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Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism

Author

Listed:
  • Zheng, Yan
  • Wang, Shengyou
  • Dong, Chunjiao
  • Li, Wenquan
  • Zheng, Wen
  • Yu, Jingcai

Abstract

Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods.

Suggested Citation

  • Zheng, Yan & Wang, Shengyou & Dong, Chunjiao & Li, Wenquan & Zheng, Wen & Yu, Jingcai, 2022. "Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    • Handle: RePEc:eee:phsmap:v:608:y:2022:i:p1:s0378437122008329
    DOI: 10.1016/j.physa.2022.128274
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    References listed on IDEAS

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    1. Xiaoquan Wang & Chunfu Shao & Chaoying Yin & Chengxiang Zhuge & Wenjun Li, 2018. "Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
    2. Okutani, Iwao & Stephanedes, Yorgos J., 1984. "Dynamic prediction of traffic volume through Kalman filtering theory," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 1-11, February.
    3. Wang, Ke & Ma, Changxi & Qiao, Yihuan & Lu, Xijin & Hao, Weining & Dong, Sheng, 2021. "A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
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    Cited by:

    1. Sun, Xiaoyong & Chen, Fenghao & Wang, Yuchen & Lin, Xuefen & Ma, Weifeng, 2023. "Short-term traffic flow prediction model based on a shared weight gate recurrent unit neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    2. Zhang, Weibin & Zha, Huazhu & Zhang, Shuai & Ma, Lei, 2023. "Road section traffic flow prediction method based on the traffic factor state network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    3. Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).

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