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DyGCN-LSTM: A dynamic GCN-LSTM based encoder-decoder framework for multistep traffic prediction

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Abstract

Intelligent transportation systems (ITS) are gaining attraction in large cities for better traffic management. Traffic forecasting is an important part of ITS, but a difficult one due to the intricate spatiotemporal relationships of traffic between different locations. Despite the fact that remote or far sensors may have temporal and spatial similarities with the predicting sensor, existing traffic forecasting research focuses primarily on modeling correlations between neighboring sensors while disregarding correlations between remote sensors. Furthermore, existing methods for capturing spatial dependencies, such as graph convolutional networks (GCNs), are unable to capture the dynamic spatial dependence in traffic systems. Self-attention-based techniques for modeling dynamic correlations of all sensors currently in use overlook the hierarchical features of roads and have quadratic computational complexity. Our paper presents a new Dynamic Graph Convolution LSTM Network (DyGCN-LSTM) to address the aforementioned limitations. The novelty of DyGCN-LSTM is that it can model the underlying non-linear spatial and temporal correlations of remotely located sensors at the same time. Experimental investigations conducted using four real-world traffic data sets show that the suggested approach is superior to state-of-the-art benchmarks by \(\varvec{25\%}\) in terms of RMSE.

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Acknowledgements

This work is funded by Scheme for Promotion of Academic and Research Collaboration (SPARC) under the Ministry of Human Resource Development, India, within project code P1506.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihar, India

    Rahul Kumar & Joydeep Chandra

  2. Department of Informatics Engineering, Faculty of Engineering, University of Porto, Porto, Portugal

    João Mendes Moreira

  3. LIAAD, INESC TEC, Porto, Portugal

    João Mendes Moreira

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  1. Rahul Kumar
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Kumar, R., Mendes Moreira, J. & Chandra, J. DyGCN-LSTM: A dynamic GCN-LSTM based encoder-decoder framework for multistep traffic prediction. Appl Intell 53, 25388–25411 (2023). https://doi.org/10.1007/s10489-023-04871-3

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