A multi-functional simulation platform for on-demand ride service operations  

作　　者：Siyuan Feng Taijie Chen Yuhao Zhang Jintao Ke Zhengfei Zheng Hai Yang 

机构地区：[1]Department of Logistics and Maritime Studies,The Hong Kong Polytechnic University,Hong Kong,999077,China [2]Department of Civil Engineering,The University of Hong Kong,Hong Kong,999077,China [3]Alibaba Taotian Group,Hangzhou,310000,China [4]Department of Civil and Environmental Engineering,The Hong Kong University of Science and Technology,Hong Kong,999077,China

出　　处：《Communications in Transportation Research》2024年第1期320-331,共12页交通研究通讯(英文)

基　　金：Development of simulator and visualization tools were supported by a smart traffic fund(PSRI/29/2201/PR);It was also partially supported by the Research Grants Council of the Hong Kong Special Administrative Region,China under a Theme-based Research Scheme(TRS)T41-603/20R and general research grants(GRF)HKU15209121 and PolyU15207424.

摘　　要：On-demand ride services or ride-sourcing services have been experiencing fast development and steadily reshaping the way people travel in the past decade.Various optimization algorithms,including reinforcement learning approaches,have been developed to help ride-sourcing platforms design better operational strategies to achieve higher efficiency.However,due to cost and reliability issues,it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride-sourcing platforms.Acting as a proper test bed,a simulation platform for ride-sourcing systems will thus be essential for both researchers and industrial practitioners.While previous studies have established simulators for their tasks,they lack a fair and public platform for comparing the models/algorithms proposed by different researchers.In addition,the existing simulators still face many challenges,ranging from their closeness to real environments of ride-sourcing systems to the completeness of tasks they can implement.To address the challenges,we propose a novel simulation platform for ride-sourcing systems on real transportation networks.It provides a few accessible portals to train and test various optimization algorithms,especially reinforcement learning algorithms,for a variety of tasks,including on-demand matching,idle vehicle repositioning,and dynamic pricing.Evaluated on real-world data-based experiments,the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations.

关 键 词：Ride-sourcing service Simulation Reinforcement learning On-demand matching Idle vehicle repositioning 

分 类 号：TN9[电子电信—信息与通信工程]