Intelligent scheduling method for congestion prevention of urban rail vehicles in time-varying environment

Urban rail vehicle congestion restricts the smoothness and operation capability of rail transit lines. This paper proposes an intelligent congestion prevention scheduling method for urban rail vehicles based on multi terminal and multi line transfer in a time-varying environment. Firstly, based on SARIMA model and SVM model, the target model of urban rail vehicle congestion prevention scheduling in time-varying environment is established. Secondly, with the optimal spacing of stations as the optimization object and the allocation of urban rail vehicle dispatching stations as the input bar, the global optimal objective function of rail vehicle anti congestion dispatching is obtained, and the optimization objective is determined. Then, according to the evacuation conditions of time and traffic capacity, taking the passenger flow distribution conditions of urban rail vehicle dispatching stations as the input conditions, and based on the passenger flow distribution law, the passenger flow prediction model of urban rail transit under the condition of multi station and multi line is constructed. Finally, based on the passenger flow data, the passenger flow change characteristics of future stations are predicted to achieve the anti blocking dispatching of urban rail vehicles. The simulation test results show that in a time-varying environment, this method can effectively alleviate traffic congestion, optimize the selection of rail transit routes, and improve passenger travel efficiency at different starting and ending points.