Genetic Algorithm Based Dynamic Collaborative Optimization Method for Train Dispatching and Passenger Flow Guidance

Abstract

Regional rail transit, formed by the economic integration needs of urban agglomerations, is a comprehensive rail transit system with different kinds of transportation systems. Nowadays, although different kinds of rail transit systems are developing rapidly and the facilities are comprehensive, there is a lack of effective dynamic coordination among the various standards and subsystems. Consequently, the regional rail transit network cannot meet higher overall transport capacity and safety requirements. To solve this problem, this paper proposes a dynamic collaborative optimization method for regional rail transit based on passenger route guidance and train scheduling. This method takes the minimization of the maximum peak value of the road network dynamic risk as the optimization objective to establish a mathematical optimization model. The rail transit road network in the Chengdu–Chongqing area is taken as an example to establish a road network simulation model. Through the combination of the simulation model and intelligent optimization algorithms, the entire multi-standard rail transit network is dynamically optimized for train dispatching and passenger flow guidance. Finally, the effectiveness of the method is verified by the simulation of the actual rail transit network.