A Machine Learning Based Method for Short-term Urban Rail Transit Passenger Flow Prediction

Abstract

In order to improve the accuracy of urban rail transit passenger flow prediction, a machine learning based method for short-term urban rail transit passenger flow prediction, which efficiently fuses the date-related characteristics, and the autocorrelation of time series changes of passenger flow is proposed. By analyzing the historical passenger flow of urban rail transit stations, the correlation of passenger flow time series and the date-related characteristics of multiple scales composed of date stage, week and work-and-rest day are obtained. With time nodes such as legal holidays, summer vacation and winter vacation, the date within a year can be divided into several time stages, and the stage coefficient is introduced to quantify it to get the stage characteristic. The time series of station historical passenger flow is combined with multiscale date-related characteristics to establish the back propagation neural network prediction model, which realizes the short-term prediction of the inbound passenger flow of urban rail transit stations. The proposed method is validated and compared with the long-short term memory neural network (LSTMNN) model. The experimental result shows that the proposed method is more stable and accurate than the LSTMNN in general, and it has good applicability for different stations.