Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers
Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers
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DOI：
10.1016/j.trpro.2019.05.006
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
C. Lei;Zhoutong Jiang;Y. Ouyang
中科院分区：
文献类型：
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作者：
C. Lei;Zhoutong Jiang;Y. Ouyang
快速发展的按需拼车服务，包括那些具有自动驾驶技术的服务，有望彻底改变移动性的交付。然而，车辆供应和出行需求的时空分布之间的严重不平衡构成了一个紧迫的挑战。提出了一个多阶段博弈模型，解决了司机/车辆完全合规的按需拼车系统中的动态定价和怠速车辆调度问题。具有平衡约束的动态数学规划（MPEC）被公式化以捕获移动性服务提供商的相互依赖的决策过程（例如，关于车辆分配）和旅行者（例如，关于乘车共享和旅行路径选项）。基于近似动态规划（ADP）的算法，与定制的子程序求解MPEC，开发解决整体问题。数值实验表明，所提出的动态定价和车辆调度策略可以帮助拼车服务提供商实现更好的系统性能（与近视的政策相比），同时面临的空间和时间变化的拼车需求。
Rapidly advancing on-demand ridesharing services, including those with self-driving technologies, hold the promise to revolutionize delivery of mobility. Yet, significant imbalance between spatiotemporal distributions of vehicle supply and travel demand poses a pressing challenge. This paper proposes a multi-period game-theoretic model that addresses dynamic pricing and idling vehicle dispatching problems in the on-demand ridesharing systems with fully compliant drivers/vehicles. A dynamic mathematical program with equilibrium constraints (MPEC) is formulated to capture the interdependent decision-making processes of the mobility service provider (e.g., regarding vehicle allocation) and travelers (e.g., regarding ride-sharing and travel path options). An algorithm based on approximate dynamic programming (ADP), with customized subroutines for solving the MPEC, is developed to solve the overall problem. It is shown with numerical experiments that the proposed dynamic pricing and vehicle dispatching strategy can help ridesharing service providers achieve better system performance (as compared with myopic policies) while facing spatial and temporal variations in ridesharing demand.