Urban Rail Transit Network Planning Based on Particle Swarm Optimization Algorithm

In order to solve the problem that urban rail transit network is affected by a large number of signals, resulting in poor control effect, and improve living comfort residents near transit, study on planning based particle swarm optimization algorithm proposed. The learning factor dynamically adjusted according inertia weight parameters, parameters are selected combination with setting maximum velocity parameters. individual optimal using dominant relationship between particles, completed combining selection requirements global particle. We design v2x communication implementation scheme, obtain traffic flow information build signal input output model algorithm, feedback signal, determine scale network, so as complete planning. experimental results show proposed method can utilization rate planning, effectively change amplitude, reduce repetition