Incremental path planning: Reservation system in V2X environment

• Published in: Physica A Vol. 623; p. 128914
• Main Authors: Fu, Daocheng, Cai, Pinlong, Lin, Yilun, Mao, Song, Wen, Licheng, Li, Yikang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2023
• Subjects: Connected vehicles; Path planning; Spatiotemporal resource; Traffic flow modeling; Connected vehicles; Path planning; Traffic flow modeling; Spatiotemporal resource
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2023.128914

Previous work assumes that traffic flow evolves over time, and paths are planned based on traffic estimation and prediction, although such an assumption is simple and efficient for single vehicles, there is a self-contradictory problem when planning paths for multiple vehicles. If multiple vehicles choose the same uncongested road based on traffic forecasts, this may lead to congestion on that road, which in turn affects the efficiency of path planning. The V2X environment offers the possibility to solve the above problems. In this paper, a new perspective is developed where the traffic flow is considered as a superposition of spatiotemporal paths. From this perspective, a novel method is proposed in which the paths are planned incrementally according to the remaining spatiotemporal resources and the travel demands, which is referred to as Incremental Path Planning (IPP). IPP plans the paths of vehicles according to a predefined priority, after a vehicle’s path is planned, the occupancy of spatiotemporal resources is updated, and the remaining resources are then passed to the next vehicle for path planning. In IPP, an incrementally updated traffic model is proposed to obtain the traffic state. Based on this model, a time-dependent path search algorithm is proposed to reduce vehicle travel times. Simulation experiments based on real data sets have demonstrated the excellent performance of IPP in both improving traffic efficiency and driving experience. •Present a new perspective on traffic flow modeling.•Solve multi-vehicle path planning interdependent problem by an incremental way.•Adapt a time-dependent path planning algorithm to improve the traffic efficiency.