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Reinterpreting Vehicle Ownership in the Era of Shared and Smart Mobility Rounaq Basu

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Reinterpreting Vehicle Ownership in the Era of Shared and Smart Mobility Rounaq Basu Reinterpreting Vehicle Ownership in the Era of Shared and Smart Mobility by Rounaq Basu B.Tech. in Civil Engineering Indian Institute of Technology Bombay (2016) Submitted to the Department of Urban Studies and Planning in partial fulfillment of the requirements for the degrees of Master in City Planning (MCP) and Master of Science in Transportation (MST) at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2019 ○c Rounaq Basu, MMXIX. All rights reserved. The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Author............................................................................ Department of Urban Studies and Planning May 21, 2019 Certified by. Joseph Ferreira Professor of Urban Planning & Operations Research Thesis Supervisor Accepted by....................................................................... Ceasar McDowell Professor of the Practice Co-Chair, MCP Committee Accepted by....................................................................... Heidi Nepf Donald and Martha Harleman Professor of Civil and Environmental Engineering Chair, Graduate Program Committee 2 Reinterpreting Vehicle Ownership in the Era of Shared and Smart Mobility by Rounaq Basu Submitted to the Department of Urban Studies and Planning on May 21, 2019, in partial fulfillment of the requirements for the degrees of Master in City Planning (MCP) and Master of Science in Transportation (MST) Abstract Emerging transportation technologies like autonomous vehicles and services like on-demand shared mobility are casting their shadows over the traditional paradigm of vehicle owner- ship. Several countries are witnessing stagnation in overall car use, perhaps due to the proliferation of access-based services and changing attitudes of millennials. Therefore, it becomes necessary to revisit this paradigm, and reconsider strategies for modeling vehicle availability and use in this new era. This thesis attempts to do that through three studies that contribute to the methodological, conceptual, and praxis literatures. The first study proposes a hybrid modeling methodology that leverages machine learning techniques to enhance traditional behavioral discrete choice models used in practice. The usefulness of this model to predict market shares of unforeseen choices like new mobility services is illustrated through an application to the off-peak car in Singapore. Our model significantly improves upon the market shares predicted by traditional models throughan average reduction of 60% in RMSE. The second study shifts the focus from vehicle ownership to vehicle availability in the form of mobility bundles. We leverage Singapore’s unique policy environment to empirically model households’ preferences for unique mobility bundles that are constructed in an ordinal fashion. This is followed by an examination of car usage within the household. Significant intra-household interaction effects are found with respect to job location, in addition tothe observation of gender biases in the decision-making process. The third study evaluates the effectiveness of car-lite policies that seek to replace private vehicle usage with shared and smart mobility services. Behavioral responses to the policy and associated market effects are modeled using an integrated land use transport simulator calibrated for Singapore. Initially favorable aggregate outcomes tend to disappear as short- term market effects set in. Although outcomes stabilize to a certain extent over the long- term, the initial characteristics of the study area are found to strongly influence the success of such policies. Thesis Supervisor: Joseph Ferreira Title: Professor of Urban Planning & Operations Research 3 4 Acknowledgments First and foremost, I would like to express my gratitude to my supervisor, Prof. Joe Ferreira. In addition to the official roles of academic advisor and research supervisor, he hasbeen a mentor to me throughout my time at MIT in every sense of the word. His guidance, advice, and trust have been truly invaluable. Thanks to Joe, I have been able to engage in exciting research opportunities, challenge myself in rigorous academic pursuits, and enjoy a rewarding teaching experience. His encouragement and support, even as I doubted myself over the ambitious dream of finishing three studies in the limited time-frame of a Master’s degree, have been unwavering and critical for this thesis. I would like to thank my reader Prof. Chris Zegras for providing valuable feedback throughout the research process. Chris supported me during an important move in my life, for which I am truly grateful. I would also like to express my appreciation for the opportunities he provided, both in and