.), and the land use mix of the neighbourhood might significantly affect the decisions regarding the adoption AVs. Although consumers’ adoption of new technology is affected by their both present and past experiences and needs (Saaksjarvi, 2003), little to no studies have explored the retrospective 16 effects of decisions and experiences such as historical exposure to technology, historical household characteristics, and accessibility of the residential location over the life course on the adoption of AV technology. The introduction of AVs on the road might significantly affect the household vehicle fleet as the ownership of an AV, and the shared use might trigger the disposal of vehicles from the current vehicle fleet. Availability of AVs in different forms might lead the individuals to change their longer-term decision, such as vehicle ownership (Kim et al., 2020). Several studies have explored the effect of both AVO and SAV on vehicle fleet management and transaction decisions. For instance, W. Zhang et al. (2018) applied a vehicle scheduling algorithm using the 2011 Atlanta travel survey data in order to explore the potential vehicle reduction in the presence of private AVs. Analyzing the trip patterns of the study area, it is found that more than 18% of the household can reduce their vehicle fleet without compromising their present travel behaviour, which will result in an overall 9.5% reduction in private vehicles in the study region. Fagnant et al. (2016) conducted a simulation-based study in Austin, Texas to explore the impact of SAV on the vehicle ownership. The study considers 1.3% of the regional trips to be made by SAV and run the simulation in a 12x24 square mile area in the regional core of Austin. The results indicate huge potential of SAV in vehicle ownership reduction as a SAV can outperform nine conventional vehicles while maintaining the same level of service in the designated area. Considering the huge potential of AVs to reduce the conventional vehicle fleet, it is critical to understand how longer-term vehicle transactions might occur in the presence of AVs. In the case of empirical analysis, developing advanced models need to be considered to address the limitations of traditional ordered and discrete choice models by capturing unobserved heterogeneity among the sample individuals and error correlation among different choices. 17 Inability to capture such heterogeneity and error correlation might result in biased estimation of parameters. Research Questions Based on the discussion above, the following research questions are addressed in this study: 1. How individuals’ preferences for different levels of vehicle automation is affected by the historical changes and experiences of individuals’ life course? 2. What are the underlying factors for individuals’ preference for AV ownership and sharing? Is there any unobserved error correlation among the choices that jointly affect the decision to adopt AVO and SAV? 3. What are the contributing factors that affect individuals’ vehicle transaction decisions in the presence of AVs? 4. Is there any existence of unobserved heterogeneity among the individuals? Summary Based on the above discussions, it can be summarized that a wide array of researches has been conducted on individuals’ preferences for AVs. However, little to no studies have focused on the effect of longer-term changes and experiences of individuals’ life course on their choices, such as different levels of vehicle automation, the intended adoption of AVs as a private and shared vehicle, and the potential impact of AVs on household vehicle ownership. This study adopts a life history-oriented approach to investigate the adoption of AV. One of the major contributions of this study is to examine how preference towards AVs is influenced by the retrospective changes and experiences such as exposure to technology, vehicle ownership history, and historical measures of accessibility and built-environment of the home location over the life-18 course of the individuals. This research also tests the effects of individuals’ attitudes, such as attitude towards travel modes, land use, and environment. Methodologically, the study developed several advanced econometric models. The preference for different levels of vehicle automation is investigated using a random parameter rank-ordered logit (RPROL) modelling approach to accommodate unobserved heterogeneity. In the case of modelling individuals’ preference towards AVO and SAV, a joint bivariate ordered probit (JBOP) model is developed to capture the unobserved error correlation, which jointly affects the preferences. Finally, a latent class random parameter logit (LCRPL) model is developed to investigate individuals’ vehicle transaction decisions in the AV future. The model captures heterogeneity, assuming both discrete and continuous distribution of the parameters. 19 Chapter 3: Conceptual Framework and Data Introduction To better understand the longer-term decision of individuals regarding the adoption of AVs, a dynamic modelling framework is required. A life history-oriented approach of modelling is adopted to capture the dynamics of the individuals’ preferences towards AVs. A retrospective survey data conducted in the Okanagan region of British Columbia (BC), Canada, is used in this study. The retrospective data allows the study to test the effects of individuals’ experiences and changes over the life course on AV adoption decisions. The chapter is outlined as follows: The next section provides an overview of the conceptual framework. Section 3.3 discusses the study area and the data resources used in the study. Theoretical Context Life History-oriented Approach A life history-oriented approach accommodates interactions among decisions and changes such as residence, work, education, family life, neighbourhood, and travel choices along the whole life-course or a certain segment of the lifetime (Chatterjee & Scheiner, 2015; Fatmi et al., 2017; J. Zhang, 2018; J. Zhang & Van Acker, 2017). The application of a life history-oriented approach of modelling is based on the hypothesis that individuals’ travel behaviour is largely dependent on their past travel behaviour and its continuity and the life events over the life course (J. Zhang, 2018). Several studies have investigated the longitudinal effects of individuals’ 20 decisions over the life course on their travel behaviour; particularly, longitudinal modelling of major longer-term decisions such as vehicle ownership has received considerable attention from the research community. For example, Oakil et al. (2014) examined how households' vehicle transaction decisions of vehicle acquisition and disposal are influenced by changes over the lifetime, such as the birth of a child and residential relocation. Macfarlane et al. (2015) examined the influence of historical exposure to the built-environment on vehicle ownership and found that individuals’ historical exposure to high-density areas is found to be negatively associated with multiple vehicle ownership. Besides, Oakil et al. (2011) argued that longer-term mobility decisions such as vehicle ownership and residential mobility demand a longitudinal modelling approach to accommodate the dynamics and interdependences of a household’s lifecycle and mobility decisions over time. Similarly, in the case of AV adoption, individuals' perception towards AV might be influenced by their previous exposure to technology, such as the availability of technologies (e.g. lane departure warning) in their existing or previous vehicle fleet. In addition, such longer-term decisions might also be affected by the evolution of household demographics, land use and accessibility measures of the residence over the lifetime. Exposure to Technology Vehicle technology is rapidly evolving. Adopting and usage of higher levels of automation and AVs will be a paradigm shift towards adopting major technology. According to the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the intention to use a specific technology depends on the perceived usefulness, attitude to use, and perceived ease of use (Davis et al., 1989; Madigan et al., 2016; Marangunić & Granić, 2015; Venkatesh et al., 2003). Saaksjarvi (2003) revealed that individuals usually adopt a new form of technology when they have past exposure to similar types of technology and 21 that technology is consistent with their needs and values. Godoe & Johansen (2012) suggested that technology readiness (TR), which corresponds to individuals’ readiness to adopt a new form of technology that will ease their life, determines their level of interest in embracing it. Therefore, it is critical to accommodate historical exposure to technology, such as technology available in the vehicles and technology available in the household, in the modelling framework. Conceptual Framework of the Study The conceptual framework of the research is presented in Figure 3.1. The study exclusively investigates the AV adoption decisions in the following three dimensions: (1) individuals’ preferences for different levels of automation, (2) ) individuals’ preferences towards AVO and SAV, and (3) individuals’ vehicle transaction decisions in an AV future. Figure 3.1 The Conceptual Framework of the Study. 22 The modelling framework includes the historical experiences and changes in different life domains, such as the mobility domain, residence domain, and neighbourhood domain. The mobility domain consists of vehicle ownership history, advanced technology (e.g. lane assist, parking assist, and autonomous emergence stop) available in both the past and present vehicle fleet. The residence domain incorporates the technology available in the household, such as smartphones, computers, and google home and the historical demographics, such as dwelling type, ownership type of the house, and yearly household income. Finally, the neighbourhood domain accommodates the land use mix diversity, distance to the urban core from the residence, and distance to the nearest points of interest such as school, food store etc., from the residence. The modelling framework further tested the effect of socio-demographic characteristics, latent attitudinal factors, and travel attributes on the AV adoption decisions. Data Sources The research is conducted in the Okanagan region of BC, Canada that includes five cities – Kelowna, West Kelowna, Vernon, Peachland, and Lake Country. A retrospective survey data is utilized to investigate the historical deposition effects of individuals’ lifetime on individuals’ decisions regarding AV adoption. In addition, data from the Canada census statistics and open data resources of Okanagan is utilized in this study. The data sources are briefly discussed below: Primary Data Source The study primarily draws data from the retrospective Travel Technology and Mobility Survey (TTMS) conducted in the Okanagan Region of BC, 2019. The survey collected historical information from the respondents regarding their residential history, employment record, 23 technology ownership history, vehicle ownership record, the occurrence of life-cycle events, travel technology preference, attitudes and socio-demographics. The residential history component collected information from respondents regarding the three most recent residential location information and associated attributes such as dwelling type, tenure type, household size, vehicle ownership and primary mode of commute. The technology component collected historical ownership of technology at different residences, such as ownership of the number of smartphones, laptops, desktops and google home. The vehicle ownership component collected information regarding households’ current and previous vehicle fleets. One of the questions in this component asked the respondents about the availability of technological features such as lane assist, parking assistance, autonomous emergency stop and blind-spot detection in their vehicles. The socio-demographic characteristics include age, gender, income, levels of education, employment status, transit pass and driver’s licence ownership and subscription to shared mobility. The socio-demographics, residence and vehicle ownership history were collected for the most recent three residences. In addition, TTMS accommodates 26 attitudinal statements in which the respondents were asked to express their preference towards different travel modes, land use and environment. The responses were recorded in a five-point Likert scale of strongly agree to strongly disagree. Finally, TTMS includes a stated preference component that asks the respondent about their preferences for AVs, such as preference for different levels of automation, AVO, SAV, and vehicle transaction decisions in an AV future. The details of the survey questions are outlined in Appendix A After cleaning for the missing information and applying weights, the final sample includes 1654 responses. The final data was compared with the 2016 Canadian Census statistics for the Okanagan region in order to evaluate the representativeness of the sample. The validation 24 results suggest that most of the sample attributes reasonably matches the census statistics. For instance, in the case of household income group represented by yearly income $30,000 to $49,999 and $50,000 to $79,999, TTMS under-represents the population by 0.04% and 0.66% respectively. A similar characteristic can be observed in the case of the dwelling type where TTMS under-represents semi-detached dwellers by 0.56% and over-represents households residing in apartment that has fewer than five storeys by 0.94% compared to census 2016. However, TTMS substantially under-represents owned dwellers by 15.25% whereas, over-represents the rented dwellers by the same margin. Thus, the sample might be considered as the representative sample for this study. The comparison of different attributes between TTMS and census statistics is outlined in Appendix B Secondary Data Sources The study used a number of secondary data sources in addition to the survey data. The neighbourhood characteristics, such as dwelling density, population density, and percentage of different dwelling types in a census dissemination area, were collected from 2016 Canada census statistics. Besides, the location of different accessibility points, such as schools, healthcare centers, shopping centers, and restaurants, were collected from the Enhanced Point of Interest (EPOI) data. Finally, the land use information such as the area of residential, industrial, and other land use was collected from the open data resources of the Okanagan region. Summary The conceptual framework of the thesis is based on the life history-oriented approach modelling. Overall, the framework includes the relationship between the longer-term decisions regarding the adoption of AVs and the different life domains. Among different life domains, the thesis 25 extensively tested the effect of different components of the mobility, residence, and neighbourhood domains on individuals’ preferences for AV. The primary data comes from the retrospective TTMS survey conducted in BC. The data includes the stated preferences regarding AVs, socio-demographics, vehicle ownership history, information regarding technology in the household and vehicle fleet, travel characteristics, and attitudinal statements. Several secondary resources are also utilized in the thesis. The information regarding accessibility measures is drawn from EPOI, and the land use information is collected from the open data sources of the study region. 