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APADGCN: Adaptive Partial Attention Diffusion Graph Convolutional Network for Traffic Flow Forecasting

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Spatial Data and Intelligence (SpatialDI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13887))

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Abstract

Traffic flow forecasting is a core task of urban governance and plays a vital role in the development of ITS. Because of the complexity and uncertainty of traffic patterns, it is of great challenge to capture spatial-temporal correlations. Recent researches mainly focus on the pre-defined adjacency matrix based on prior knowledge as the basis of spatial-temporal correlation modeling, but the fixed graph structure cannot adequately describe the dependency between traffic sensors. To tackle this issue, a novel deep learning model framework is proposed in this paper: Adaptive Partial Attention Diffusion Graph Convolutional Network(APADGCN), which consists of three main parts: 1) the Multi-Component module that divides the historical traffic flow into recent, daily-periodic, and weekly-periodic, to capture the traffic patterns of different periodic; 2) the spatial correlation modeling which can dynamically capture node relationships and model spatial dependency, and enhance the aggregation ability of low-order information; 3) the temporal correlation modeling which models long-term time dependencies using convolution and gating. The final result is obtained by the weighted fusion of the results of the multi-components. We compared our APADGCN with various baseline models in the four real datasets from the Caltrans Performance Measurement System (PeMS). The experimental results show that the prediction accuracy of APADGCN is better than that of the baseline models.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Bowen Zhang & Bohan Li

  2. Key Laboratory of Safety-Critical Software, Ministry of Industry and Information Technology, Beijing, China

    Bohan Li

  3. National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology, Xi’an, China

    Bohan Li

  4. School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, 541010, China

    Jinzhan Wei

  5. College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Hao Wen

Authors
  1. Bowen Zhang
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  2. Bohan Li
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  3. Jinzhan Wei
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  4. Hao Wen
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Corresponding author

Correspondence to Bohan Li .

Editor information

Editors and Affiliations

  1. Renmin University of China, Beijing, China

    Xiaofeng Meng

  2. East Normal China University, Shanghai, China

    Xiang Li

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Jianqiu Xu

  4. Nanjing Normal University, Nanjing, China

    Xueying Zhang

  5. Jiangxi University of Finance and Economics, Nanchang, China

    Yuming Fang

  6. Huazhong University of Science and Technology, Wuhan, China

    Bolong Zheng

  7. Zhengzhou University, Zhengzhou, China

    Yafei Li

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Zhang, B., Li, B., Wei, J., Wen, H. (2023). APADGCN: Adaptive Partial Attention Diffusion Graph Convolutional Network for Traffic Flow Forecasting. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-32910-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32909-8

  • Online ISBN: 978-3-031-32910-4

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