Supply and Demand Management in Ride-Sourcing Markets

1st Edition - April 30, 2023
Authors: Jintao Ke, Hai Yang, Hai Wang, Yafeng Yin
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 8 9 3 7 - 1
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 8 9 3 8 - 8

Supply and Demand Management in Ride-Sourcing Markets offers a fundamental modeling framework for characterizing ride-sourcing markets by spelling out the complex relations… Read more

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Supply and Demand Management in Ride-Sourcing Markets offers a fundamental modeling framework for characterizing ride-sourcing markets by spelling out the complex relationships among key endogenous and exogenous variables in the markets. This book establishes several economic models that can approximate matching frictions between drivers and passengers, describes the equilibrium state of ride-sourcing markets, and more. Based on these models, the book develops an optimum strategy (in terms of trip fare, wage and/or matching) that maximizes platform profit. While the best social optimum solution (for maximizing the social welfare) is generally unsustainable, this book provides options governments can use to encourage second-best solutions.

In addition, the book's authors establish models to analyze ride-pooling services, with traffic congestion externalities incorporated into models to see how both new platforms and government designs can optimize operating strategies in response to the level of traffic congestion.

Cover image
Title page
Table of Contents
Copyright
Contributors
About the authors
Preface
Chapter 1. Introduction of ride-sourcing markets
1.1. Background
1.2. Theoretical developments
1.3. Outline of this book
Chapter 2. Fundamentals of ride-sourcing market equilibrium analyses
2.1. Introduction
2.2. Matching frictions (inductive approaches)
2.3. Matching frictions (deductive approaches)
2.4. Market measures
2.5. Discussion
Glossary of notation
Chapter 3. Calibration and validation of matching functions for ride-sourcing markets
3.1. Introduction
3.2. Matching functions and market metrics
3.3. Experimental settings
3.4. Analysis of experimental results
3.5. Summary
3.6. Discussion and conclusion
Appendix 3.A
Chapter 4. Government regulations for ride-sourcing services
4.1. Properties of the pareto-efficient solutions
4.2. An alternative method to obtain and analyse pareto-efficient solutions
4.3. Regulations
4.4. Discussion and conclusion
Chapter 5. Equilibrium analysis for ride-pooling services
5.1. Introduction
5.2. Pool-matching schemes
5.3. Equilibrium analyses
5.4. Market measures
5.5. Numerical illustrations
5.6. Conclusion
Chapter 6. Ride-pooling services and traffic congestion
6.1. Introduction
6.2. Equilibrium analyses
6.3. Market measures
6.4. Conclusion
Chapter 7. Equilibrium analysis for ride-pooling services in the presence of traffic congestion
7.1. Introduction
7.2. Equilibrium analyses
7.3. Market measures
7.4. Numerical studies
7.5. Conclusion and remarks
Chapter 8. Revisiting government regulations for ride-sourcing services under traffic congestion
8.1. Introduction
8.2. Theoretical analyses
8.3. Numerical studies
8.4. Conclusion
Chapter 9. Third-party platform integration in ride-sourcing markets
9.1. Background
9.2. Market equilibrium and optimal strategies
9.3. Evaluation of the performance of platform integration
9.4. Numerical studies
9.5. Conclusion
Appendix 9.A. Proof of Lemma 9-1
Appendix 9.B. Proof of theorem 9-1
Appendix 9.C. Proof of Lemma 9-2
Appendix 9.D. Proof of theorem 9-2
Appendix 9.E. Proof of Lemma 9-3
Appendix 9.F. Proof of Lemma 9-4
Appendix 9.G. Proof of theorem 9-3
Appendix 9.H. Proof of Lemma 9-5
Appendix 9.I. Proof of theorem 9-4
Appendix 9.J. General matching function
Chapter 10. Ride-sourcing services and public transit
10.1. Background
10.2. Model description
10.3. Optimal strategy design
10.4. Numerical case study
10.5. Conclusion
Chapter 11. Optimization of matching-time interval and matching radius in ride-sourcing markets
11.1. Research problem
11.2. Modelling and optimising the matching process
11.3. Model properties in imbalanced scenarios
11.4. Numerical studies
11.5. Conclusion
Chapter 12. Labour supply analysis of ride-sourcing services
12.1. Background
12.2. Related literature
12.3. Labour supply model
12.4. Modelling endogeneity of income rates and self-selected participation in the labour force
12.5. Research design
12.6. Results and discussion
12.7. Conclusion
Chapter 13. Some empirical laws of ride-pooling services
13.1. Introduction
13.2. Literature review
13.3. Optimisation framework and data descriptions
13.4. Empirical laws
13.5. Conclusions
Appendices
Glossary of notation
Chapter 14. Summary
Glossary of abbreviations
Index

