Reinforcement Learning in the Ridesharing Marketplace

Zhiwei (Tony) Qin, Xiaocheng Tang, Qingyang Li, Hongtu Zhu, Jieping Ye (Autoren)

Buch | Hardcover

VII, 133 Seiten

2024 | 2025
Springer International Publishing (Verlag)
978-3-031-59639-1 (ISBN)

Artikel merken

This book provides a comprehensive overview of reinforcement learning for ridesharing applications. The authors first lay out the fundamentals of the ridesharing system architectures and review the basics of reinforcement learning, including the major applicable algorithms. The book describes the research problems associated with the various aspects of a ridesharing system and discusses the existing reinforcement learning approaches for solving them. The authors survey the existing research on each problem, and then examine specific case studies. The book also includes a review of two of methods closely related to reinforcement learning: approximate dynamic programming and model-predictive control.

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.

Introduction.- Ridesharing.- Reinforcement Learning Prime.- Pricing & Incentives.- Online Matching.- Vehicle Repositioning.- Routing.- Ride-pooling.- Related Methods.- Open Resources.- Challenges and Opportunities.- Closing Remarks.

Erscheinungsdatum	06.08.2024
Reihe/Serie	Synthesis Lectures on Learning, Networks, and Algorithms
Zusatzinfo	VII, 133 p. 28 illus., 26 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	168 x 240 mm
Themenwelt	Mathematik / Informatik ► Informatik
Schlagworte	Dynamic Pricing • online matching • Reinforcement Learning • Ridesharing • Routing • Vehicle Repositioning
ISBN-10	3-031-59639-0 / 3031596390
ISBN-13	978-3-031-59639-1 / 9783031596391
Zustand	Neuware