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Review Article | Open Access

How machine learning informs ride-hailing services: A survey

Yang Liua,b(), Ruo Jiaa, Jieping Yec, Xiaobo Qub()
Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden
State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, United States
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Abstract

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents' travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed.

Keywords

Ride-hailing servicesMachine learningTraffic dynamicsIndividual mobility patterns

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Communications in Transportation Research
Volume 2 Issue 1,
2022
Article number: 100075
DOI: 10.1016/j.commtr.2022.100075
Cite this article:
Liu Y, Jia R, Ye J, et al. How machine learning informs ride-hailing services: A survey. Communications in Transportation Research, 2022, 2(1): 100075. https://doi.org/10.1016/j.commtr.2022.100075
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