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Data-Driven Graph Filter-Based Graph Convolutional Neural Network Approach for Network-Level Multi-Step Traffic Prediction

Author

Listed:
  • Lei Lin

    (Goergen Institute for Data Science, 1209 Wegmans Hall, University of Rochester, Rochester, NY 14627, USA)

  • Weizi Li

    (Department of Computer Science, The University of Memphis, Memphis, TN 38152, USA)

  • Lei Zhu

    (Systems Engineering & Engineering Management, The University of North Carolina at Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA)

Abstract

Accurately predicting network-level traffic conditions has been identified as a critical need for smart and advanced transportation services. In recent decades, machine learning and artificial intelligence have been widely applied for traffic state, including traffic volume prediction. This paper proposes a novel deep learning model, Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF), for network-wide multi-step traffic volume prediction. More specifically, the proposed GCNN-DDGF model can automatically capture hidden spatiotemporal correlations between traffic detectors, and its sequence-to-sequence recurrent neural network architecture is able to further utilize temporal dependency from historical traffic flow data for multi-step prediction. The proposed model was tested in a network-wide hourly traffic volume dataset between 1 January 2018 and 30 June 2019 from 150 sensors in the Los Angeles area. Detailed experimental results illustrate that the proposed model outperforms the other five widely used deep learning and machine learning models in terms of computational efficiency and prediction accuracy. For instance, the GCNN-DDGF model improves MAE, MAPE, and RMSE by 25.33%, 20.45%, and 29.20% compared to the state-of-the-art models, such as Diffusion Convolution Recurrent Neural Network (DCRNN), which is widely accepted as a popular and effective deep learning model.

Suggested Citation

  • Lei Lin & Weizi Li & Lei Zhu, 2022. "Data-Driven Graph Filter-Based Graph Convolutional Neural Network Approach for Network-Level Multi-Step Traffic Prediction," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:16701-:d:1002121
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    References listed on IDEAS

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    1. Osvaldo Anacleto & Catriona Queen & Casper J. Albers, 2013. "Multivariate forecasting of road traffic flows in the presence of heteroscedasticity and measurement errors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(2), pages 251-270, March.
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    Cited by:

    1. Huang, Hai-chao & Chen, Jing-ya & Shi, Bao-cun & He, Hong-di, 2023. "Multi-step forecasting of short-term traffic flow based on Intrinsic Pattern Transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).

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