The Short-Term Passenger Flow Prediction Method of Urban Rail Transit Based on CNN-LSTM with Attention Mechanism

Abstract

This paper studies the short-term passenger flow prediction of urban rail transit for optimally adjusting the real-time departure of rail trains. Aiming at the problem that the traditional deep learning model does not consider the spatial-temporal information enough, the short-term passenger flow prediction model of urban rail transit based on CNN-LSTM with attention mechanism is proposed. Firstly, the stations are divided into seven categories according to the significant difference of daily passenger flow in urban rail stations so as to further analyze the distribution pattern of daily inbound and outbound passenger flow in different categories of stations; secondly, the short sequence feature abstraction ability of CNN is used to extract the spatial characteristics of historical passenger flow in each time period in different categories of stations; finally, the attention mechanism is used to assign different weights to the extracted characteristic information, and the temporal characteristic information is obtained from the LSTM comprehensive short-term sequence to realize the short-term passenger flow prediction of urban rail transit. Experiments show that the prediction model has the encouraging prediction performance and accuracy.