outside academia, that helped me think critically about connecting academic research and public policy. I am grateful to Prof. Amy Glasmeier for the care and kindness she has bestowed on me. It has been an absolute pleasure to work with her, and learn from her experience and knowledge. I thank my lab-mates Roberto Ponce Lopez and Jingsi Shaw for their friendship and camaraderie, which helped me keep my head up in the academic whirlwind. I appreciate the support from my colleagues in Singapore, Chetan Rogbeer and Xiaohu Zhang, in particular. I also thank Mazen Danaf and Mathew Swisher, who were very patient and generous with their time and feedback when I wanted to bounce my ideas off them. A note of thanks is also due to Ellen Rushman and Sandy Wellford at DUSP, and Kiley Clapper at CEE, for their help with administrative issues. I appreciate the support of the Singapore Urban Redevelopment Authority (URA), the Singapore Land Authority (SLA), the Singapore Land Transport Authority (LTA), and the Housing Development Board (HDB) in providing data and other helpful information. The research presented in this thesis was supported by the National Research Foundation Singapore through the Singapore MIT Alliance for Research and Technology’s Future Urban Mobility IRG research program. Talking of causal effects, I would be remiss to not thank my parents for their loveand support over all the years of my existence. It would not be necessary to use a model to prove that they have had a strongly positive and statistically significant impact on my life. 5 THIS PAGE INTENTIONALLY LEFT BLANK 6 Contents 1 Introduction 15 1.1 Background & Motivation . 15 1.1.1 Costs of vehicle ownership . 16 1.1.2 Attitudes towards vehicle ownership . 17 1.1.3 The “peak car” phenomenon . 18 1.1.4 Behavioral shifts in millennials . 18 1.1.5 Moving towards a shared mobility paradigm . 20 1.2 Research questions . 20 1.3 Thesis organization . 22 2 Literature Review 23 2.1 Conceptual frameworks . 23 2.2 Statistical research methods . 25 2.3 Determinants of vehicle ownership . 28 2.4 Impact of car-sharing systems . 32 2.5 Impact of autonomous vehicles (AVs) . 37 3 Data & Context 39 3.1 Contextual Setting . 39 3.1.1 Private vehicle ownership policies . 40 3.1.2 Private vehicle usage policies . 43 3.2 Data Sources . 44 3.2.1 Household Interview Travel Survey (HITS) . 44 3.2.2 Built environment & Land use . 45 3.2.3 Travel skims . 48 7 4 Using a hybrid approach to predict impacts of new mobility 51 4.1 Introduction . 51 4.2 Literature Review . 52 4.2.1 Machine learning applications in transportation . 53 4.2.2 Econometrics and Machine Learning: Two peas in a pod . 55 4.2.3 The class-imbalance phenomenon . 56 4.2.4 Key takeaways . 58 4.3 Framework . 58 4.3.1 Model construction . 59 4.3.2 Handling low-sample alternatives . 60 4.3.3 Forecasting market shares of new mobility . 61 4.4 Methodology . 63 4.4.1 Econometric models . 63 4.4.2 Machine learning models . 66 4.4.3 Variable specifications . 70 4.4.4 Model assessment metrics . 73 4.4.5 Synthetic sample generation . 76 4.5 Results & Discussion . 78 4.5.1 Model selection . 78 4.5.2 Handling low-sample alternatives . 86 4.5.3 Forecasting new mobility market shares . 88 4.6 Conclusion . 90 5 Examining household dynamics in vehicle availability and use decisions 93 5.1 Introduction . 93 5.2 Literature review . 95 5.2.1 Key takeaways . 97 5.2.2 Research contributions of this study . 98 5.3 Methodology . 98 5.3.1 Vehicle availability . 99 5.3.2 Vehicle use . 100 5.3.3 Direct elasticities . 101 8 5.4 Results & Discussion . 102 5.4.1 Household mobility bundle choice . 102 5.4.2 Major car user in household . 112 5.5 Conclusion . 115 6 Evaluating the impact of car-lite policies on housing-mobility choices 119 6.1 Introduction . 119 6.2 Literature Review . 120 6.2.1 Impact of AVs on private vehicle ownership . 121 6.2.2 Impact of AVs on residential relocation . 122 6.2.3 Research contributions of this study . 123 6.3 Framework . 123 6.3.1 SimMobility: An overview . 123 6.3.2 Behavioral models in SimMobility-Long Term . 125 6.4 Methodology . 127 6.4.1 Car-lite Policy . 127 6.4.2 Study Areas . 128 6.4.3 Scenario Design . 130 6.4.4 Sequential simulation framework for car-lite policy analysis . 132 6.5 Results & Discussion . 134 6.5.1 Stochasticity of simulated market shares . 134 6.5.2 Computational performance . 135 6.5.3 Temporal variation in the study areas . 136 6.5.4 Behavioral variations across movers . 138 6.5.5 Vehicle availability transitions . 140 6.6 Conclusion . 144 7 Conclusion 147 7.1 Key findings . 147 7.1.1 Predicting the impact of new mobility . 147 7.1.2 Household dynamics in vehicle availability and use decisions . 148 7.1.3 The impact of car-lite policies on mobility bundle choices . 150 7.2 Limitations & future research . 151 9 7.3 Concluding remarks . 152 A Data preparation 153 A.1 Income imputation for HITS individuals . ..
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