26 Chapter 4: Individuals’ Preferences towards Different Levels of Vehicle Automation Introduction The objective of the study is to examine individuals’ preferences for different levels of vehicular automation. The study primarily focuses on how historical experiences such as historical exposure to technology affect their preference for vehicle autonomy. In addition, the study tested the effects of individuals’ attitudes, travel characteristics, accessibility, socio-demographic attributes. The primary data source for this study is the retrospective Travel Technology and Mobility Survey (TTMS) conducted in the Okanagan region of British Columbia, Canada, in 2018. The TTMS contains a stated preference component that asked respondents to rank their preferences for different levels of vehicle autonomy. The study develops a random parameter rank-ordered logit (RPROL) model. The motivation for adopting a rank-ordered modelling technique is to provide better behavioural representation by accommodating the relative preferences for different levels of vehicular automation. Furthermore, the rank-ordered model is extended to a random parameter model to capture unobserved heterogeneity among the sample individuals. Data The TTMS includes a stated preference component that asks the respondents about their preferences towards autonomous vehicles and shared mobility. One of the questions of this component asked the respondents to rank among the following six levels of vehicle automation: 27 (i) Level 0- No Automation, (ii) Level 1- Driver Assistance, (iii) Level 2- Partial Automation, (iv) Level 3- Conditional Automation, (v) Level 4- High Automation and (vi) Level 5- Full Automation. To familiarize the respondents with different levels of vehicle autonomy, they were provided with a brief description of six levels of automation according to “SAE J3016™: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems” by SAE (5). “No Autonomy” is the level where all the driving tasks are performed by the driver. “Driver Assistance” is the level where some driving assistive features are available in the vehicle, but the driver has to control the vehicle. Partial automation refers to the advanced features where the vehicle possesses combined automated functions (e.g. acceleration), but the driver must monitor the environment and perform the driving tasks. The automation level where the driver doesn’t always need to monitor the environment but should always be ready to take control of the vehicle upon notice is defined as conditional automation. When the vehicle is capable of performing all driving tasks under certain conditions, but the driver may have the option to control the vehicle is referred to as high automation. Full automation is specified as vehicular automation, where the vehicle is capable of performing all driving tasks in all conditions with an option to be controlled by the driver. Each respondent ranked different levels of vehicle automation on a scale of 1 to 6, where 1 refers to their most preferred automation, and 6 prefers the least one. Following cleaning, processing, adding weights, and validating, the final data set includes 1241 respondents. Methodology: Random Parameter Rank-ordered Logit Model The study develops a random parameter rank-ordered logit (RPROL) model to understand individuals’ preferences towards different levels of vehicle autonomy. The rank-ordered model accommodates the relative preferences towards different levels of vehicle autonomy, which is 28 inherent in the collected data. The traditional travel mode choice models are developed to predict the choice of a single-mode from the alternatives. However, individuals might rank the alternatives in an order such as first, second, third and so on; rather than choosing one from the list and rejecting the others. To offer a better behavioural understanding, such relative preference scenario needs to be accommodated within the data and corresponding models. With this motivation, the rank-ordered logit model is developed. The model is further extended to a random parameter model to capture the unobserved heterogeneity among the sample individuals. The RPROL model relaxes the independence from irrelevant alternatives (IIA) assumption and therefore, allows the variation of the parameters among the sample individuals (Layton, 2000). Assuming that the utility 𝑈𝑖𝑗 is derived from any individual i for different vehicular automation j (where j = 1, 2, ……., J) where all of the automation levels were available to the respondents for providing their ranked choices. The model is capable of utilizing both the full and partial ranking of the alternatives provided by the respondents. Among 1241 respondents, 1075 respondents (86.62%) provided a complete ranking of their preferences (i.e. ranked all six preferences from 1 to 6) whereas the rest 13.38% provided the partial ranking. In the case of the partially completed response of an individual, incomplete alternatives were considered as the least ranked choices for that individual for ease of analysis. The utility function can be represented as: 𝑈𝑖𝑗 = 𝛽′𝑖𝑋𝑖𝑗 + 𝜀𝑖𝑗 where, 𝑋𝑖𝑗 is the vector of explanatory factors for levels of automation j, 𝛽′𝑖 is the corresponding column vector of parameters that may vary across the sample individuals for existing unobserved heterogeneity and 𝜀𝑖𝑗 is the random error term. It is assumed that 𝜀𝑖𝑗 is independently and 29 identically distributed across the individuals with a mean of zero and variance σ2 for each alternative following Type-I Extreme Value (EV) distribution (Nair et al., 2019). Let, 𝑟𝑖𝑘 represents the kth-ranked automation level for a specific set of ranking provided by individual i (where 𝑟𝑖1 represents the first-ranked alternative, 𝑟𝑖2 represents the second-ranked alternative and so on). Therefore, for a set of rank-ordered choices the following inequalities are valid: 𝑈𝑟𝑖2 − 𝑈𝑟𝑖1 < 𝑈𝑟𝑖3 − 𝑈𝑟𝑖2 < ⋯⋯ < 𝑈𝑟𝑖𝐽 − 𝑈𝑟𝑖𝐽−1 < 0. Furthermore, the rank-ordered choices correspond to J-1 independent and most preferred choice scenario, as the individual i has to choose the second alternative from J-1 alternatives available and so on (Srinivasan et al., 2006). Eventually, the final rank i.e. (J-1)th rank is chosen from the last two alternatives. The probability of assigning rank k to an alternative j by individual i can be represented as: 𝑃𝑖𝑗𝑘 =exp⁡(𝛽𝑖′𝑋𝑟𝑖𝑞)∑ exp⁡(𝛽𝑖′𝑋𝑟𝑖𝑘𝐽𝑘=1 ) The conditional probability of individual i assigning a ranked sequence of alternatives as 𝑟𝑖1, 𝑟𝑖2, …, 𝑟𝑖𝐽−1 is given by the following equation (Layton, 2000): 𝑃′𝑖𝑗𝑘 = ∏exp⁡(𝛽𝑖′𝑋𝑟𝑖𝑞)∑ exp⁡(𝛽𝑖′𝑋𝑟𝑖𝑘)𝐽𝑘=1⁡𝐽−1𝑞=1 Furthermore, the unconditional probability of the alternatives being ranked as 𝑟𝑖1, 𝑟𝑖2, …, 𝑟𝑖𝐽−1 by individual i is calculated as: 𝑃(𝛽𝑖) = ∫∏exp⁡(𝛽𝑖′𝑋𝑟𝑖𝑞)∑ exp⁡(𝛽𝑖′𝑋𝑟𝑖𝑘)𝐽𝑘=1⁡𝐽−1𝑞=1 ⁡𝑓(𝛽𝑖|𝜇 , 𝜎)𝑑𝛽 where, 𝑓(𝛽𝑖|𝜇, 𝜎) is the probability density function of 𝛽𝑖 which is assumed to be normally distributed with mean μ and standard deviation σ (Hensher & Greene, 2003). The following 30 simulated log-likelihood function is derived to determine the parameter values based on the above equation: LL = ⁡∑ ln1𝑅∑ 𝑃(𝛽𝑖𝑚)𝑅𝑚=1𝑁𝑖 (5) where, 𝛽𝑖𝑚is the mth draw from 𝑓(𝛽𝑖|𝜇, 𝜎), R is the total number of draws and N is the number of observations. The log-likelihood function is maximized for determining the parameter values. The function is a multivariate integral and is not of closed form. Therefore, the estimation of the parameters involves a simulation technique. The log-likelihood function is maximized by adopting a Quasi-Monte Carlo (QMC) simulation using 1000 Halton draws. The QMC method outperforms the other simulation techniques with better accuracy at a fewer number of draws and computational duration (Srinivasan et al., 2006). The goodness-of-fit measures of the model are estimated in terms of the adjusted pseudo rho-squared, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) measures. Independent Variables Considered The study tested the effects of a wide variety of variables, including the variables representing historical experiences and changes such as historical exposure to technology, the evolution of household demographics, attitudinal constructs, travel characteristics, accessibility attributes and socio-demographics. Historical exposure to technology includes technology usage in different domains of life – e.g. technology usage in daily life and technology available in the vehicle fleet. Variables representing exposure to technology in daily life include ownership of the number of smartphones, desktops, laptops, and availability of google home over the life course. Exposure to vehicle technology includes the availability of lane assist, parking assist and autonomous stop, among others in the current and previous household vehicle fleet. Variables representing the 31 evolution of household demographics include historical tenure type, historical change in household income, and historical location of residences. Among travel characteristics, variables representing mobility tool ownership are tested. Mobility tool ownership can be defined as the tools that give access to different travel modes, such as ownership of driver’s licence gives access to cars (Fatmi et al., 2014). This study tests the ownership of travel tools as the single ownership, such as driver’s licence, transit pass, and vehicle ownership, as well as bundle ownership such as ownership of driver’s licence and transit pass. Primary commute mode choice was also tested in this study. In addition, the study examined the impact of a number of accessibility variables such as distance to work or school and distance to the regional core from home. Socio-demographic variables include individuals’ age, gender, income, employment status and level of education, among others. Attitudinal factors are derived from the 26 attitudinal statements available in TTMS. Among the 26 statements, 11 are directly representative of attitude towards different travel modes, and 8 are related to attitude towards land use and environment. The responses were collected in a five-point Likert scale of strongly agree to strongly disagree. Factor analysis of the statements related to travel modes results in three factors: pro-AV, pro-car and pro-transit. Factor analysis of the statements related to land use and environment results in three attitudinal factors: pro-urban, pro-suburban and pro-environment. Factors are extracted using Principal Component Analysis (PCA) with oblique rotation. PCA is a dimensionality reduction technique used to derive latent variables termed as principal components from a set of variables based on the inter-correlation among them. Principal components have a linear relationship with the observed variables (Abdi & Williams, 2010) and the correlation between a principal component and a variable is termed as factor loading. The value of the factor loadings ranges from 0 to 1 (both positive and negative) 32 where a higher value represents the higher correlation. The variability of the variables explained by the principal components can be found by taking the square of the factor loadings (Abdi & Williams, 2010). The pattern matrices of PCA are represented in Table 4.1 and Table 4.2. For ease of interpretation, factor loading less than 0.35 is suppressed. The pro-AV persons are those who are willing to use AVs as shared and privately-owned vehicles. They also prefer AVs as they think that AVs will ensure effective time use while Table 4.1 Factor Analysis Result Showing Attitude towards Travel Modes. Statements Pro-AV Pro-transit Pro-car I am proud to own a car because it gives me the flexibility and freedom to go around 0.789 Getting to work without a car is a hassle 0.789 I think ride-sharing or ride-hailing services, such as Uber and Lift, are great for the community. 0.746 Ridesharing is a great alternative to owning a car as it reduces the hassles to find parking 0.739 I find commute time using transit as a good use of my time -0.659 I feel embarrassed taking transit -0.884 Transit is an essential service 0.848 I prefer to pay more taxes to give more subsidies and improve transit 0.388 -0.399 I will prefer purchasing an autonomous vehicle when it will be available in the market 0.542 -0.682 I think car-sharing or ride-hailing services using the autonomous vehicle is a great idea 0.842 I think riding in an autonomous vehicle will give me more free time to perform other activities 0.688 -0.392 travelling. Pro-transit attitude refers to those who consider transit as an essential service and therefore are willing to use transit and even support subsidies for the improvement of the existing transit services. Individuals with a pro-car attitude are willing to own a car as they prefer the flexibility and privacy provided by the car mode. They also consider the car as a hassle-free 33 travel mode. In the case of attitude towards land use and environment, pro-suburban individuals are those who prefer to live in single-detached dwellings away from the inner city. On the other hand, pro-urban attitude relates to the individuals who prefer to live within the urban core closer to the frequent bus routes. They also prefer to use active transportation for commuting. Furthermore, the pro-environment attitude represents individuals who are concerned about the environment, such as greenhouse gas (GHG) emissions. They are also willing to provide subsidies for improving the infrastructure to reduce GHG emissions. Table 4.2 Factor Analysis Result Showing Attitude towards Land Use and Environment. Statements Pro-environment Pro-urban Pro-suburban I prefer to live in a house with a backyard 0.776 I prefer to live in the inner city -0.734 I prefer to live within walking distance to frequent bus route 0.879 Living in a multi-family unit would not give me enough privacy -0.446 0.715 I prefer cycling and walking than driving 0.36 Households should be fined if their greenhouse gas emissions exceed a set daily limit 0.821 I am willing that my tax payees’ money be invested in the Infrastructure to support electric/other clean-fuel vehicle technologies 0.775 The price of gasoline should be raised to improve air quality 0.525 -0.389 -0.482 Result and Discussion of the RPROL Model Goodness-of-fit Measures The summary statistics of the variables retained in the final model are represented in Table 4.3. The parameter estimation results and the goodness-of-fit measures of the RPROL model are shown in Table 4.4. The model only includes variables that are significant at least at the 95% confidence interval and provide behaviourally intuitive results. In addition to the RPROL, a conventional ranked-ordered logit (ROL) model is developed for comparison purposes. The 34 RPROL model shows better goodness-of-fit, with an adjusted pseudo-rho squared value of 0.478 compared to that of the ROL model of 0.426. The AIC and BIC values of the RPROL model are 6991 and 7344.562, respectively, which is lower than the AIC (7661.1) and BIC (7983.881) values of the ROL model. Therefore, the RPROL model is considered as the final model for further discussion of the model results. Overall, the model results reveal that the historical exposure to technology in daily life and availability of technology in the vehicle fleet, the evolution of household demographics, attitudinal constructs, travel behaviour, accessibility measures and socio-demographic attributes significantly affect individuals’ preference towards different levels of vehicle autonomy. Parameter Estimation Results One of the significant contributions of the study is exploring the effect of life history-oriented attributes on individuals’ interest in different levels of automation. Historical exposure to technology plays an important role in adopting newer forms of technology which is evident from the model results. Individuals who own vehicles with advanced technological features (e.g. lane assist, parking assistance, autonomous emergency stop, blind spot detection) over the life-course have a higher likelihood of choosing high and full vehicle automation. However, they are found to have lower interest in choosing lower and no automation. One of the interesting findings of the study is that those who only have advanced technological features in their present vehicle are less likely to choose higher levels of vehicle autonomy. Technological devices such as the number of smartphones owned per person and google home ownership in the household are positively associated with the preference towards higher levels of automation. It is evident from these results that those who are technology-savvy in different domains of life are more likely to adopt the higher automation. 