Urban Rail Transit Network Planning Based on Particle Swarm Optimization Algorithm

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