Overview
- Explains the benefits of taking a reinforcement learning approach to ridesharing optimization problems
- Analyzes a number of specific works that cover the optimization of ridesharing platforms using reinforcement learning
- Highlights the major challenges and opportunities that are crucial for advancing reinforcement learning for ridesharing
Part of the book series: Synthesis Lectures on Learning, Networks, and Algorithms (SLLNA)
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About this book
Keywords
Table of contents (12 chapters)
Authors and Affiliations
About the authors
Zhiwei (Tony) Qin, Ph.D., is a Principal Scientist at Lyft Rideshare Labs. He earned his Ph.D. from Columbia University. His research interests include operations research, machine learning, deep learning, and big data analytics, with applications in smart transportation and E-commerce.
Xiaocheng Tang, Ph.D., is an AI Research Scientist at Meta. He earned his Ph.D. from Lehigh University. His research interests lie at the intersection of machine learning, reinforcement learning, and optimization.
Qingyang Li, Ph.D., is a Senior Engineering Manager at DiDi Autonomous Driving. He earned his Ph.D. from Arizona State University. His research interests include machine learning, deep learning, reinforcement learning, and computer vision.
Jieping Ye, Ph.D. is affiliated with the Alibaba Group. He earned his Ph.D. from the University of Minnesota. His research interests include machine learning, data mining, artificial intelligence, transportation, and biomedical informatics.
Hongtu Zhu, Ph.D. is a Professor in the Department of Biostatics at The University of North Carolina at Chapel Hill. He earned his Ph.D. at The Chinese University of Hong Kong. His research interests include medical imaging analysis, imaging genetics, artificial intelligence, statistics, biostatics, and computational neuroscience.
Bibliographic Information
Book Title: Reinforcement Learning in the Ridesharing Marketplace
Authors: Zhiwei (Tony) Qin, Xiaocheng Tang, Qingyang Li, Hongtu Zhu, Jieping Ye
Series Title: Synthesis Lectures on Learning, Networks, and Algorithms
DOI: https://doi.org/10.1007/978-3-031-59640-7
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
Hardcover ISBN: 978-3-031-59639-1Published: 04 August 2024
Softcover ISBN: 978-3-031-59642-1Due: 18 August 2025
eBook ISBN: 978-3-031-59640-7Published: 03 August 2024
Series ISSN: 2690-4306
Series E-ISSN: 2690-4314
Edition Number: 1
Number of Pages: VII, 133
Number of Illustrations: 2 b/w illustrations, 26 illustrations in colour
Topics: Machine Learning, Computer Applications, Artificial Intelligence, Algorithms, Computational Mathematics and Numerical Analysis, Business Information Systems