35 The model results suggest that individuals historically living in an owned dwelling have a higher likelihood of choosing high and full automation while they are less interested in lower levels of automation such as driver assistance. Individuals who have experienced an increase in their income over the years are more likely to choose conditional and full automation, while they are less interested in no automation. Those who lived closer to their work locations over the years are found to be interested in no vehicle automation and driver assistance. Individuals with pro-AV attitudes are found to have an inclination towards partial and conditional automation. Pro-car attitudes lead to higher interest in lower and no vehicle Table 4.3 Summary Statistics of the Variables Retained in the Final Model. Variables Description Mean or Proportion Standard Deviation Life history-oriented variables Historical exposure to technology Historically owned vehicle with advanced technology Historically owned vehicle with advanced technologies such as lane assist, parking assist, autonomous emergency stop and blind-spot detection 25.46% - Advance technology in the currently owned vehicle only Currently own vehicle with advanced technologies such as lane assist, parking assist, autonomous emergency stop and blind-spot detection 15.87% - Smartphone ownership per person Number of smartphone ownership per person in the current residence 1.262 0.401 Google home ownership Individual with google home in the current residence 14.80% - Evolution of household demographics Lived in an owned dwelling Individual historically lived in an owned dwelling 60.11% - Income increased over the years Individual's income has increased over the years 87.27% - Lived closer to work location Individual historically moved closer to work location 11.52% - Attitudinal factors Pro-AV Pro-AV attitude 8.195 2.635 Pro-car Pro-car attitude 1.64 2.063 Pro-urban Pro-urban attitude 1.39 1.461 36 Variables Description Mean or Proportion Standard Deviation Pro-suburban Pro-suburban attitude 1.382 2.345 Pro-environment Pro-environment attitude 4.392 1.829 Travel Characteristics No driver's licence Individual doesn't have driver's licence 4.03% - Multiple travel tool Individual owns both driver's licence and transit pass 52.62% - Vehicle ownership per person Number of vehicles per person in the household 1.09 0.53 Pri. mode: Auto-drive Primary commute mode: auto-drive 53.00% - Accessibility measures Commute distance >= 10 km Distance to work location from household more than 10 km 41.58% - urban core distance >= 10 km Distance to the regional core from household more than 10 km 77.94% - Socio-demographics Income < $50,000 Individual's yearly income below $50,000 47.87% - Income > $100,000 Individual's yearly income above $100,000 11.36% - Age 18-34 Individual's age between 18 and 34 41.58% - Gender: Male Gender of the individual is male 41.42% - automation with lower interest in full automation. These results indicate that some individuals are still interested in driving their vehicles themselves instead of letting their vehicles drive for them. However, heterogeneity can be observed in pro-car individuals in the case of choosing full automation, which is evident from the statistically significant and higher value of the standard deviation. Individuals with the pro-urban attitude are less likely to choose higher automation levels. On the other hand, they show interest in no automation, although this variable demonstrates unobserved heterogeneity with a statistically significant standard deviation. Pro- suburban individuals have a higher likelihood of choosing full automation. This variable also demonstrates heterogeneity with a higher value of standard deviation than the mean value which indicates that pro-suburban people may also show less interest in full automation. 37 The model results suggest that an individual’s travel characteristics can affect their preference towards different levels of vehicle autonomy. Individuals without a driver’s licence are found to have a higher likelihood of preferring full automation, whereas they are unlikely to choose no automation. This result indicates that inability to drive leads an individual to choose the fully autonomous vehicle. Variables representing multiple travel tools (transit pass and driver’s licence) ownership are negatively associated with driver assistance and partial automation. Besides, individuals with multiple travel tools show less interest in no automation, although heterogeneity can be observed, which is demonstrated by a statistically significant and higher value of the standard deviation of the random parameter. Higher vehicle ownership per person in the household positively impacts individuals’ preference towards full automation, whereas Table 4.4 Parameter Estimation Results of the RPROL model. Variables No Automation Level 1 Level 2 Level 3 Level 4 Full Automation Constant 47.432*** 38.898*** 38.460*** 36.965*** 33.518*** - Life history-oriented variables Historical exposure to technology Historically owned vehicle with advance technology -.764*** -.263*** 1.040*** 5.817*** Advance technology in the currently owned vehicle only -1.224*** -3.950*** -8.939*** Smartphone ownership per person -.052** .597*** .765*** 2.635*** Google home ownership 1.559*** 3.399*** 6.954*** Evolution of household demographics Lived in an owned dwelling -0.089*** .965*** .625*** Income increased over the years -3.657*** .060*** 11.379*** 38 Variables No Automation Level 1 Level 2 Level 3 Level 4 Full Automation Lived closer to work location 2.900*** 1.604*** Attitudinal factors Pro-AV .098*** .034*** Pro-car .388*** .086*** -.628*** Pro-urban .876*** -.128*** -.289*** -.416*** Pro-suburban .170*** Pro-environment .429*** .158*** Travel Characteristics No driver's licence -6.576*** 12.149*** Multiple travel tool -1.039*** -.220*** -.718*** Vehicle ownership per person -2.920*** -1.075*** -.666*** 2.883*** Pri. mode: Auto-drive 4.057*** Accessibility measures Commute distance >= 10 km .435*** Urban core distance >= 10 km -1.795*** 2.157*** 9.673*** Socio-demographics Income < $50,000 -.163*** .801*** Income>= $100,000 1.522*** 4.055*** Age 18-34 -2.157*** -.217*** .724*** 4.653*** Gender: Male .074*** 6.672*** Standard deviation of the random parameters Pro-car 1.402*** Pro-urban 1.865*** Pro-suburban 1.345*** Multiple travel tool 1.546*** Income < $50,000 .166*** Income>= $100,000 4.210*** Goodness-of-fit measures Log likelihood -3426.514 Pseudo rho squared 0.478 AIC 6991 BIC 7344.562 *** 99% confidence interval; ** 95% confidence interval 39 negatively impacts their interest in lower levels of automation and no automation. Individuals whose primary commute mode is auto-drive are more likely to prefer full automation. The model results suggest that accessibility measures such as commute distance to work or school and distance of regional core from the household also impact the decision of choosing different levels of vehicle autonomy. Individuals whose commute distance is 10 kilometres or more are likely to prefer partial automation. Individuals living in a household located 10 kilometres or further from the regional core have a higher likelihood of choosing high levels of vehicle autonomy and full automation, whereas they are less likely to prefer no automation. These results further reveal that individuals living in suburban areas are more inclined towards higher levels of automation. Among the socio-demographic attributes, income, age and gender are found to have a significant effect on the choice of the vehicular automation level. The low-income group, represented by an individual with a yearly income less than $50,000, shows higher interest for lower levels of vehicle autonomy. On the other hand, they are less interested in no automation. However, this low-income group demonstrates heterogeneity with a significant standard deviation. This result indicates that some individuals are still interested in completely manual driving. Individuals with a higher income indicated by a yearly income of $100,000 or above show a higher likelihood of choosing higher levels of vehicle automation and full automation. The parameter estimation results suggest that the standard deviation value of this parameter is higher than the mean value for level 4 automation. Therefore, unobserved heterogeneity exists among the sample individuals, which indicates that they may prefer lower automation as well. Younger individuals aged between 18 and 34 are more likely to prefer higher levels to full automation, but they are less likely to prefer lower levels of automation and no vehicle automation. This result is consistent with the findings of 40 Abraham et al. (2014). Male individuals are also found to be interested in higher levels to full automation. Summary One of the key features of this study is to examine how previous experiences along the life-course, such as historical exposure to technology in daily life and historical availability of technology in the household vehicle fleet, influences the preference towards different vehicle technologies in terms of levels of autonomy. A random parameter rank-ordered logit (RPROL) model is developed using data from a retrospective survey conducted in the Okanagan region of Canada. The survey collected individuals’ responses to different vehicle autonomy levels on a ranked scale of 1 to 6, where 1 indicates the most preferred option and 6 indicates the least preferred option. To accommodate these relative preferences among different vehicle automation levels, the rank-ordered logit modelling technique is adopted. The model is extended to a random parameter model to capture unobserved heterogeneity among the sample individuals. Besides, the study also analyzed the effect of attitudinal factors, travel characteristics, accessibility, and socio-demographic attributes. The model results suggest that historical experiences significantly influence individuals’ preferences towards different levels of vehicle autonomy. The results confirm the effects of historical exposure to technology, including technology used in daily life and technology available in the household vehicle fleet. Individuals historically exposed to advance technological features in their vehicle over their lifetime have a higher likelihood of adopting a higher level of vehicle automation. They also show a lower interest in lower levels of automation. On the other hand, those who have advance technological features in the current vehicle only, have a lower likelihood of adopting higher vehicle autonomy. Technological device 41 ownership, such as smartphones and google home, have positive effects on higher levels of vehicular automation. Individuals historically living in an owned household and who have experienced increased income over their lifetime are interested in higher vehicle automation. However, people living in urban areas near their work location are not interested in higher levels of vehicular automation. Furthermore, variables representing younger adults and individuals without driver’s licence show a higher interest in full automation, whereas they are not interested in no automation. The model confirms the existence of significant heterogeneity among the individuals. For example, pro-urban individuals prefer no automation whereas, pro-suburban individuals are more inclined towards full automation. Interestingly, both pro-urban and pro-suburban variables show significant heterogeneity with a larger standard deviation value. The higher income group (i.e. income > $100,000) is found to be highly interested in higher and full vehicle automation. However, this variable confirms significant heterogeneity with a large standard deviation. Similar heterogeneity is confirmed for individuals with lower income, pro-car attitude, and multiple travel tools. Overall, this study gives important behavioural insights; particularly, the effects of historical experiences and attitudinal factors on the preference for different levels of vehicle autonomy. The findings will help cities and vehicle manufacturers for targeted marketing of different levels of vehicle automation as well as fully autonomous vehicles. 42 Chapter 5: Individuals’ Preferences for Ownership and Sharing of AV Introduction This study adopts a life history-oriented approach to investigate the adoption of AVs as privately-owned vehicles and vehicles in shared services. A life history-oriented approach focuses on how decisions and changes such as travel choice, residential location choice, the birth of a child and education, among others, interact with each other over the lifetime of the individuals. One of the key features of this research is to follow a life history-oriented approach to examine how individuals’ exposure to technology, vehicle ownership history, and historical accessibility and built-environment attributes of the previous residences over their life course affect the AV adoption decisions. Furthermore, this research tests the influence of attitudinal constructs, travel characteristics and socio-demographics on AV adoption. This study utilizes data from the retrospective TTMS survey conducted in the Okanagan region of British Columbia in 2018. The survey contains stated preference components that collected information regarding individual's preference towards AVs. A joint bivariate ordered probit (BOP) modelling technique is adopted to capture the unobserved error correlation between the adoption decisions accounting for the ordinal scale of the preference variables. Data TTMS included a stated preference component that collected information regarding individuals’ preferences towards AVs. One of the questions asked the respondents to provide their preference 43 towards AVs as a privately-owned vehicle. Another question asked the respondents about their preference for AVs as a vehicle in shared services. In both cases, participants provided their responses in a five-point Likert scale of very interested to very uninterested. The distribution for AVO is as follows: not at all interested - 24.96%, somewhat uninterested - 14.68%, neutral - 19.00%, somewhat interested - 24.27%, and very interested - 17.10%. In the case of the preference for SAV, the distribution is: very uninterested - 24.05%, somewhat uninterested - 12.26%, neutral - 16.49%, somewhat interested - 24.61%, and very interested - 19.6%. The cross-tabulated summary for the preference for AVO and SAV is represented in Table 1. Table 5.1 Cross-tabulated summary statistics of the preference towards AVO and SAV. AVO\SAV Not at all interested Somewhat uninterested Neutral Somewhat interested Very interested Total (%) Not at all interested 200 46 51 15 1 313 (27.03%) Somewhat uninterested 18 64 12 45 3 142 (12.26%) Neutral 20 30 74 27 40 191 (16.49%) Somewhat interested 43 8 67 119 48 285 (24.61%) Very interested 8 22 16 75 106 227 (19.60%) Total (%) 289 (24.96%) 170 (14.68%) 220 (19.00%) 281 (24.27%) 198 (17.10%) 1158 (100%) Figure 5.1 shows the distribution of the preferences for AVO and SAV for different home ownership types. Among the individuals who lived in owned houses over the life course, 21.43% are very interested in AVO, whereas 15.57 % of them are found to be very interested in SAV. In the case of individuals living in a rented house over the life course, the proportion of very interested in AVO (13.13%) is relatively less than the proportion of very interested in SAV (20.95%). Figure 5.2 shows the distribution of the preference for AVs with respect to google 44 Figure 5.1 Preferences for AVs for different Home Ownership Types Over the Life Course. Figure 5.2 Preferences for AVs for Google Home Ownership in the Household. home ownership. 33.80% of the respondents who own google home are found to be very interested in AVO, whereas 19.44% are very interested in SAV. Finally, Figure 5.3 shows the 0%5%10%15%20%25%30%35%AVO SAV AVO SAVOwned RentedPercentageHistorical home ownership typeHistorical home ownership type vs AV preferencesNot at all interested Somewhat uinterested Neutral Somewhat interested Very interested0%5%10%15%20%25%30%35%40%AVO SAV AVO SAVOwn google home Does not own google homePercentageGoogle home ownership Google home ownership vs AV preferencesNot at all interested Somewhat uinterested Neutral Somewhat interested Very interested45 distribution of smartphone ownership per person versus the preferences for AVO and SAV. Interestingly, with the increase in smartphone ownership per person in the household, a higher interest for both AVO and SAV is observed. Figure 5.3 Preferences for AVs for Smartphone Ownership per Person in the Household. Methodology: Joint Bivariate Ordered Probit Model In this study, the joint bivariate ordered probit (BOP) modelling technique is utilized to investigate individuals’ preferences for AVO and SAV services. The motivation behind jointly modelling these two dependent variables is to explore the presence of potential error correlation, which might affect the preference propensity for AVO and SAV services (Chiou et al., 2013). The correlation might exist because of the potential exchangeability between the alternatives or other unobserved attributes that might be associated with the preference towards AVO and SAV. Overlooking the potential correlation might lead to inaccurate estimation of the residuals and standard error of the coefficients, which eventually may affect the statistical significance of the 0%5%10%15%20%25%30%35%40%45%50%AVO SAV AVO SAVLess than 1 1 or morePercentageSmartphone ownership per personSmartphone ownership per person vs AV preferencesNot at all interested Somewhat uinterested Neutral Somewhat interested Very interested46 coefficients to be estimated. BOP is an extended version of the univariate ordered probit (OP) model, and the latent and continuous propensity of an individual 𝑖 for AVs can be represented as follows: 𝑦𝑖∗ = 𝛽𝑥𝑖 + 𝜀𝑖 where, 𝑥𝑖 is the vector of explanatory variables, 𝛽 is the vector of the coefficients associated with explanatory variables and 𝜀𝑖 is a random error term. However, 𝑦𝑖∗ is not directly observable from the responses. The observable counterpart 𝑦𝑖 can be found 𝑦𝑖∗ as follows: 𝑦𝑖 ={ 0, 𝑖𝑓⁡𝑦𝑖∗ ≤ 0⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡1, 𝑖𝑓⁡0 < 𝑦𝑖∗ ≤ 𝜇1⁡⁡2, 