Behavior Prediction and Its Design for Safe Departure Intervals Based on Huang Yan-Pei Thought

Rail transit passenger flow is affected by many factors. In order to get a more suitable departure interval, the factors of passenger flow changes must be fully considered. Based on Huang Yan-Pei Thought, this paper analyzes the influencing factors of riding behavior, and uses neural network model to predict the behavior of potential travelers taking rail transit. At the same time, through the analysis of the spatial and temporal distribution of rail transit passenger flow, a multi-objective planning model is established based on the indexes of vehicle full load and passenger comfort, which is helpful for the reasonable arrangement of urban rail transit capacity.