Passenger Flow Prediction of Urban Rail Transit Based on Deep Learning Methods

Zhi Xiong,Dunjiang Song,Quanyi Huang,Shaobo Zhong,Jianchun Zheng

doi:10.3390/smartcities2030023

Abstract

The rapid development of urban rail transit brings high efficiency and convenience. At the same time, the increasing passenger flow also remarkably increases the risk of emergencies such as passenger stampedes. The accurate and real-time prediction of dynamic passenger flow is of great significance to the daily operation safety management, emergency prevention, and dispatch of urban rail transit systems. Two deep learning neural networks, a long short-term memory neural network (LSTM NN) and a convolutional neural network (CNN), were used to predict an urban rail transit passenger flow time series and spatiotemporal series, respectively. The experiments were carried out through the passenger flow of Beijing metro stations and lines, and the prediction results of the deep learning methods were compared with several traditional linear models including autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), and space–time autoregressive integrated moving average (STARIMA). It was shown that the LSTM NN and CNN could better capture the time or spatiotemporal features of the urban rail transit passenger flow and obtain accurate results for the long-term and short-term prediction of passenger flow. The deep learning methods also have strong data adaptability and robustness, and they are more ideal for predicting the passenger flow of stations during peaks and the passenger flow of lines during holidays.

Highlights

With the continuous formation and expansion of urban rail transit networks, this transportation mode brings high efficiency and convenience to residents’ travel
It can be seen that the prediction accuracy of the long short-term memory neural network (LSTM NN) was much higher than that of autoregressive integrated moving average (ARIMA), regardless of the evening-peak or full-time, especially in the prediction of peak shape
Comparing the dayparting Mean absolute error (MAE) of two methods (Figure 7), the dayparting MAE of ARIMA fluctuated sharply during the evening-peak, while the dayparting MAE of the LSTM NN remained low throughout the full-time, which indicates that the LSTM NN is highly adaptable to extremely changing data

Summary

Introduction

With the continuous formation and expansion of urban rail transit networks, this transportation mode brings high efficiency and convenience to residents’ travel. Classical time series analysis models such as ARIMA [8] and the seasonal autoregressive integrated moving average (SARIMA) model [5,9] have been widely used in road traffic and urban rail transit passenger flow prediction, and they have achieved fairly good results. By using a spatial weight matrix to quantify the correlations between traffic flow at any observation location and the traffic conditions in adjacent locations, the spatiotemporal evolution of traffic flow in the road networks was statistically described, and STARIMA achieved satisfactory prediction results. Since these methods usually require the stationarity hypothesis, their application is limited. Nonlinear methods are more flexible, and their prediction results generally perform better [19,20]

Methods

Results

Conclusion