This paper investigates the impact of ride-hailing services,particularly the integration of autonomous vehicles(AVs),on urban transportation systems.The paper discusses the challenges faced by ride-hailing platforms i...This paper investigates the impact of ride-hailing services,particularly the integration of autonomous vehicles(AVs),on urban transportation systems.The paper discusses the challenges faced by ride-hailing platforms in managing a fleet of both AVs and conventional vehicles(CVs)within the spatial network of a city.It examines the approaches and methods used to manage demand allocation for AVs and CVs,considering the strategic behavior of human drivers and considerations for possible regulations.Using mean-field game theory,this paper proposes efficient strategies for managing fleet operations along with those of traffic optimization and service efficiency.The analysis highlights the complexities of integrating AVs into existing transportation systems and advocates for the development of robust theoretical traffic models for regulatory decisions and improved urban mobility.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.72271068)in part by the Shenzhen Science and Technology Program(Grant No.KCXST20221021111404010).
文摘This paper investigates the impact of ride-hailing services,particularly the integration of autonomous vehicles(AVs),on urban transportation systems.The paper discusses the challenges faced by ride-hailing platforms in managing a fleet of both AVs and conventional vehicles(CVs)within the spatial network of a city.It examines the approaches and methods used to manage demand allocation for AVs and CVs,considering the strategic behavior of human drivers and considerations for possible regulations.Using mean-field game theory,this paper proposes efficient strategies for managing fleet operations along with those of traffic optimization and service efficiency.The analysis highlights the complexities of integrating AVs into existing transportation systems and advocates for the development of robust theoretical traffic models for regulatory decisions and improved urban mobility.
