How machine learning informs ride-hailing services: A survey

IF 12.5 Q1 TRANSPORTATION
Yang Liu , Ruo Jia , Jieping Ye , Xiaobo Qu
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引用次数: 27

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.

机器学习如何影响网约车服务:一项调查
近年来,网约车服务已成为城市交通系统的重要组成部分,它不仅为居民的出行活动提供了极大的便利,而且塑造了新的出行行为,使城市出行模式多样化。本研究对基于机器学习的按需叫车服务方法进行了全面回顾。首先强调了按需网约车服务在城市交通时空动态中的重要性,基于机器学习的宏观网约车研究展示了其在指导城市智能交通系统的设计、规划、运营和控制方面的价值。然后,从个人出行模式的角度对出行行为的研究进行了总结,包括拼车行为和模式选择行为。此外,对网约车系统中最重要的组成部分——订单匹配和车辆调度策略进行了收集和总结。最后,讨论了网约车服务的一些关键挑战和机遇。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
15.20
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