Supply and Demand Management in Ride-Sourcing Markets
Autor: | Jintao Ke, Hai Yang, Hai Wang, Yafeng Yin |
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EAN: | 9780443189388 |
eBook Format: | ePUB/PDF |
Sprache: | Englisch |
Produktart: | eBook |
Veröffentlichungsdatum: | 30.04.2023 |
Kategorie: | |
Schlagworte: | Background traffic Bundled mode Calibration Cobb?douglas-type matching function Commission regulation Complement Demand regulation Demand splitting Drivers' income regulation Externality Feeders First-best solution First-come-first-serve |
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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. - Serves as a foundation for subsequent research studies that investigate ride-sourcing services through mathematical modeling - Offers valuable managerial insights for ride-sourcing platforms and helps them develop more efficient and effective operating strategies - Assists the governments or social planners in designing appropriate regulatory schemes to achieve more sustainable and societally beneficial market outcomes
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.
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.