Jintao Ke

Dr. Jintao Ke received his B.S. degree (2016) in civil engineering from Zhejiang University, and his Ph.D. degree (2020) in Civil and Environment Engineering from Hong Kong University of Science and Technology. He is now an Assistant Professor at the University of Hong Kong. His research interests include smart transportation, smart city, urban computing, shared mobility, machine learning in transportation, operational management for transportation studies, etc. He has published over 20 SCI/SSCI indexed research papers in in top-tier transportation journals, such as Transportation Research Part A/B/C/E and IEEE Transactions on Intelligence Transportation System. He serves as an Advisory Board Member of Transportation Research Part C: Emerging Technologies, and referees for a few top transportation journals.

Affiliations and expertise

Assistant Professor, Department of Civil Engineering, University of Hong Kong, Hong Kong

Hai Yang

Prof. Hai Yang is currently a Chair Professor in the Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology. He received his Bachelor degree from Wuhan University, China, and PhD from Kyoto University, Japan. Prof. Yang is internationally known as an active scholar in the field of transportation, with nearly 300 papers published in SCI/SSCI indexed journals. He has received a number of national and international awards, including JSCE Outstanding Paper Award (1991); Distinguished Overseas Young Investigator Award from the National Natural Science Foundation of China (2004); National Natural Science Award bestowed by the State Council of China (2011); HKUST School of Engineering Research Excellence Awards (2012); Frank M. Masters Transportation Engineering Award, American Society of Civil Engineers (ASCE) (2020); and 2021 ASCE Francis C. Turner Award. He is an elected permanent member of the International Advisory Committee of the International Symposium on Transportation and Traffic Theory.

Affiliations and expertise

Chair Professor, Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong

Hai Wang

Dr. Wang is an Associate Professor in the School of Computing and Information Systems at Singapore Management University and a visiting faculty at the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. He received his Bachelors degree from Tsinghua University and his PhD from MIT. His research focuses on methodologies of Analytics and Optimization, data-driven decision-making, and machine learning, and their applications in smart cities, including innovative transportation, advanced logistics, modern e-commerce, and intelligent healthcare. He serves as Associate Editor for Transportation Science and Service Science, Special Issue Editor for Transportation Research Part B, Part C, and Service Science, and Editorial Board Member for Transportation Research Part C and Part E. Dr. Wang was selected as Chan Wu & Yunying Rising Star Fellow in Transportation, received a Lee Kong Chian Research Excellence Award, was nominated for MIT’s Top Graduate Teaching Award, and won the Excellent Teaching Award at SMU. During his PhD at MIT, he also served as the Co-President of the MIT Chinese Students' & Scholars Association and a Chair of MIT-China Innovation and Entrepreneurship Forum.

Affiliations and expertise

Associate Professor, School of Computing and Information Systems, Singapore Management University, Singapore

Yafeng Yin

Dr. Yafeng Yin is a Professor at Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor. He works in the area of transportation systems analysis and modeling and has published more than 100 refereed papers in leading academic journals. Dr. Yin is the Editor-in-Chief of Transportation Research Part C: Emerging Technologies. He is also an Associate Editor of Transportation Science and serves on the editorial boards for another four transportation journals such as Transportation Research Part B: Methodological. He is a member of Transportation Network Modeling, Transportation Economics, and International Cooperation Committees of Transportation Research Board. Between 2008 and 2018, Dr. Yin served on the Board of Directors for Chinese Overseas Transportation Association (COTA) and was its President between 2014 and 2016. Dr. Yin received his Ph.D. from the University of Tokyo, Japan in 2002, his master’s and bachelor’s degrees from Tsinghua University, Beijing, China in 1996 and 1994 respectively.

Affiliations and expertise

Professor at Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor.