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Spatial-temporal dynamic semantic graph neural network

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Abstract

Most existing methods based on graph neural network for traffic flow forecasting cannot effectively exploit potential semantic features, multiple features are aggregated without refining the proportion of their respective weights, and the over-smoothing problem limits the stacked depth of the model. To solve these problems, spatial-temporal dynamic semantic graph neural network is proposed in this paper. Firstly, two different semantic features have been captured by dynamic time warping algorithm and Pearson correlation coefficient for constructing two semantic adjacency matrices. Secondly, a dynamic aggregation method is proposed that learns the weighting ratio corresponding to each feature through training. Thirdly, the injection-stacked structure is designed to solve the over-smoothing problem and allow the network to be stacked with more layer and improve the forecasting accuracy. Finally, the experiments on four PEMS datasets with various methods such as spatio-temporal graph convolutional networks, attention-based spatial-temporal graph convolutional networks, etc. verify that spatial-temporal dynamic semantic graph neural network obtains minimal forecasting errors by capturing the potential semantic features, dynamically aggregating multiple features, and deepening the network layers by injecting-stacked structure. It achieves that root mean square error is 25.59, mean absolute error is 16.12 and mean absolute percentage error is 16.15 on the PEMS03 dataset.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 41974033 and 61803208), the Scientific and technological achievements transformation project of Jiangsu Province (BA2020004), 2020 Industrial Transformation and Upgrading Project of Industry and Information Technology Department of Jiangsu Province, Postgraduate Research & Practice Innovation Program of Jiangsu Province, Bidding project for breakthroughs in key technologies of advantageous industries in Nanjing (2018003).

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Authors and Affiliations

  1. School of Electrical and Automation Engineering, Nanjing Normal University, Xuelin Road 2, Nanjing, 210023, Jiangsu, China

    Rui Zhang & Fei Xie

  2. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Jiangjun Street 29, Nanjing, 211106, Jiangsu, China

    Rui Sun

  3. Center for Transport Studies, Imperial College London, London, SW7 2AZ, UK

    Rui Sun

  4. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Longpandong Road 159, Nanjing, 210037, Jiangsu, China

    Lei Huang

  5. School of Instrument Science and Engineering, Southeast University, Sipailou 2, Nanjing, 210096, Jiangsu, China

    Xixiang Liu

  6. School of Instrument Science and Engineering, Nanjing Institute of Technology, Hongjing Avenue 1, Nanjing, 211167, Jiangsu, China

    Jianjun Shi

  7. Nanjing Zhongke Raycham Laser Technology Co., Ltd, Xinghe Road 18, Nanjing, 210038, Jiangsu, China

    Jianjun Shi

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  1. Rui Zhang
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  2. Fei Xie
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  3. Rui Sun
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  4. Lei Huang
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Correspondence to Fei Xie.

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Zhang, R., Xie, F., Sun, R. et al. Spatial-temporal dynamic semantic graph neural network. Neural Comput & Applic 34, 16655–16668 (2022). https://doi.org/10.1007/s00521-022-07285-3

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