关键词: 交通工程, 城市轨道交通, 进站量预测, ARIMA模型, 短期预测

Abstract: Passenger flow prediction is the foundation of the passenger transport organization, and the prediction results can provide the basis for decision making of operation management and emergency disposal. Aiming at the passenger flow prediction of urban rail transit, an autoregressive integral moving average ARIMA model was established for short-term prediction of station inflows, which was based on analyzing the periodic fluctuation law and change trend of the passenger flow in rail transit stations. Taking Beijing subway as an example, an empirical analysis was carried out. The seasonal ARIMA model meeting the requirements was used to carry out the short-term prediction of passenger flow, the actual passenger flow of Dongzhimen station was selected for model parameter calibration, and the passenger flow prediction of typical stations (terminal station, intermediate station, transfer station and interchange station) in the road network was carried out. The research results show that the average prediction error of ARIMA model is only 4%, which has high prediction accuracy and verifies the correctness of the prediction method.

Key words: traffic engineering, urban rail transit, prediction of inflows, ARIMA model, short-term prediction