A generalized fluid model of ride-hailing systems
A generalized fluid model of ride-hailing systems
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DOI：
10.1016/j.trb.2021.05.014
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Zhengtian Xu;Yafeng Yin;X. Chao;Hongtu Zhu;Jieping Ye
中科院分区：
文献类型：
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作者：
Zhengtian Xu;Yafeng Yin;X. Chao;Hongtu Zhu;Jieping Ye
本文提出了一个宏观流体建模框架，以协助运营大规模按需应用程序的乘车系统的平台的战略决策。该框架捕获了乘车系统的时空特征，并且在表示平台正在实现的控制策略方面是灵活的。因此，它可以分析具有可观察市场反应的大规模乘车系统，并促进控制策略的优化。作为该框架的一个示范，我们定制了一个叫车系统由滴滴出行在中国的一个大城市，并进行了实证研究的系统。在此过程中，我们使用经验数据以高度的粒度校准模型，然后研究最佳定价策略，以实现平台的各种管理目标。据我们所知，该模型是同类模型中的第一个，它提供了一种易于处理的方法来支持大型乘车系统的分析和优化。该实证研究还为研究网约车服务的各种运营策略提供了首批测试平台之一。
This paper proposes a macroscopic fluid modeling framework to assist with strategic decision making of a platform for operating a large-scale on-demand app-based ride-hailing system. The framework captures the spatiotemporal characteristics of a ride-hailing system, and is flexible in representing control policies that a platform is implementing. It thus enables the analysis of a large-scale ride-hailing system with observable market responses and facilitates the optimization of the control polices. As a demonstration of the proposed framework, we customize it for a ride-hailing system operated by Didi Chuxing in a large city of China, and conduct an empirical study of the system. In doing so, we calibrate the model with a high degree of granularity using empirical data, and then investigate optimal pricing strategies to achieve various managerial objectives of the platform. To our best knowledge, the proposed model is the first of its kind that offers a tractable way to support the analysis and optimization of large-scale ride-hailing systems. The empirical study also provides one of the first testbeds for examining various operations strategies for ride-hailing services.