摘要
为解决多服务模式的网约车市场中所存在的模式间供需时空不匹配的问题,打破模式间派单壁垒,减少车辆无效巡游,提升乘客体验,提出一种基于强化学习的跨模式派单算法。首先,设计跨模式派单的运行规则和相应的仿真环境为算法的训练提供环境支持。其次,针对派单过程中乘客需求动态变化的特点,设计两阶段派单流程。在第一阶段,根据车辆与需求的时空特征确定虚拟派单请求;在第二阶段,采用k最近邻算法对可行派单请求进行细化,选择与虚拟派单请求最为相似的请求作为最终的派单方案。实验表明,与基线方法相比,所提出的跨模式派单算法提升了基础服务模式中3.1%的请求响应率,同时减少了高级服务模式中车辆的空驶时间,实现了更高的经济效益与服务水平。
To meet personalized travel demand,ride-sourcing platforms provide differentiated service modes for travelers.These service modes are usually operated independently,and because of the heterogeneity of travel demand,the fragmented ride-sourcing services struggle with imbalances between supply and demand within each sub-market.For that,cross-mode dispatching can be adopted to reduce the vehicle idle time and improve passengers’experience.However,it may undermine the efficiency of the premium mode,such as increased matching failures.To consider the long-term impact of cross-mode dispatching,the dispatching problem of multi-service modes is modeled by the Markov decision process.A multi-service mode dispatching framework is proposed,comprised of a simulator and a two-stage reinforcement learning algorithm.The simulator provides environment support while avoiding matching failures of premium mode caused by cross-mode dispatching.The two-stage reinforcement learning algorithm refines requests based on the k-nearest neighbor algorithm to solve the problem of the dynamic change of the candidate request set.Experiments show that the framework fulfills 3.1%additional basic requests without the decreasing of fulfilled premium requests,by cross-mode dispatching,demonstrating the feasibility of the cross-mode dispatching and the performance of the framework.Sensitivity analyses are designed to test the robustness of the framework under different scenarios.
作者
王印权
吴建军
孙会君
张宇丰
吕莹
WANG Yin-quan;WU Jian-jun;SUN Hui-jun;ZHANG Yu-feng;LYU Ying(State Key Laboratory of Rail Traffic Control and Safety(Beijing Jiaotong University),Beijing 100044,China;Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport of Ministry of Transport(Beijing Jiaotong University),Beijing 100044,China;Institute of Transportation System Science and Engineering,Beijing Jiaotong University,Beijing 100044,China)
基金
Projects(91846202, 71621001, 71890972, 71890970) supported by the National Natural Science Foundation of China。
关键词
网约出行
多服务模式
跨模式派单
强化学习
新兴交通系统
ride-sourcing
multi-service modes
cross-mode dispatching
reinforcement learning
emerging transportation systems
