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English
Elsevier - Health Sciences Division
12 May 2023
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.

By:   , , , , , , , , , , ,
Imprint:   Elsevier - Health Sciences Division
Country of Publication:   United States
Dimensions:   Height: 229mm,  Width: 152mm, 
Weight:   450g
ISBN:   9780443189371
ISBN 10:   0443189374
Pages:   404
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
1 Introduction 2 Fundamentals of Market Equilibrium Analyses 3 Regulations 4 Ride-pooling Services 5 Congestion Externalities 6 Ride-Pooling in the Presence of Traffic Congestion 7 Revisiting Government Regulations 8 Competition Between Platforms 9 Third-party Integrator 10 Ride-sourcing and Public Transit 11 Matching Time Interval and Radius 12 Labor Supply Analysis 13 Conclusion and Future Work

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. 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. 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. 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.

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