𝑖𝑓⁡𝜇1 < 𝑦𝑖∗ ≤ 𝜇2⋮⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡𝐽, 𝑖𝑓⁡𝜇𝐽−1 ≤ 𝑦𝑖∗⁡⁡⁡⁡⁡⁡ where, µ is the threshold parameter to be estimated, and 𝐽 is the highest preference level for AVO or SAV. In this modelling method, the observable discrete components are coded as 0 (not at all interested), 1 (somewhat uninterested), 2 (neutral), 3 (somewhat interested) and 4 (very interested). The BOP model considers two dependent variables to represent the propensity for AVO (𝑦𝑖1) and SAV (𝑦𝑖2) which can be represented as follows: 𝑦𝑖,1∗ = 𝛽1𝑥𝑖,1 + 𝜀𝑖,1, ; 𝑦𝑖,1 = 𝑗⁡𝑖𝑓⁡𝜇𝐽−1,1 ≤ 𝑦𝑖,1∗ ≤⁡𝜇𝐽,1; ⁡𝑗 = 0, 1, …… , 𝐽 𝑦𝑖,2∗ = 𝛽2𝑥𝑖,2 + 𝜀𝑖,2, ; 𝑦𝑖,2 = 𝑘⁡𝑖𝑓⁡𝜇𝐽−1,2 ≤ 𝑦𝑖,2∗ ≤⁡𝜇𝐽,2; ⁡𝑘 = 0, 1, …… ,𝐾 This study assumes the error terms 𝜀𝑖,1 and 𝜀𝑖,2 in the above equations to be independent and identically distributed (IID) (Dias et al., 2017). Furthermore, a standard normal distribution of the error terms with a mean 0 and variance 1 is assumed, which leads to the use of a probit modelling framework (Yamamoto & Shankar, 2004). In contrast, the logit modelling framework 47 may also be considered if the error terms are assumed to have a standard logistic distribution (Sun & Elefteriadou, 2014; Wali et al., 2017). The cross-equation correlated error terms are as follows: (𝜀𝑖,1𝜀𝑖,2)~𝑁 [(00) , (1 𝜌𝜌 1)] where, 𝜌 represents the correlation coefficient, the value of which ranges from 0 to 1. A positive value of 𝜌 implies that the unobserved factors may have a similar effect on the preference towards both AVO and SAV, and a negative value represents an opposite relationship. In the case of 𝜌 ≈ 0, there will be a negligible correlation between the two dependent variables, and the bivariate model will eventually be equivalent to two univariate models. Therefore, in this line of research, it is considered as the null hypothesis that the error terms are not correlated. According to Chen et al. (2019), the joint probability of an individual’s preference for AVO (𝑦𝑖,1 = 𝑗) and SAV (𝑦𝑖,2 = 𝑘) in a BOP modelling framework can be determined by the following equation: 𝑃(𝑦𝑖,1 = 𝑗;⁡𝑦𝑖,2 = 𝑘) = 𝑃(⁡𝜇𝐽−1,1 ≤ 𝑦𝑖,1∗ ≤⁡𝜇𝐽,1; ⁡𝜇𝐽−1,2 ≤ 𝑦𝑖,2∗ ≤⁡𝜇𝐽,2)= 𝑃(𝜇𝐽−1,1 ≤ 𝛽1𝑥𝑖,1 + 𝜀𝑖,1 ≤⁡𝜇𝐽,1; ⁡𝜇𝐽−1,2 ≤ 𝛽2𝑥𝑖,2 + 𝜀𝑖,2 ≤⁡𝜇𝐽,2)= [∅2(𝜇𝐽,1 − 𝛽1𝑥𝑖,1, 𝜇𝐽,2 − 𝛽2𝑥𝑖,2; 𝜌) − ∅2(𝜇𝐽−1,1 − 𝛽1𝑥𝑖,1, 𝜇𝐽,2 − 𝛽2𝑥𝑖,2; 𝜌)]− [∅2(𝜇𝐽,1 − 𝛽1𝑥𝑖,1, 𝜇𝐽−1,2 − 𝛽2𝑥𝑖,2; 𝜌) − ∅2(𝜇𝐽−1,1 − 𝛽1𝑥𝑖,1, 𝜇𝐽−1,2 − 𝛽2𝑥𝑖,2; 𝜌)]⁡ where, ∅2(. ) represents the standard bivariate normal cumulative distribution function. To form the log likelihood function, a dummy variable 𝛿(𝑗, 𝑘) is considered, which takes a value of 1 if 48 an individual 𝑖 provides preference level 𝑗 for AVO and 𝑘 for SAV and takes a value of 0 otherwise. The log likelihood function is represented as follows: 𝐿𝐿 =∑∑∑𝛿(𝑗, 𝑘)𝑙𝑜𝑔𝑃(𝑦𝑖,1 = 𝑗;⁡𝑦𝑖,2 = 𝑘)𝐾𝑘=0𝐽𝑗=0𝑁𝑖=1⁡⁡⁡⁡⁡ where, 𝑁 is the number of respondents. The coefficients are estimated by maximizing the log likelihood function and are used to interpret the highest and lowest ordered preference of the dependent variable. The positive sign of the coefficients indicates that an increase in the associated explanatory variable is likely to increase the probability of the highest ordered preference while decreasing the probability of the lowest ordered preference (Greene & Hensher, 2010; Russo et al., 2014). Independent Variables Considered This study tests the effects of several variables, including life history-oriented attributes, technology ownership, travel attributes, attitude, and socio-demographics. Life history-oriented attributes include historical vehicle ownership and household characteristics, historical exposure to technology and historical built-environment measures. These historical attributes are generated using data from the housing history and vehicle ownership record components of the TTMS. The historical vehicle ownership and household characteristics include average vehicle ownership in the household and historical home ownership type. These variables are generated by comparing the vehicle, dwelling and demographic attributes in the most recent three residential locations. In the case of historical exposure to technology, variables are generated by comparing the ownership of the number of smartphones, laptops and google homes among the three most recent residences. Further technology-related variables are generated by comparing 49 information from the vehicle ownership record component. For instance, the variable representing the historical exposure to vehicle technology such as lane assist, parking assist, and autonomous emergency stop is generated by comparing the availability of those features in the current and previous vehicles. The historical built-environment measures include the accessibility and land use attributes for all the reported residential locations. Accessibility characteristics include the distance from home to work, school, urban core, food store and restaurants, among others. Land use attributes such as percentage of residential, commercial, and industrial land use, among others, are generated within a 1km buffer area from the respondent’s residential location. The land use information was utilized to derive the land use mix index (LUI) (Bhat & Gossen, 2004). The LUI value ranges from 0 to 1, where a higher value indicates a higher land use mix. In the case of deriving the attitudinal factors, TTMS contains 16 attitudinal statements representing the attitude of the respondents towards different travel modes, land use and environment. The respondents were asked to express their level of interest on a five-point Likert scale (strongly agree to strongly disagree). Factor analysis was performed using these preference statements to generate the attitudinal factors. Factors are determined using a principal component analysis (PCA) with oblique rotation. Factor loadings less than 0.35 are suppressed for ease of interpretation. PCA using the attitudinal statements yielded 5 attitudinal factors: pro-suburban, pro-environment, pro-car, pro-shared-mobility, and pro-transit. The pattern matrix for the PCA is represented in Table 5.2. Pro-suburban people are more inclined towards having a backyard and privacy. They are likely to live farther away from the inner city; hence, a negative loading for the statement representing interest to live in the inner city is expected. The pro-environment attitude represents people who 50 Table 5.2 Factor Analysis Result Showing Attitude towards Travel Modes, Land Use and Environment. Statements Pro-suburban Pro-environment Pro-car Pro-shared-mobility Pro-transit I prefer to live in a house with a backyard 0.713 I prefer to live in the inner city -0.743 I prefer to live within walking distance to frequent bus route -0.409 0.372 Living in multi-family unit would not give me enough privacy 0.751 Households should be fined if their greenhouse gas emissions exceed a set daily limit 0.757 I am willing that my tax payee’s money be invested to the Infrastructure to support electric/other clean fuel vehicle technologies 0.825 I prefer cycling and walking than driving 0.408 -0.538 Price of gasoline should be raised to improve air quality 0.741 I am proud to own a car because it gives me the flexibility and freedom to go around 0.693 Getting to work without a car is a hassle 0.68 I think ride-sharing or ride-hailing services, such as Uber and Lift, are great for the community 0.853 Ride sharing is a great alternative to owning a car as it reduces the hassles to find parking 0.832 Bike share services increase accessibility to many destinations -0.418 0.62 I find commute time using transit as a good use of my time -0.729 I feel embarrassed taking transit -0.781 Transit is an essential service 0.746 51 are concerned about environmental pollution and are willing to spend money on building facilities to reduce GHG emissions. The pro-car attitude represents individuals who enjoy travelling in cars due to the flexibility and freedom. Hence, higher positive loadings are found for statements representing- proud to own a car due to its flexibility and freedom and getting to work without a car is a hassle. The Pro-shared-mobility attitude refers to the individuals who prefer ridesharing services to avoid the hassle of owning cars. Similarly, the pro-transit attitude relates to the respondents who are more inclined towards the usage and improvement of the transit services. Travel characteristics include the primary commute mode such as walk, bike, auto-drive, auto-passenger, and transit. Among the socio-demographic characteristics, this study examines the influence of age, income, employment status, level of education, household size, number of children in the household, dwelling type, and vehicle ownership, among others. The descriptive statistics of the variables retained in the final model are represented in Table 5.3. Results and Discussion of the JBOP Model Goodness-of-fit Measures Table 5.4 represents the parameter estimation results and the goodness-of-fit measures of the joint AVO and SAV model. A statistically significant (t-statistic = 35.51) and higher value of the polychoric correlation coefficient 𝜌 (0.69) justifies the modelling of AVO and SAV jointly. Besides, a positive value of 𝜌 suggests a positive correlation between the preference towards AVO and SAV. Therefore, the unobserved factors are likely to affect the propensity positively or negatively for AVO and SAV simultaneously. To test the hypothesis of no correlation, a 52 Table 5.3 Summary statistics of the variables retained in the final model. Variable Description Mean/ Percentage Standard Deviation Historical Exposure to Technology Average smartphone Historical average smartphone ownership in the household 3.19 1.16 Advance vehicle technology Historically owns a vehicle with advanced technology (dummy) 25.73% - Historical Household Characteristics and Vehicle Ownership Owned house Historically lived in an owned dwelling (dummy) 59.50% - Average VO Historical average household vehicle ownership 2.73 0.92 Historical Accessibility Measures Average LUI Average land use index within a 1 km buffer area of the recent three residential locations 0.53 0.13 Urban core > 3km Distance from home to the urban core more than 3km for the most recent three residential locations (dummy) 25.47% - School distance < 3 km lived less than 3 km from the nearest educational institution in the most recent three residential locations (dummy) 86.70% - Technology Ownership Smartphone per person Smartphone ownership per person in the current household 0.79 0.41 Google home Google home ownership in the current household (dummy) 15.54% - Attitudinal Factors Pro-shared-mobility Pro-shared-mobility 8.96 2.16 Pro-transit Pro-transit 3.45 1.17 Pro-environment Pro-environment 8.83 3.00 Socio-demographics and Built-environment Income < 50k Individual's income < $50,000 (dummy) 48.36% - Income ≥ 100k Individual's income ≥ $100,000 (dummy) 11.40% - Full-time Full-time worker (dummy) 49.57% - Dwelling density Dwelling density of the census dissemination area (units per square km) 1070.92 1034.39 Travel Characteristics Auto-drive Primary commute mode: auto-drive (dummy) 49.31% - Auto-passenger Primary commute mode: auto-passenger (dummy) 5.01% - Walk Primary commute mode: walk (dummy) 8.64% - 53 likelihood ratio test is performed according to Greene and Hensher (2010). The likelihood ratio (LR) is calculated as follows: 𝐿𝑅 = 2(𝐿𝐿𝐵𝑂𝑃 − 𝐿𝐿𝐴𝑉𝑂 − 𝐿𝐿𝑆𝐴𝑉) where, 𝐿𝐿𝐵𝑂𝑃 is the log likelihood of the BOP model, 𝐿𝐿𝐴𝑉𝑂 and 𝐿𝐿𝑆𝐴𝑉 are log likelihood values of independent univariate OP models corresponding to AVO and SAV. The LR value is found to be 540.9, which is significantly higher than the critical chi-squared value for 1 degree of freedom at even 99% level of confidence. Therefore, it cannot be rejected to model individuals’ preference for AVO and SAV jointly under this empirical context. Overall, the model confirms the historical deposition effects of retrospective changes and experiences such as exposure to technology, vehicle ownership history, historical measures of accessibility of the home location, and evolution of socio-demographic characteristics over the life-course of the individuals. Furthermore, the model also retains the effects of attitudinal factors, travel characteristics, and socio-demographics. Further discussions on the model results are presented below. Parameter Estimation Results of the AVO Component The model results suggest that life history-oriented changes and experiences significantly affect preferences towards AVs as owned vehicles. For example, average smartphone ownership in the current and previous residences shows a positive relationship for AVO. This implies that individuals residing in households with higher smartphone ownership over their life-course are more likely to own AVs. Individuals who are historically exposed to using technologies in their daily life, such as smartphones, are comfortable using technologies and consequently might prefer to adopt newer technologies such as AVs; hence, they are more likely to own an AV. Another variable representing the availability of technologies in the existing vehicle fleet such as 54 lane assist, parking assist and autonomous emergency stop reveals a positive relationship. This finding illustrates that individuals familiar with using advanced technologies in their vehicles have a higher likelihood to prefer owning disruptive vehicle technologies such as AVs. In the case of variables representing historical vehicle ownership and household attributes, individuals living in an owned household over their lifetime are more likely to own an AV. This variable might represent a historically higher income group that can afford to own AVs. Similarly, individuals living in households with an increased income over the life course have a higher likelihood of owning an AV. The model results reveal the retrospective impact of accessibility measures on individuals’ preferences towards AVO. The variable representing retrospective accessibility measures such as historically lived more than 3 km from the urban core shows a positive relationship. This might represent suburban dwellers who have comparatively longer travel time (e.g. longer commute time) and do not have convenient access to multimodal travel options such as bike and transit, have a higher inclination towards owning an AV. Exposure to technology in daily life is a significant factor that might drive an individual towards adopting a new form of technology as AVs. It is observed from the model results that the higher smartphone ownership per person in the household is positively associated with the adoption of AV as a privately-owned vehicle. Owning a google home is also found to positively affect the propensity for AVO. Several studies suggested that technology devices such as smartphone, computer or tablet ownership are significant indicators of individuals’ technological expertise (Astroza et al., 2017; Moore et al., 2020). Therefore, the findings of this study dictate that technology-savvy people are more likely to prefer owning an AV as it may enable them to perform a wide variety of activities while travelling. 55 Among the attitudinal factors, individuals with a pro-shared-mobility attitude show a lower likelihood of owning AV, as expected. On the other hand, a positive relationship is found for the variable representing individuals with pro-environment attitudes, who are very concerned about air pollution and greenhouse gas (GHG) emission from the burning of fossils and prefer the use of alternative clean energy. Since AVs are more likely to be fully electric vehicles, pro-environment individuals have a higher likelihood of owning them. In the case of income groups, higher-income individuals represented by yearly income greater than $100,000 are more likely to own an AV. Among different employment groups, full-time workers have a higher likelihood of owning AV. One of the interesting findings is that a variable representing auto passengers reveals a higher likelihood of owning an AV. This result implies that AVs might offer travel freedom to auto passengers who might be dependent on their family or friends with a driver’s licence to drive with them. Individuals who use active transportation modes such as walk are less likely to own an AV. Parameter Estimation Results of the SAV Component The model confirms significant historical deposition effects on the preference towards adopting AVs as shared services. For example, variables representing historical exposure to technologies in everyday life, such as average smartphone ownership in the household over the life course, show a positive relationship. The finding implies that individuals’ long-term exposure to technology over the life course has significant effects on their interest in sharing AVs. Variables representing retrospective effects of vehicle ownership, such as average vehicle ownership over the life course, show negative relationships. This implies that individuals owning more personal vehicles in the households might feel comfortable and convenient in riding their own vehicles 56 Table 5.4 Parameter estimation results of the BOP model. Variable AVO SAV Coefficient t-statistic Coefficient t-statistic Historical Exposure to Technology Average smartphone 0.0005** 1.99 0.0008** 2.23 Advance vehicle technology 0.07 1.32 - - Historical Household Characteristics and Vehicle Ownership Owned house 0.16*** 2.93 - - Average VO -0.0005** -2.13 -0.0004* -1.68 Historical Accessibility Measures Average LUI - - 0.49*** 2.77 urban core > 3km 0.0001* 1.74 - - School distance < 3 km - - 0.0006*** 9.36 Technology Ownership Smartphone per person 0.6*** 7.92 0.65*** 8.68 Google home 0.55*** 6.1 0.15 1.6 Attitudinal Factors Pro-shared-mobility -0.02 -1.46 0.01 0.94 Pro-transit - - -0.03 -1.36 Pro-environment 0.02 1.38 0.01 1.1 Socio-demographics and Built-environment Income < 50k - - 0.13** 2.1 Income ≥ 100k 0.08 0.91 - - Full-time 0.09 1.45 0.13* 1.76 Dwelling density - - 0.00004 1.59 Travel Characteristics Auto-drive - - 0.10* 1.83 Auto-passenger 0.25** 2.12 - - Walk -0.21** -2.15 - - Threshold Parameters 𝜇1 0.00 - 0.00 - 𝜇2 0.37*** 11.73 0.43*** 13.21 𝜇3 0.80*** 19.58 0.93*** 21.56 𝜇4 1.50*** 28.13 1.7*** 31.1 Correlation Coefficient 𝜌 0.69*** 35.51 Goodness-of-fit Measures Log likelihood -3280.6 Adjusted pseudo r-squared 0.17 NOTE: - = not significant; *** = 1% significance level; ** = 5% significance level; * = 10% significance level; 𝜇1 is fixed at zero 57 instead of sharing AVs with others (Clewlow, 2016; Coll et al., 2014). Besides, the added cost of shared services might also negatively affect their propensity for SAV. In the case of the retrospective effects of accessibility measures, the variable representing the average land use index over the life course is found to have a positive relationship with the adoption of SAV. This implies that individuals living in a higher land use mix area are more likely to prefer shared services provided by AVs. Living in such a neighbourhood might provide higher accessibility to the residents to diversified land use by easily making shorter trips (Nazari, 2019). In addition, parking cost and searching time constraints associated with these dense neighbourhoods may also affect their preference towards SAVs. Another retrospective variable representing individuals who historically lived less than 3 kilometres from the nearest educational institution shows a positive relationship. This result further implies that individuals living in urban areas are more likely to adopt SAVs. Among the attitudinal factors, pro-shared-mobility attitude is found to positively affect the propensity for SAVs, as expected. Considering rideshare or carshare services a good alternative to owning a car, individuals with a pro-shared-mobility attitude might prefer the shared services provided by AVs rather than owning them. A pro-transit attitude is found to negatively affect the preference towards SAVs. As pro-transit individuals are assumed to reside in close proximity to frequent bus routes in this study context, transit might provide them with the desired accessibility. Besides, the added cost might also discourage them from using SAVs and eventually stick to transit. Pro-environment individuals are supposed to be highly concerned about the GHG emission, and therefore they might prefer more sustainable transportation modes such as SAVs instead of owning an AV. 58 In the case of income groups, lower-income individuals represented by the yearly income less than C$50,000 are more likely to prefer the shared services provided by AVs. Since the price of the AVs is expected to be higher than the conventional vehicles, lower-income individuals might not have the luxury of owning an AV. However, the potential of SAVs to provide enhanced mobility may attract those individuals to prefer shared services. Similarly, full-time workers are also likely to prefer SAVs, which is consistent with the findings of Nazari et al. (2018). Living in a neighbourhood with higher dwelling density is found to positively affect the preference towards SAVs, which agrees with the findings of Lavieri et al. (2017). As living in a dense neighbourhood might allow individuals to access desired destinations within closest proximity, people might prefer SAVs for making shorter trips. The model results also suggest the effect of individuals' daily travel behaviour on their preference for SAVs. Interestingly, individuals whose primary commute mode to work or school is auto-drive are more likely to prefer sharing AVs rather than owning one. The reason behind this might be the increased accessibility and reduced parking cost associated with the carsharing services provided by AVs. Marginal Effects The marginal effects analysis represented in Table 5.5 primarily shows the effects of the explanatory variables on the outcomes of the preference variables. To estimate the magnitude of the impact of each explanatory variable on each intermediate ordered response, the marginal effects of the joint BOP model are determined. Marginal effect is defined as the percentage change in probability of each preference level for AVO or SAV due to the unit change in an explanatory variable. Table 5 represents the marginal effects of the variables retained in the final model. The estimates provide important behavioural insights which may help to identify certain 59 groups for effective marketing schemes of AVs. For example, a unit increase in the average smartphone ownership in the household over the life course may increase the probability of being highly interested in AVO by 13.6%. Similarly, those historically residing in owned dwellings are approximately 4% more likely to have the highest level of preference for owning an AV. It is interesting to note the retrospective impact of the accessibility measures on the preference for SAVs. For example, historically living in a neighbourhood with higher land use mix diversity may increase the probability of having a higher preference for SAV by 22.8%. Therefore, urban dwellers having access to diversified land use may be targeted as one of the most promising adopters of SAV. Similar impacts can be observed from the exposure to Table 5.5 Marginal effects of the variables retained in the joint BOP model. Variable AVO SAV 0 1 2 3 4 0 1 2 3 4 Historical Exposure to Technology Average smartphone -0.199 -0.024 0.006 0.082 0.136 -0.0001 -0.0007 -0.0006 0.0002 0.0002 Advance vehicle technology -0.015 -0.003 -0.001 0.006 0.014 - - - - - Historical Household Characteristics and Vehicle Ownership Owned house -0.047 -0.009 -0.002 0.018 0.040 - - - - - Average VO 0.0002 0.0003 0.0009 -0.0006 -0.0002 0.0007 0.0004 0.0003 0 -0.0001 Historical Accessibility Measures Average LUI - - - - - -0.118 -0.066 -0.059 0.015 0.228 urban core > 3km -0.0001 0 0 0 0.0001 - - - - - School distance < 3 km - - - - - -0.0001 -0.0007 -0.0006 0.0002 0.0002 Technology Ownership Smartphone per person -0.0002 -0.0003 0 0.0006 0.0001 -0.132 -0.058 -0.039 0.039 0.190 Google home -0.149 -0.038 -0.022 0.043 0.166 -0.026 -0.015 -0.015 0.001 0.054 60 Variable AVO SAV 0 1 2 3 4 0 1 2 3 4 Attitudinal Factors Pro-shared-mobility 0.006 0.001 0 -0.002 -0.006 -0.003 -0.002 -0.002 0.0004 0.006 Pro-transit - - - - - 0.006 0.003 0.003 -0.001 -0.011 Pro-environment -0.006 -0.001 0 0.002 0.005 -0.003 -0.002 -0.001 0.0004 0.005 Socio-demographics and Built-environment Income < 50k - - - - - -0.032 -0.018 -0.016 0.004 0.063 Income ≥ 100k -0.037 -0.008 -0.003 0.013 0.034 - - - - - Full-time -0.025 -0.005 -0.001 0.009 0.022 -0.027 -0.015 -0.013 0.004 0.052 Dwelling density - - - - - 0 0 0 0 0.0001 Travel Characteristics Auto-drive - - - - - -0.013 -0.007 -0.006 0.002 0.025 Auto-passenger -0.066 -0.015 -0.007 0.022 0.065 - - - - - Walk 0.082 0.012 0 -0.033 -0.062 - - - - - NOTE: 0 = not at all interested, 1 = somewhat uninterested, 2 = neutral, 3 = somewhat interested, 4 = very interested technology in day-to-day life in the case of both AVO and SAV. For instance, a unit increase in the smartphone ownership per person in the household may increase the probability of being very interested in SAV by 19%. Not surprisingly, smartphones are going to play a significant part in accessing the shared services provided by AVs. Ownership of google home is another indicator of exposure to technology in daily life in this study context. Interestingly, individuals owning a google home may have a 16.6% and 5.4% higher probability of having a higher preference towards AVO and SAV. Lower-income groups (income less than C$50,000) are 6.3% more likely to prefer SAV. Similarly, full-time workers are 5.2% more likely to be very interested in SAV. Therefore, these two groups should be targeted for promoting AV sharing in an effective manner. Auto-passengers are 6.5% more likely to highly prefer AVO, whereas 61 active transportation such as walking mode users are 8.2% unlikely to be not at all interested in AVO. Summary This study adopts a life history-oriented approach to investigate individuals’ preference towards AVs in the following two dimensions: 1) a privately-owned vehicle, and 2) a vehicle in shared service. This study contributes by examining the historical deposition effects of changes and experiences over individuals’ life course such as exposure to technology, vehicle ownership history, historical measures of accessibility and built-environment of the residential location. This research also tests the effects of attitudinal factors and travel attributes including primary commute mode. Preference towards AV ownership (AVO) and shared AV (SAV) are modelled using data available at a five-point Likert scale of very uninterested to not at all interested. A joint BOP model is developed that accounts for the ordinal nature of the data and captures unobserved correlation among the preference variables. In the case of preference towards AVO, the model results confirm the historical deposition effects of exposure to technology; specifically, technology in daily life such as average ownership of smartphones, and advanced vehicle technology in the vehicle fleet of the most recent three households reveal a higher likelihood to own AVs. Historically suburban dwellers residing farther from the urban core are more likely to own AVs. Individuals living in a household with a higher number of smartphones per person and with a google home are found to have a higher likelihood of adopting AV as an owned vehicle. Among the attitudinal constructs, individuals with a pro-environment attitude show positive relationships for AVO and whereas a pro-shared-mobility attitude shows a negative relationship. High-income individuals and full-62 time workers are more likely to prefer owning AVs. Interestingly, auto-passengers are more likely to own AVs. In the case of SAV, the model results suggest that historical exposure to technologies such as smartphone ownership increases the likelihood of AV sharing. Historically urban dwellers repeatedly residing nearer to educational institutions and diversified land use mix areas are more likely to adopt SAV. Exposure to technology in daily life such as ownership of google home and a higher number of smartphones per person in the household is positively associated with the propensity for SAV. Individuals with a pro-environment attitude show a higher interest in SAV whereas a pro-transit attitude shows lower interest. As expected, lower-income individual shows higher likelihood for AV sharing. Living in a neighbourhood with a high dwelling density is found to have a positive relationship. Similarly, full-time workers and auto-drive commuters are likely to prefer SAV. Overall, the model results also suggest that an unobserved correlation exists between the preference for AVO and SAV which is evident from the statistically significant and higher value of cross-equation correlation factor ρ. The parameter estimation results and the elasticity estimates provide important behavioural insights which may be further used for policy planning and targeted marketing to promote AVs. For example, suburban dwellers living farther from the urban core may be targeted as the early adopters of AVO whereas urban dwellers with higher accessibility to diversified land use may be targeted as the adopters of SAV. Higher-income groups are expected to be the adopters of AVO whereas low-income groups are more likely to use the shared service provided by AVs. Although the study provides important behavioural insights regarding the adopters of AVs from a life history-oriented perspective, it is not free from certain limitations. Firstly, because of the 63 limited availability of the data several important attributes representing the retrospective effects of socio-demographics and accessibility measures were not reflected in the model results. Secondly, during the survey respondents were only provided with the definition of the AV according to SAE, the respondents were not enlightened with the cost of travelling in AVs. In this sense, there might be some reporting bias associated with the responses regarding the preference for AVO and SAV. Future research should be focused on more comprehensive methods of retrospective data collection to address such biases. Thirdly, some of the variables, such as the attitudinal attributes in the AVO and SAV models, and household income in the SAV model, are not significant. However, these variables were retained in the final model considering that they confirm priori hypotheses and provide important behavioural insights regarding the potential adopters of AVs. For example, individuals with a pro-environment attitude are found to have a higher likelihood to adopt AV as a personal vehicle as well as a vehicle in the shared fleet. Individuals with the pro-environment attitude are very concerned about air pollution and greenhouse gas (GHG) emissions from the burning of fossils and prefer the use of alternative clean energy. Since AVs are more likely to be an environmentally friendly technology, this preference of pro-environment individuals is expected. Interestingly, individuals with a pro-shared-mobility attitude might prefer the shared services provided by AVs rather than owning them. Individuals with a Pro-transit attitude are less likely to use SAVs. Finally, the study only captures the unobserved cross-equation correlation among the dependent variables. However, it fails to capture the unobserved heterogeneity among the individuals which may affect the long-term choices. Therefore, one of the immediate research focuses should be developing more advanced econometric models such as latent class or mixed 64 BOP model to capture the unobserved heterogeneity. This might eventually improve the overall significance of the model parameters. 65 Chapter 6: Modelling Vehicle Transaction Decisions in the AV Future Introduction This chapter focuses on modelling individuals’ vehicle transaction decisions when AVs will be widely available both in the form of privately-owned vehicles and shared vehicles. It is critical to understand how vehicle ownership behaviour will change in the presence of AVs. Individuals may manage their vehicle fleet by adding an AV, replacing a vehicle with an AV in the vehicle fleet, and switching to the SAV services, and choose to do nothing/ other. The primary objectives of this study are – (1) to develop an advanced econometric model to investigate individuals’ vehicle transaction decisions in the AV future and (2) to adopt a life history-oriented approach utilizing the retrospective survey data. The study examines the effect of historical experiences such as historical exposure to technology, the evolution of household demographics, and accessibility measures on vehicle transaction decisions. In addition, the effect of socio-demographic characteristics and attitudinal attributes are also tested in the model. A latent class random parameter logit (LCROL) model is developed, which captures both intra- segment and inter-segment heterogeneity. The outline of the chapter is as follows: section 6.2 explores the data used for the vehicle transaction modelling. Section 6.3 describes the methodology used, followed by the parameter estimation results and discussion in section 6.4. Finally, section 6.4 includes the conclusion and future researches. 66 Data One of the modules in the survey was a stated preference component, which collected information about respondents’ preferences towards autonomous vehicles. One of the questions of this component asked the respondents how they will prefer to adjust their household vehicle fleet in the future when AVs will be available for personal ownership and as a vehicle in the shared services. Their response can be aggregated into the following choice alternatives: (i) add AV: add a new AV to the current household vehicle fleet, (ii) SAV + same fleet: switch to SAV service and keep the current vehicle fleet size, (iii) replace with AV: replace an existing household vehicle with a new AV, (iv) SAV + reduced fleet: switch to SAV service and reduce the current vehicle fleet size, and (v) do nothing/other. A descriptive analysis suggests that 8.38% of the respondents are interested in adding an AV to their vehicle fleet. 31% of the respondents prefer to adopt SAV keeping the same vehicle fleet, whereas 19% prefer to downsize the vehicle fleet while switching to SAV. Around 9.97% are interested in adjusting their vehicle fleet by replacing a vehicle with an AV. Finally, 31.65% of the respondents prefer to do nothing or other in the presence of AVs on the road. The distribution of the alternatives is presented in Figure 6.1. After cleaning for the missing information and adding weights, the sample includes 1378 respondents. Methodology: Latent Class Random Parameter Logit Model In this study, an LCRPL model is developed to investigate vehicle transaction decisions in the AV future. The segment allocation component of the model takes the form of a multinomial logit (MNL) model. The probability of an individual i belongs to segment c can be formulated as follows: 67 µ𝑖𝑐 =𝑒𝑧𝑐𝑥𝑖∑ 𝑒𝑧𝑐𝑥𝑖𝐶𝑐=1; 𝑐 = 1, 2, …𝐶⁡⁡ where, 𝑧𝑐 is the vector of segment-specific coefficients and 𝑥𝑖 is the vector of explanatory Figure 6.1 Distribution of the dependent variables. variables. The utility derived from the vehicle transaction decision by an individual i making vehicle transaction decision j while belonging to a specific segment c is represented as follows: 𝑈𝑖𝑗,𝑐 = 𝛽𝑖𝑐𝑥𝑖𝑗 + 𝜀𝑖𝑐 ⁡ The probability of the vehicle transaction decision is as follows: µ𝑖𝑗,𝑐 =𝑒𝛽𝑖𝑐𝑥𝑖𝑗∑ 𝑒𝛽𝑖𝑐𝑥𝑖𝑗𝐽𝑗=1; 𝑗 = 1, 2, … . , 𝐽 The intra-segment heterogeneity is captured by assuming the parameters can vary randomly across individuals following the specification of the random parameter logit (RPL) model (Bujosa et al., 2010; Mehadil Orvin & Rahman Fatmi, 2020). Therefore, the vector of parameters 𝛽′𝑖𝑐 which can vary within the segment takes the following form: 𝛽′𝑖𝑐 =⁡𝛽𝑖𝑐 + ∆𝑧𝑖𝑐 +⁡𝜔𝑖 0.00%5.00%10.00%15.00%20.00%25.00%30.00%35.00%Add AV SAV+ same fleetReplace with AVSAV+reduced fleetDo nothing/ otherPercentageAlternativesDistribution of Vehicle Transaction Decisions68 where, 𝑧𝑖𝑐 represents the vector of explanatory variables that may affect the mean parameter estimate 𝛽𝑖𝑐, ∆ is the corresponding vector of coefficients and 𝜔𝑖 represents the random effect that captures the intra-segment unobserved heterogeneity. To accommodate the preference heterogeneity, the choice probability takes the following form: 𝑃𝑖𝑗|𝑐 =⁡∫𝑒𝛽𝑖𝑐𝑥𝑖𝑗∑ 𝑒𝛽𝑖𝑐𝑥𝑖𝑗𝐽𝑗=1𝑓(𝛽|𝜔)𝑑𝛽 where, 𝑓(𝛽|𝜔) is the probability density function. Therefore, the unconditional probability of an individual i making vehicle transaction decision j can be written in the following form: 𝑃𝑖𝑗 =∑µ𝑖𝑐𝑃𝑖𝑗|𝑐𝐶𝑐=1 The log likelihood function of the model takes the following form: 𝐿𝐿 =∑𝑙𝑛(∑µ𝑖𝑐 (∏(𝑃𝑖𝑗|𝑐)𝛿𝑖𝑗𝐽𝑗=1)𝐶𝑐=1)𝑁𝑖=1⁡ where, N is the number of observations and 𝛿𝑖𝑗 is the dummy variable which equals 1 when individual 𝑖 chooses alternative j, otherwise 0. The above log likelihood function is a multidimensional integral which does not have a closed-form (Bujosa et al., 2010). Therefore, a simulation technique is required to estimate the model parameters. The study maximizes the simulated log likelihood function according to Greene and Hensher (Greene & Hensher, 2013), which is as follows: 𝐿𝐿𝑠 =⁡∑𝑙𝑛(∑µ𝑖𝑐1𝑅∑(∏(𝑃𝑖𝑗|𝑐)𝛿𝑖𝑗𝐽𝑗=1)𝑅𝑟=1𝐶𝑐=1)𝑁𝑖=1 69 The above log likelihood function is maximized using 1000 Halton draws. The goodness-of-fit measures of the model are estimated and compared in terms of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted pseudo r-squared values. Independent Variables Considered The study tested a wide array of variables in the model, which can be categorized as historical exposure to technology, historical household characteristics, historical accessibility measures, attitudinal factors, and socio-demographic and built-environment variables. The summary statistics of the variables used in the model are represented in Table 6.1. Variables representing historical exposure to technology include exposure to vehicle technology and technology available in the household over the life course. This variable is generated by comparing the presence of advanced technology such as lane assist, parking assist, and autonomous emergency stop available in the vehicles owned by an individual in the most recent residences. To test the effect of exposure to technology in daily life, the average number of smartphones and computers available in the household for the most recent three residences. The historical household characteristics variables include the dwelling type and ownership type of the residence over the life course. Among the historical accessibility measures, variables representing the distance from the residence to the nearest point of interest such as school, food store, healthcare center, shopping center, bus stop etc. and distance to the urban core are tested. The study also tested the effect of land use mix diversity by generating a land use index (LUI) variable according to Bhat & Gossen (2004). The values of LUI range from 0 to 1, where the higher the value, the higher the land use mix diversity. TTMS includes 26 attitudinal statements where individuals’ responses were recorded in a five-point Likert scale (strongly agree to strongly disagree). Among these statements, 11 were 70 Table 6.1 Summary statistics of the variables retained in the LCRPL model. Variables Description Mean/ Percentage Standard Deviation Historical exposure to technology Vehicle technology Advanced technology such as lane assist, parking assist, and autonomous emergency stop available in the vehicles owned over the lifetime (dummy) 25.02% - Avg. computer ownership Average of the total number of computers in different residences over the lifetime 1.90 1.05 Avg. smartphone ownership Average of the total number of smartphones in different residences over the lifetime 2.20 1.15 Historical household characteristics Historically rented house Lived in a rented house over the lifetime (dummy) 52.57% - Historically single-detached house Lived in a single-detached house over the lifetime (dummy) 13.74% - Historical accessibility measures Historically home to closest school ≤1 km Historically lived ≤ 1km from the closest educational institution (dummy) 52.94% - Historically home to core distance > 1 km Historically lived > 1km from the urban core (dummy) 84.13% - Attitudinal factors Pro-AV Pro-AV attitude 7.75 3.91 Pro-environment Pro-environment attitude 9.91 3.64 Pro-car Pro-car attitude 0.62 2.03 Socio-demographic characteristics and built-environment Income > $100,000 Individual's yearly income C$100,000 or more 8.49% - Age 25-34 Individual's age 25-34 20.87% - Google home Owns a google home 14.09% - Land use index Land use mix index 0.60 0.09 directly related to individuals’ attitudes towards different travel modes, including AVs. Another 8 statements correspond to individuals’ attitudes towards land use and environment. Principle Component Analysis (PCA) is performed on these statements, which yields three variables representing attitude towards AVs and different travel modes, and another three variables representing attitude towards land use and environment. The PCA analysis results are presented in Table 6.2 and Table 6.3. 71 Table 6.2 PCA Results Showing Attitude towards AVs and different travel modes. Statements Pro-AV Pro-transit Pro-car I am proud to own a car because it gives me the flexibility and freedom to go around 0.789 Getting to work without a car is a hassle 0.789 I think ride-sharing or ride-hailing services, such as Uber and Lift, are great for the community. 0.746 Ridesharing is a great alternative to owning a car as it reduces the hassles to find parking 0.739 I find commute time using transit as a good use of my time -0.659 I feel embarrassed taking transit -0.884 Transit is an essential service 0.848 I prefer to pay more taxes to give more subsidies and improve transit 0.388 -0.399 I will prefer purchasing an autonomous vehicle when it will be available in the market 0.542 -0.682 I think car-sharing or ride-hailing services using the autonomous vehicle is a great idea 0.842 I think riding in an autonomous vehicle will give me more free time to perform other activities 0.688 -0.392 Table 6.3 PCA Results Showing Attitude towards Land Use and Environment. Statements Pro-environment Pro-urban Pro-suburban I prefer to live in a house with a backyard 0.776 I prefer to live in the inner city -0.734 I prefer to live within walking distance to frequent bus route 0.879 Living in a multi-family unit would not give me enough privacy -0.446 0.715 I prefer cycling and walking than driving 0.36 Households should be fined if their greenhouse gas emissions exceed a set daily limit 0.821 I am willing that my tax payees’ money be invested in the Infrastructure to support electric/other clean-fuel vehicle technologies 0.775 The price of gasoline should be raised to improve air quality 0.525 -0.389 -0.482 In the case of attitude towards AVs and different travel modes, the pro-AV attitude corresponds to the individuals who are inclined towards owning or sharing AVs when available and believe 72 that AVs can ensure more productive use of travel time. Pro-transit attitude corresponds to the individuals who believe transit to be an essential service and are inclined towards using transit. Pro-car attitude refers to the individuals who believe owning a car will reduce a lot of hassle while they are less interested in transit usage. In the case of attitude towards land use and environment, PCA results in pro-environment, pro-urban, and pro-suburban variables. Pro-environment attitude refers to environment concerned individuals who are aware of the adverse impact of GHG emission and therefore more interested in energy-efficient transportation modes. Pro-urban individuals are inclined towards living in high-density areas with frequent transit routes and prefer to use cycling and walking instead of driving cars. On the other hand, the pro-suburban attitude refers to the individuals are more concerned about their privacy. Therefore, they are more inclined towards living in single-detached houses farther from the inner city. Results and Discussion of the LCRPL Model Goodness-of-fit Measures The LCRPL model is estimated for an increasing number of segments, starting from 2. The 2-segment LCRPL model yields AIC and BIC values of 2411.30 and 2795.70, respectively. The 3-segment model shows higher AIC and BIC values. Hence, the 2-segment model fits the data better. Furthermore, an MNL model is developed for comparison purposes. The 2-segment outperforms the MNL model with a higher adjusted pseudo r-squared value of 0.36. Therefore, the LCRPL model with two segments is considered as the final model for further result discussions. The comparison of goodness-of-fit measures of different models is presented in Table 6.4. 73 Table 6.4 Comparison of goodness-of-fit measures. Measures MNL 2-Segment LCRPL 3-Segment LCRPL Log likelihood -1451.50 -1128.64 -1253.88 Adjusted pseudo r-squared 0.10 0.36 0.28 AIC 2953.00 2411.30 2745.80 BIC 3077.80 2795.70 3339.90 Segment Allocation Component The segment allocation component model results are presented in Table 6.5, which includes the socio-demographic characteristics and historical accessibility measures of the individuals. The segment membership probabilities of segments I and II are 48.80% and 51.20%, respectively. Table 6.5 Segment allocation component of LCRPL model. Variables Segment-I Segment-II Coefficient t-stat. Coefficient t-stat. Constant -0.27 -0.81 Reference Socio-demographic characteristics Age 25-34 -0.39 -1.12 Reference Income > $100,000 0.81*** 2.91 Reference Historical household characteristics Historically rented house -1.61*** -6.74 Reference Historically single-detached house 0.25 0.74 Reference Historical accessibility measures Historically home to core distance > 1 km 1.24*** 3.19 Reference Historically home to closest school < 1 km -0.19 -1.00 Reference The model is estimated considering segment-II as the reference segment. The model results show a negative parameter for individuals aged 25-34 in Segment-I, which indicates that younger individuals are less likely to belong to this segment. High-income individuals (yearly income more than $100,000) are more likely to be allocated to Segment-I. Individuals living in a single-detached house over the life course have a higher likelihood of belonging to Segment-I, whereas 74 individuals historically living in a rented house are less likely to be included in this segment. Furthermore, Individuals historically living in suburban areas farther from the urban core and the point of interest, such as schools or education centers are more likely to be included in Segment-1. Overall, middle to older adults, having higher income, historically residing in single-detached houses in suburban areas are likely to be included in Segment-I. Therefore, segment-I can be identified to include older and higher-income suburban dwellers. On the other hand, Segment-II is likely to include younger and lower-income urban dwellers. Parameter Estimation Results of the Vehicle Transaction Decisions Component The parameter estimation results of the vehicle transaction decisions component are presented in TABLE 4. The model results confirm the effects of historical exposure to technology and attitudinal factors on vehicle transaction decisions. The variables representing historical exposure to technology include the availability of advanced vehicle technology, average smartphone, and computer ownership over the life course. Attitudinal factors include pro-AV, pro-car, and pro-environment attitudes. The model captures heterogeneity within the segments for the land use diversity index variable, which represents the land use mix characteristics of the home neighbourhood of the individuals. In the case of adding an AV to the vehicle fleet, the pro-AV attitude shows a positive relationship. However, the LUI variable shows significant heterogeneity in mean in the case of pro-AV attitude for segment-I. This result indicates that if land use mix diversity index of suburban areas can be improved, suburban dwellers with pro-AV attitudes show a lower likelihood of adding an AV to their fleet. With the high land use mix diversity, individuals might need to make relatively shorter trips to access different destinations which are likely to be close, and parking constraints are higher in these areas. This might affect their decisions to adopt other sustainable modes such as walk, bike, transit, and even shared 75 services using AVs. Historically owning vehicles with advanced technology is found to be a significant factor for segment-I. This result indicates that past experience with similar types of technology might play a significant role, in the case of the likelihood to add an AV to the vehicle fleet by the suburban dwellers. The variable representing a higher number of computer ownership in the household over the life course is positively related to adding an AV in the fleet. Similarly, owning google home is positively associated with the decision to add an AV for suburban dwellers in segment-I. These results indicate that individuals’ both past and present exposure to technology has a significant effect on their decision to manage household vehicle fleet, specifically suburban dwellers adding an AV to the vehicle fleet. In the case of switching to SAV while keeping the current vehicle fleet unchanged, the pro-AV attitude shows a negative relation for segment-II. Perhaps, individuals are less comfortable in sharing the ride with a stranger. Inter-segment heterogeneity can be observed for the variable representing pro-environment attitude. Suburban dwellers in segment-I are less likely to switch to SAV. Urban dwellers in segment-II are more likely to prefer the more sustainable SAV mode while keeping the current fleet unchanged. Variables representing historical exposure to technology such as advanced technology in the vehicles owned over the life course and higher number of smartphones owned over the life course are positively associated with switching to SAV. However, in segment-II, the LUI variable shows significant heterogeneity in mean for the smartphone ownership variable, which indicates that the likelihood of switching to SAV while keeping the same vehicle fleet is likely to decrease with the increase in land use mix diversity. Higher land use diversity might improve the accessibility to different destinations; hence, individuals