A Method for Traffic Flow Forecasting in a Large-Scale Road Network Using Multifeatures

  • Chuhao Zhou South China University of Technology, School of Civil Engineering and Transportation, Guangzhou, China
  • Peiqun Lin South China University of Technology, School of Civil Engineering and Transportation, Guangzhou, China
  • Xukun Lin Guangdong Provincial Department of Transportation, Guangzhou, China
  • Yang Cheng University of Wisconsin Madison, Wisconsin Traffic Operations and Safety Laboratory, USA
Keywords: traffic flow prediction, deep learning, multistep prediction, toll station management

Abstract

Accurate traffic prediction on a large-scale road network is significant for traffic operations and management. In this study, we propose an equation for achieving a comprehensive and accurate prediction that effectively combines traffic data and non-traffic data. Based on that, we developed a novel prediction model, called the adaptive deep neural network (ADNN). In the ADNN, we use two long short-term memory (LSTM) networks to extract spatial-temporal characteristics and temporal characteristics, respectively. A backpropagation neural network (BPNN) is also employed to represent situations from contextual factors such as station index, forecast horizon, and weather. The experimental results show that the prediction of ADNN for different stations and different forecast horizons has high accuracy; even for one hour ahead, its performance is also satisfactory. The comparison of ADNN and several benchmark prediction models also indicates the robustness of the ADNN.

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Published
2021-08-05
How to Cite
1.
Zhou C, Lin P, Lin X, Cheng Y. A Method for Traffic Flow Forecasting in a Large-Scale Road Network Using Multifeatures . Promet - Traffic&Transportation. 2021;33(4):593-08. DOI: 10.7307/ptt.v33i4.3709
Section
Articles