Computer Science ›› 2021, Vol. 48 ›› Issue (2): 264-270.doi: 10.11896/jsjkx.200300098

• Artificial Intelligence • Previous Articles     Next Articles

Traffic Flow Forecasting Method Combining Spatio-Temporal Correlations and Social Events

LYU Ming-qi, HONG Zhao-xiong, CHEN Tie-ming   

  1. College of Computer Science & Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2020-03-16 Revised:2020-08-28 Online:2021-02-15 Published:2021-02-04
  • About author:LYU Ming-qi,born in 1982,Ph.D,associated professor,is a member of China Computer Federation.His main research interests include data mining and ubiquitous computing.
    CHEN Tie-ming,born in 1978,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include data mining and information security.
  • Supported by:
    The Joint Funds of the National Natural Science Foundation of China(U1936215),Zhejiang Provincial Natural Science Foundation (LY18F020033) and Innovative Development Project of the Industrial Internet (TC190H3WN,TC200H01V).

Abstract: Traffic flow prediction,as a key issue in intelligent transportation system,becomes a research hotspot in the field of transportation both at home and abroad.The main challenge of traffic flow prediction is twofold.First,traffic flow has complica-ted spatial and temporal correlations.Second,traffic flow can be influenced by social events.Aiming at these challenges,this paper proposes a deep learning framework for traffic flow prediction.On the one hand,a sub-network by combining graph convolutional neural network and recurrent neural network is designed to extract spatio-temporal correlation features from the non-European road network space.On the other hand,a sub-network based on convolutional neural network is designed to extract social event features from textual data.Finally,the traffic flow prediction model is implemented by merging the spatio-temporal correlation feature extraction subnetwork and the social event feature extraction sub-network.In order to verify the validity of the model,experiments are conducted based on real traffic flow data.Compared with the baseline methods,the proposed method has higher accuracy,and the accuracy improves by 3% to 6%.

Key words: Convolutional neural network, Graph convolutional network, Recurrent neural network, Social events, Traffic flow forecasting

CLC Number: 

  • TP391
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