Passenger flow prediction at rail transit station entrances and exits: A case study of Beijing

Abstract

Accurate prediction of passenger flow at station entrances and exits is a hot topic in rail transit passenger flow prediction work, and provides important support for the refined and humanized design of stations. This paper comprehensively considers factors such as land use around the station, traffic connection conditions, station attributes and attractiveness, and constructs a two-layer passenger flow prediction model, including traffic zone layer passenger flow prediction based on multivariate nonlinear regression and entrance and exit layer passenger flow prediction based on the CRITIC method. Based on available data, the model first predicts the total passenger flow at all entrances and exits in the traffic zone, and then distributes the total passenger flow to each entrance and exit of the traffic zone. Different types of rail transit stations were randomly selected to verify the effectiveness of the model. The results show that the error between the model prediction value and the actual value of the daily entrance and exit volume is within , and the average error is , which has a high prediction accuracy.