might be less inclined towards keeping the existing fleet and switching to SAV. 76 For replacing a vehicle in the household vehicle fleet with an AV, pro-car and pro-AV attitudes both show inter-segment heterogeneity. High-income individuals living in suburban areas included in segment-I are less likely to replace a vehicle with AV, whereas lower-income urban dwellers in segment-II have a higher likelihood of replacing it. Individuals' past experience with vehicle technology is positively associated with their decision to replace a vehicle with an AV. Similarly, individuals living in a household with a higher number of smartphones over the life course are also likely to modify their fleet by replacing a vehicle with an AV. In the case of switching to SAV and reduce the vehicle fleet, the model results suggest that pro-AV individuals in Segment-I have a lower likelihood of switching to SAV and reduce the vehicle fleet. This result indicates that higher-income suburban dwellers are less likely to switch to SAV and downsize their vehicle fleet. On the other hand, lower-income urban dwellers in segment-II with a pro-AV attitude are more likely to prefer switching to SAV and downsize vehicle fleet size as it might reduce the hassle of owning and parking the vehicles in high-density areas. Besides, living in the urban core might affect their decision to use SAV as they are more likely to make shorter trips due to the increased accessibility to different points of interest (Nazari et al., 2018). In such areas, SAVs are also widely available. The results show that individuals living in a household with a higher number of smartphones over the life course are more likely to switch to SAV and downsize the household fleet. In the case of do nothing/other, pro-car individuals are likely to do nothing, as they might prefer to maintain their existing vehicle fleet. Pro-environment individuals also have a higher likelihood of do nothing/other. Historical exposure to vehicle technology shows a negative relationship with do nothing/ other. However, this variable shows heterogeneity in mean for land use diversity index. Therefore, individuals residing in higher land use mix areas might prefer to do 77 transactions of vehicles in the presence of AVs. The variable representing a higher number of computers in the household over the life course is negatively associated with do nothing/other for segment-I. On the contrary, individuals in segment-II have a higher likelihood of doing Table 6.6 Vehicle Transaction decisions component results of the LCRPL model. Variables Segment-I Segment-II Coefficient t-stat. Coefficient t-stat. Add a new AV to the current household vehicle fleet Constant 3.41* 1.69 -18.09*** -5.65 Pro-AV (RP) 2.76*** 3.91 1.62*** 3.15 Vehicle technology 2.78*** 4.89 - - Avg. computer ownership - - 8.94*** 6.27 Google home 1.72*** 3.58 - - Switch to SAV service and keep the current vehicle fleet size Constant 4.18 1.15 -8.72*** -5.65 Pro-AV - - -0.15*** -2.70 Pro-environment -3.28*** -4.59 3.59*** 6.59 Vehicle technology 2.73*** 2.03 22.83 0.96 Avg. smartphone ownership (a) (RP) 2.48*** 2.84 11.45*** 4.53 Replace existing household vehicle with a new AV Constant 0.34 0.16 1.09 0.26 Pro-AV -4.23*** -6.42 2.27*** 5.07 Pro-car -1.84*** -7.52 0.67 0.8 Vehicle technology 3.16*** 5.49 - - Avg. smartphone ownership 2.12*** 5.55 - - Switch to SAV service and reduce the current vehicle fleet size Constant 8.12*** 4.24 2.53 0.93 Pro-AV -3.52*** -5.37 2.89*** 4.21 Pro-environment -0.46*** -5.80 - - Avg. smartphone ownership (b) (RP) 2.48*** 2.84 11.54*** 4.53 Avg. computer ownership - - -3.44*** -2.69 Do nothing/other Pro-car (RP) 2.76*** 3.91 4.04*** 1.89 Pro-environment - - 2.87*** 5.62 Vehicle technology (RP) -37.71*** -2.68 - - Avg. computer ownership -4.71*** -6.15 1.38*** 3.88 Heterogeneity in the mean of the random parameter Pro-AV: LUI -1.74*** -3.65 - - Pro-car: LUI 18.12*** 4.65 4.04*** 2.14 Avg. smartphone ownership: LUI (a) 28.92*** 5.46 -5.07*** -3.85 Avg. smartphone ownership: LUI (b) -4.36*** -3.08 11.55*** 4.53 Vehicle technology: LUI 75.05*** 3.05 - - Note: stat = statistic; - = not significant; *** = 1% significance level; ** = 5% significance level; * = 10% significance level; 78 nothing/other. These results indicate that both past and present acquaintance with similar types of technology might lead higher-income suburban dwellers to do vehicle transactions, whereas lower to middle-income individuals in Segment-II might prefer to do nothing/other. Summary The focus of this study is to understand individuals’ preference towards adjusting their vehicle fleet when AVs will be available as personally owned vehicles as well as vehicles in the shared fleet. Vehicle fleet composition is modelled as a dynamic decision process of adding, disposing, replacing, and do-nothing decisions. A life history-oriented approach is adopted to explore the effects of exposure to technology over the life-course such as technology device ownership and the availability of advanced vehicle technology. In addition, the effects of historical household attributes, historical accessibility measures, and attitudinal factors are tested. A latent class random parameter logit model (LCRPL) is developed using data from a retrospective TTMS collected in the Okanagan region of British Columbia. The LCRPL model captures multi-dimensional heterogeneity – i.e. assuming a discrete distribution to allocate individuals’ into heterogeneous discrete segments. Then a continuous distribution is assumed to capture heterogeneity within the segments. The model is estimated for two segments – segment-I includes higher-income individuals who are historically suburban dwellers, and segment-II includes lower-income individuals who are historically suburban dwellers. The model results confirm that individuals’ historical exposure to technology in different life domains, e.g. tech-savviness in daily life and availability of vehicle technology over the life course have significant effects on their vehicle transaction decisions in the AV future. For example, historical exposure to vehicle technologies such as lane assist, 79 parking assist, and autonomous emergency stop is likely to trigger a switch to SAV and keep the existing vehicle fleet. Interestingly, historically suburban dwellers prefer to own AV and add it to the existing fleet. They also showed preference towards replacing a vehicle from the existing fleet with AV. Historical smartphone ownership shows a positive association towards switching to SAV and downsizing vehicle fleet. However, historically suburban dwellers reveal heterogeneity in the mean for the land use index variable. They in fact reveal a positive association towards owning it and replacing an existing vehicle from the fleet. The model results suggest that significant heterogeneity exists among the individuals. Overall, older higher-income individuals who historically lived in suburban neighbourhoods are more likely to own AVs and adjust their vehicle fleet by adding as well replacing the existing vehicle with AVs. On the other hand, younger lower-income individuals who historically urban dwellers are more likely to use SAV and adjust vehicle fleet by downsizing it as well as maintaining the same fleet size. For example, urban dwellers with a pro-AV attitude are more likely to prefer SAV and downsize their existing fleet. On the other hand, suburban dwellers with a pro-AV attitude are less likely to prefer SAV and downsize their existing fleet. Suburban dwellers with pro-AV attitudes are more likely to own it and add to the existing vehicle fleet. This variable confirms intra-segment heterogeneity for the land use index. These findings indicate that mixed land use developments in the suburban areas might encourage suburban dwellers to not own AVs and add to the existing vehicle fleet. Overall, the model results provide important behavioural insights on how the AVs might affect household vehicle ownership specifically their vehicle transaction decisions. As AVs are going to hit the road very soon, understanding which group of individuals might make the transaction and which factors will significantly affect their choices are critical for policy developments. The 80 inter-segment and intra-segment heterogeneity shed light on the need to accommodate heterogeneity while policymaking. The study has certain limitations. For instance, some of the variables retained in the final model were not significant. However, these variables are retained in the model as they confirm priori hypotheses and provides important behavioural insights. One of the limitations of the study is that the retrospective effect of individuals' travel characteristics, such as primary commute mode, commute distance etc. could not be tested in the model as these data were not available. In addition, respondents of the survey were not provided with the information regarding the price of AVs and the cost of using SAV. Therefore, there might be some biases associated with the responses Future researches should focus on the extensive collection of retrospective data to accommodate more historical variables in the model as well as provide a comprehensive overview on the prices and costs associated with owning AVs and using SAVs to remove biases in the responses. 81 Chapter 7: Conclusions Summary of the Study The study adopts a life history-oriented approach of modelling the AV adoption decisions by exploring the preferences towards different levels of vehicle automation, preferences towards AVO and SAV, and vehicle transaction decisions in the AV future. One of the significant contributions of the thesis is to examine how previous experiences along the life course, such as historical exposure to technology, vehicle ownership history, household characteristics, and accessibility measures of the residence, affect the adoption of AVs. Besides, the study also analyzed the effect of attitudinal factors, travel characteristics, and socio-demographic attributes. Data from a retrospective survey conducted in the Okanagan region of BC is utilized in this study. Advanced econometric models are developed to capture the unobserved heterogeneity among the individuals and to reveal the error correlation among preferences. The summary of the key findings are as follows: An RPROL model is developed to explore the relative preferences for different vehicle automation levels and to capture unobserved heterogeneity among the sample individuals. The model results suggest that historical experiences significantly influence individuals’ preferences towards different levels of vehicle automation. The results confirm the effects of historical exposure to technology, including technology used in daily life and technology available in the household vehicle fleet. Individuals historically exposed to advance technological features in their vehicle over their lifetime have a higher likelihood of adopting a 82 higher level of vehicular automation. They also show a lower interest in lower levels of automation. On the other hand, those who have advance technological features in the current vehicle only have a lower likelihood of adopting higher vehicle autonomy. Technological device ownership, such as smartphones and google home, have positive effects on higher levels of vehicular automation. Individuals historically living in an owned household and who have experienced increased income over their lifetime are interested in higher vehicle automation. However, people living in urban areas near their work location are not interested in higher levels of vehicular automation. Furthermore, variables representing younger adults and individuals without driver’s licence show a higher interest in full automation, whereas they are not interested in no automation. The model confirms the existence of significant heterogeneity among the individuals. For example, pro-urban individuals prefer no automation; whereas, pro-suburban individuals are more inclined towards full automation. Interestingly, both pro-urban and pro-suburban variables show significant heterogeneity with a larger standard deviation value. The higher income group (i.e. income > $100,000) is found to be highly interested in higher and full vehicle automation. However, this variable confirms significant heterogeneity with a large standard deviation. Similar heterogeneity is confirmed for individuals with lower income, pro-car attitude, and multiple travel tools. Overall, this study gives important behavioural insights; particularly, the effects of historical experiences and attitudinal factors on the preference for different levels of vehicle autonomy. The findings will help cities and vehicle manufacturers for targeted marketing of different levels of vehicle automation as well as fully autonomous vehicles. Individuals’ preferences towards AVs are investigated in two dimensions - a privately-owned vehicle and a vehicle in shared service. Preference towards AVO and SAV are modelled using 83 data available at a five-point Likert scale of very uninterested to not at all interested. A joint BOP model is developed that accounts for the ordinal nature of the data and captures unobserved error correlation among the preference variables. In the case of preference towards AVO, the model results confirm the historical deposition effects of exposure to technology; specifically, technology in daily life such as average ownership of smartphones and advanced vehicle technology in the vehicle fleet of the most recent three households reveal a higher likelihood to own AVs. Historically suburban dwellers residing farther from the urban core are more likely to own AVs. Individuals living in a household with a higher number of smartphones per person and with a google home are found to have a higher likelihood of adopting AV as an owned vehicle. Among the attitudinal constructs, individuals with a pro-environment attitude show positive relationships for AVO, whereas the pro-shared-mobility attitude shows a negative relationship. High-income individuals and full-time workers are more likely to prefer owning AVs. Interestingly, auto-passengers are more likely to own AVs. In the case of SAV, the model results suggest that historical exposure to technologies such as smartphone ownership increases the likelihood of AV sharing. Historically urban dwellers repeatedly residing nearer to educational institutions and diversified land use mix areas are more likely to adopt SAV. Exposure to technology in daily life, such as ownership of google home and a higher number of smartphones per person in the household, are positively associated with the propensity for SAV. Individuals with a pro-environment attitude show a higher interest, whereas a pro-transit attitude shows a lower interest in SAV. As expected, lower-income individual shows higher likelihood for AV sharing. Living in a neighbourhood with a high dwelling density 84 is found to have a positive relationship. Similarly, full-time workers and auto-drive commuters are likely to prefer SAV. Overall, the model results also suggest that an unobserved correlation exists between the preference for AVO and SAV, which is evident from the statistically significant and higher value of cross-equation correlation factor 𝜌. The parameter estimation results and the elasticity estimates provide important behavioural insights which may be further used for policy planning and targeted marketing to promote AVs. For example, suburban dwellers living farther from the urban core may be targeted as the early adopters of AVO, whereas urban dwellers with higher accessibility to diversified land use may be targeted as the adopters of SAV. Higher-income groups are expected to be the adopters of AVO, whereas low-income groups are more likely to use the shared service provided by AVs. The longer-term effect of AVs in the household vehicle fleet is explored by investigating individuals’ vehicle transaction decisions. An LCRPL model is developed using the retrospective survey data that captures multidimensional heterogeneity. The model results reveal that individuals having historical exposure to technology have a higher likelihood of preferring to switch to SAV. They are also found to be interested in replacing a vehicle with AV, whereas unlikely to prefer to do nothing/other. Individuals living in a single-detached house and having a higher number of vehicles over their lifetime are more likely to add an AV to their current fleet. Historically higher-income households are more likely to replace a vehicle in the current fleet with an AV, whereas they are unlikely to do nothing/ other. Individuals living in suburban areas farther from the urban core and in less dense land use diversity have a higher likelihood of adding AV in their vehicle fleet. Besides, they might also prefer