Reordering and Driving Strategy to Resolve Train Conflict Based on Cooperative Game Theory

Abstract

According to vehicle-centralized train control design, instead of traditional interlocking equipment's unified management of rail resources, train can control resources based on plan autonomously, which asks for the capability of trains to detect and resolve the potential requisition conflict caused by multi-train decentralized control. This paper proposes a realtime autonomous conflict detection and resolution (rtACDR) model based on cooperative game theory. Firstly, the occupancy time of trains at sections is predicted by using blocking time theory, a criterion for the existence of conflict is also described. When potential conflict is detected, factors considered during dispatching and driving are weighted and described as an individual characteristic function, which represents relevant train's gaming revenue. At last, based on the Shapely theory, train's reordering at conflict area is transformed into the optimization problem of cooperative alliance's revenue, and solved by corresponding driving strategy's generation. The computational tests are performed on rail network of Cao Qiao Station in Beijing with various perturbations. Based on the detailed analysis of trains' driving strategies generated by model and the comparison of model and traditional dispatching strategy's performances, feasibility of proposed model is estimated preliminarily.