Prediction of Passenger Flow in Urban Rail Transit Based on Big Data Analysis and Deep Learning

Abstract

Passenger flow prediction is the key to operation efficiency and safety of urban rail transit (URT). This paper combines the deep learning (DL) theory and support vector machine (SVM) into the DL-SVM model for URT passenger flow prediction. Firstly, the deep belief network (DBN) was adopted to extract the features and inherent variation of passenger flow data. On this basis, an SVM regression model was constructed to predict passenger flow. Then, the proposed model was compared with three shallow prediction models through experiments on Qingdao Metro. The results show that the DL-SVM outperformed the other models in accuracy and stability. The research findings shed important new light on the passenger flow prediction in the URT system.