Short-term passenger flow forecast for urban rail transit based on multi-source data

Wei Li,Liying Sui,Hairong Dong,Min Zhou

doi:10.1186/s13638-020-01881-4

Abstract

Short-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, passenger flow prediction is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction model. The model is built using intelligent data provided by a large-scale urban traffic flow warning system, such as accurate passenger flow data, collected using the Internet of things and sensor networks. The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction. The obtained pre-diction fits well with the measured data. Therefore, the SARIMA–SVM model can fully charac-terize traffic variations and is suitable for passenger flow prediction.

Highlights

Urban rail transit is the backbone of urban transportation, and its safety management and emergency response efficiency must be improved [1]
3.5 Results and analysis In this study, the performance of the model was evaluated by root-mean-square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), see Eqs. (6), (7), and (8)
The RMSE is suitable for comparison between different models of the same data set, and the MAPE can be used for comparison between different data sets

Summary

Introduction

Urban rail transit is the backbone of urban transportation, and its safety management and emergency response efficiency must be improved [1]. The methods of short-term station passenger flow forecasting for urban rail transit can generally be divided into three categories (linear model, nonlinear model and combined model). Considering the regularity and time-varying characteristics of urban rail transit passenger flow, based on season autoregressive integrating moving average model (SARIMA) and support vector machine model (SVM), this paper proposes the SARIMA-SVM combination model for urban rail transit short-term station passenger flow forecasting. This paper proposes the combined model considers the periodic characteristics of passenger flow changes, and considers the nonlinear relationship between short-term passenger flow and passenger flow before and after the period It makes full use of the existing passenger flow information and realizes the prediction of short-term station passenger flow of urban rail transit.

Principle of SARIMA‐SVM combination model

SVM model

Model application

Conclusion