to replace a vehicle with AV and switch to SAV. These results indicate that rich people are more likely to adjust their vehicle 85 fleet in the presence of AV and SAV services. Owning a google home might positively affect individuals to add an AV in the fleet, whereas negatively affect the preference to do nothing. Travel attributes such as a subscription to carshare services may lead an individual to switch to SAV services as well as continue to use the same service by preferring to do nothing. Active transport mode users are less likely to adjust their fleet by either adding AV or switching to SAV. Overall, the study provides important behavioural insights on how individuals might adjust their vehicle fleet in future when the AVs and SAVs are widely accessible. The study focuses on the historical deposition effects of the individuals, which might help the policymakers to identify the group of individuals who are more likely to adjust their vehicle fleet in the AV future Contributions of this Thesis The contribution of the thesis can be summarized as follows: Firstly, the thesis contributes to the literature by revealing the effect of historical exposure to technology in different domains of life, such as technology in the household and technology in the vehicle fleet, on the preferences for AVs. In addition, the findings confirm the significant effect of historical household characteristics, vehicle ownership history, accessibility measures and the built-environment of the residence over the life course. Secondly, the effect of AV adoption on household vehicle fleet is examined by exploring the vehicle transaction decisions in an AV future. Finally, the thesis adopted advanced econometric modelling methods to investigate the preferences for AVs. The model results reveal the unobserved heterogeneity among the individuals as well as the error correlation that jointly affect the preferences regarding the adoption of AVs. 86 Future Research The study provides important behavioural insights regarding how individuals are going to adopt the advanced vehicle automation, AVs, and how they are going to manage vehicle fleet in the AV future. However, the study has certain limitations which should be addressed in future researches. Firstly, because of the limited availability of the retrospective data, the historical travel characteristics such as commute mode, travel tool ownership, and travel cost could not be tested in the models. Future studies should focus on collecting retrospective data regarding individuals’ travel attributes to understand their impact on the adoption decisions of AVs. Secondly, during the survey, respondents were only provided with the definition of the AV according to SAE. Therefore, respondents were not able to actually realize the benefits and concerns associated with travelling in an AV which might lead to biases in their responses. Creating a real-life experience of travelling in an AV and then collecting information regarding respondents’ choices regarding AV might yield more convenient data y removing such biases. In addition, the respondents were not presented with the approximate cost of owning an AV and the cost of using SAV. In this sense, there also might be some reporting bias associated with the responses regarding the preference for AVO and SAV. Future research should be focused on more comprehensive methods of retrospective data collection to address such biases. In the case of investigating individuals’ preferences towards AVO and SAV, the JBOP model only captures the unobserved error correlation between the preference variables. 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Transportation Research Part C: Emerging Technologies, 90(October 2017), 156–165. https://doi.org/10.1016/j.trc.2018.03.005 98 Appendices Appendix A : Travel Technology and Mobility Survey (TTMS) SECTION 1: Information of current and previous two residential locations Question Options Month (mm) Open-ended Year (yyyy) Open-ended The most significant reason for moving out of this residence 1. To be closer to work 2. To be closer to transit 3. To be closer to good schools 4. To be closer to shopping, entertainment, etc. 5. Due to change in household size (e.g. birth) 6. To live in a desirable house/apartment 7. Due to marriage or common-law living 8. To live in a desirable neighbourhood 9. Other Dwelling Type 1. Single-detached 2. Semi-detached 3. Town/Row house 4. Apartment (less than 5 floors) 5. Apartment (5 or more floors) 6. Other Home Ownership Type 1. Owned 2. Rented Number of rooms in the residence 1. One 2. Two 3. Three 4. Four 5. Five or above Number of bedrooms in the residence 1. One 2. Two 3. Three 4. Four 5. Five or above If owned, what is/was the approximate purchase price? Open-ended If rented, what is/was the approximate rental amount Open-ended If sold, what was the approximate selling price? Open Ended 99 Question Options Did you compromise in choosing your residence because of unaffordable housing options? 1. Yes 2. No Number of people in the household 1. One 2. Two 3. Three 4. Four 5. Five 6. Six 7. Seven 8. Eight 9. Nine 10. Ten or above Number of children in the household (under the age of 18) 1. Zero 2. One 3. Two 4. Three 5. Four 6. Five or above Number of private vehicle ownership 1. Zero 2. One 3. Two 4. Three 5. Four 6. Five or Above Number of bicycle ownership 1. Zero 2. One 3. Two 4. Three 5. Four 6. Five or above Number of people with monthly transit pass ownership 1. Zero 2. One 3. Two 4. Three 5. Four 6. Five or above Annual gross household income 1. Under 10,000$ 2. 10,000 – 29,999$ 3. 30,000 – 49,999$ 4. 50,000 – 79,999$ 5. 80,000 – 99,999$ 6. 100,000 – 149,999$ 7. 150,000$ and above 100 Question Options Most significant reason for choosing this residence 1. Closer to bus/transit stops 2. Closer to work 3. Closer to school 4. Closer to shopping 5. Closer to family/friends 6. Closer to healthcare facilities 7. Low crime rates 8. Characteristics of house 9. Neighbourhood preference SECTION 2: Information on the energy consumption and technology ownership in the current and previous two residential locations Question Options Sources of energy in residence (* Multiple choice allowed) 1. Electricity 2. Natural Gas 3. Oil 4. Solar Source of energy for hot water 1. Electricity 2. Natural Gas 3. Oil 4. Solar 5. Other Source of energy for heating 1. Electricity 2. Natural Gas 3. Oil 4. Solar 5. Other Number of desktop computers in the household 1. Zero 2. One 3. Two 4. Three 5. Four 6. Five or above Number of laptops Number of televisions Number of smartphones Number of land phones Do you have a Google Home 1. Yes 2. No Units of stove 1. Zero 2. One 3. Two 4. Three Units of dishwasher Units of home disposer Units of refrigerators 101 Units of microwave oven 5. Four 6. Five or above Units of washing machine Units of cloth dryer SECTION 3: Information related to all the private vehicles Value Options Vehicle Number 1. Vehicle 1 2. Vehicle 2 3. Vehicle 3 4. Vehicle 4 5. Vehicle 5 6. Vehicle 6 7. Vehicle 7 8. Vehicle 8 9. Vehicle 9 10. Vehicle 10 Question Options Vehicle ownership status 1. Currently Owned 2. Previously Owned Vehicle make Open-ended Vehicle Model Open-ended Model Year Open-ended Fuel type 1. Gasoline 2. Diesel 3. Plug-in electric 4. Hybrid electric 5. Plug-in hybrid electric 6. Other New or used vehicle 1. New 2. Used Vehicle ownership type 1. Purchased 2. Leased If purchased, year of purchase Open-ended If purchased, approx. purchase price Open-ended if disposed off, year of disposal (yyyy) Open-ended Method of disposal 1. Sold 2. Traded 3. Lease expired 4. Scrapped 102 Question Options Technologies available in the vehicle (*Multiple choice allowed) 1. Bluetooth 2. Navigation 3. Cruise control 4. Lane assist 5. Parking assistance 6. Autonomous emergency stop 7. Blindspot detection 8. Backup camera Vehicle Trim/ Variant Open-ended If selected other please specify Open-ended SECTION 4: Information related to the people currently living in the household including children Question Options Age Open-ended Gender 1. Male 2. Female 3. Other 4. Prefer not to disclose Driver’s license ownership 1. Yes 2. No Monthly transit pass ownership 1. Yes 2. No Subscription to shared mobility *Multiple choice allowed 1. Carshare 2. Bikeshare 3. None Highest level of education 1. No certificate 2. High school or equivalent 3. Apprenticeship/Diploma/Trade certificate 4. College certificate/Diploma equivalent 5. University/Diploma-below bachelor level 6. University bachelor’s degree or above Employment status 1. Full-time (>=30 hrs/week) 2. Part-time (< 30 hrs/week) 3. Unemployed 4. Retired 5. Student 6. Homemaker 7. Volunteer 8. Other 103 Question Options Primary mode of commute to work/school 1. Auto driver 2. Auto passenger 3. Transit 4. Bicycle 5. Walk 6. Other Commute Distance (in Kilometres) Open Ended Person’s Income 1. Under 10,000$ 2. 10,000 – 29,999$ 3. 30,000 – 49,999$ 4. 50,000 – 79,999$ 5. 80,000 – 99,999$ 6. 1,00,000 – 1,49,999$ 7. 1,50,000$ and above Person’s attitude towards life 1. Happy 2. Somewhat happy 3. Indifferent 4. Somewhat Unhappy 5. Unhappy Person’s mental and physical health status 1. Healthy 2. Somewhat healthy 3. Indifferent 4. Somewhat Unhealthy 5. Unhealthy SECTION 5: Indicate in the following calendar about the important life events that occurred in your household. (*Multiple choice allowed) Important Life Events Options Birth/Adoption of a child Open-ended: (Year (yyyy)) Death of a member Member moved out Member moved in Addition of a job Loss of a job Change of a job Retired Withdrawal from the labor force Returned to school Marriage Divorce 104 Important Life Events Options Moved out of parent's house Open-ended: (Year (yyyy)) SECTION 6: Information related to how to use Autonomous Vehicle (AV) technology Question: Which level of vehicular automation are you most comfortable using? Level Options Level 0: No Autonomy – The driver performs all driving tasks Rank on the scale of 1 to 6, Where, 1 = most preferred option and 6 = least preferred option Level 1: Driver Assistance – Vehicle is controlled by the driver, but some driving assistance features are available Level 2: Conditional Automation – Driver must always be ready to take control of the vehicle with notice but does always not require monitoring the environment Level 3: Conditional Automation – Driver must always be ready to take control of the vehicle with notice but does always not require monitoring the environment Level 4: High Automation – The vehicle can perform all driving functions under certain conditions. The driver may have the option to control the vehicle Level 5: Full Automation – The vehicle can perform all driving functions under all conditions. The driver may have the option to control the vehicle Question: Answer the following questions on the five-point scale. Questions Options Question 1: How interested will you be in owning a fully autonomous vehicle in the future? 1. Very interested 2. Somewhat interested 3. Neutral 4. Somewhat uninterested 5. Not at all interested Question 2: How interested will you be in using autonomous vehicles as shared services such as Uber and Lyft? Question 3: How interested will you be in putting up your personal autonomous vehicle in shared services when not in use (e.g. Uber and Lyft)? Question 4: If subscription to shared autonomous vehicles (SAVs) reduces your current vehicular expenditure by 50%, will you be interested in reducing your current vehicle fleet by shifting to SAVs? 105 Question: For which activities would you likely use the AVs or SAVs for? Activities Options Work/School Rank on the scale of 1 to 7, where 1 = most preferred option and 7 = least preferred option Restaurant Recreation Shopping & groceries Visit family/friends Personal business such as bank, visit to doctor, government office, attorney, etc. Pickup/ drop off someone Question Options How do you think would you manage your vehicle fleet if you are interested in AVs? 1. Add a new autonomous vehicle to the current household vehicle fleet 2. Switch to shared autonomous vehicular service and keep the current vehicle fleet size 3. Replace existing household vehicle with a new autonomous vehicle 4. Switch to shared autonomous vehicular service and reduce the current vehicle fleet size 5. Do nothing 6. Other if selected other please specify Open-ended SECTION 7: Please enter information related to how would you respond to newer and smart mobility Question Options Question 1. Are you interested in the expansion of ride-hailing services like Uber and Lyft in British Columbia? 1. Very interested 2. Somewhat interested 3. Neutral 4. Somewhat uninterested 5. Not at all interested Question 2. How often do you think would you use the ride-hailing services like Uber and Lyft? 1. Never 2. Once a month or less 3. 2-3 times a month 4. Once a week 5. 2-3 times a week 106 6. More than 3 times a week 7. Daily Question 3. Do you have the subscription to the bike-sharing services, such as Drop bike? 1. Yes 2. No Question 4. How often do you use a bike? 1. Never 2. Once a month or less 3. 2-3 times a month 4. Once a week 5. 2-3 times a week 6. More than 3 times a week 7. Daily Question 5. How often do you use transit service? SECTION 8: Information related to the current and previous two workplace locations of the primary and secondary worker in the household. Question Options Primary or Secondary worker in the household 1. Primary 2. Secondary Workplace Number 1. Current Workplace 2. Previous Workplace 3. Previous to Previous Work Start Date (Month (mm)) Open-ended Work Start Date (Year (yyyy)) Open-ended Work End Date (Month (mm)) Open-ended Work End Date (Year (yyyy)) Open-ended Distance from home to work (in kms) Open-ended SECTION 9: Please answer the following statements on a five-point scale Statements Options I prefer to live in the suburban areas, as it offers a good quality of life for families 1. Strongly agree 2. Agree 3. Neutral 4. Disagree 5. Strongly disagree I prefer to live in a house with a backyard I prefer to live in the inner city I prefer to live within walking distance to frequent bus route Living in multi-family unit would not give me enough privacy I am proud to own a car because it gives me the flexibility and freedom to go around Getting to work without a car is a hassle It is stressful to drive and get stuck in the traffic I prefer cycling and walking than driving 107 Statements Options I think ride-sharing or ride-hailing services, such as Uber and Lift, are great for the community. 1. Strongly agree 2. Agree 3. Neutral 4. Disagree 5. Strongly disagree Ride sharing is a great alternative to owning a car as it reduces the hassles to find parking I don’t feel safe sharing my ride with strangers Bike share services increase accessibility to many destinations I find commute time using transit as a good use of my time I feel embarrassed taking transit Transit is an essential service I prefer to pay more taxes to give more subsidies and improve transit I limit my driving, because it improves air quality I am willing to pay a little more to use an electric vehicle/other clean fuel vehicle for improving air quality Households should be fined if their greenhouse gas emissions exceed a set daily limit I am willing that my tax payees money be invested to the Infrastructure to support electric/other clean fuel vehicle technologies Price of gasoline should be raised to improve air quality I will prefer purchasing an autonomous vehicle when it will be available in the market I think car-sharing or ride-hailing services using autonomous vehicle is a great idea I think riding in an autonomous vehicle will give me more free time to perform other activities Travelling in autonomous vehicle would be stressful Reliability of autonomous technology might be an issue 108 Appendix B : Comparison of Different Attributes between TTMS and Census Statistics Table B.1 Distribution of Household Income Household Income TTMS Census Under $10,000 2.00% 2.34% $10;000 to $29;999 15.00% 15.13% $30;000 to $49;999 17.50% 17.54% $50;000 to $79;999 21.25% 21.91% $80;000 to $99;999 11.88% 11.62% $100;000 to $149;999 18.13% 17.90% $150;000 and over 14.25% 13.56% Table B.2 Distribution of Tenure Type. Tenure Type TTMS Census Owner 55.75% 71.00% Rented 44.25% 29.00% Table B.3 Distribution of Dwelling Type. Dwelling Type TTMS Census Single-detached house 58.32% 52.00% Semi-detached house 4.44% 5.00% Row house 10.01% 7.00% Apartment in a building that has fewer than five storeys 20.53% 32.00% Apartment in a building that has five or more storeys 2.94% 2.00% Others 3.75% 2.00%
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Fatmi, Mahmudur Rahman
Modelling individuals’ longer-term preferences towards autonomous vehicles and their effects on vehicle ownership
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