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Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks

Author

Listed:
  • Daniel Vélez-Serrano

    (Department of Statistics and Operations Research, Complutense University, 28040 Madrid, Spain)

  • Alejandro Álvaro-Meca

    (Department of Preventive Medicine and Public Health, Rey Juan Carlos University, Avd, Atenas SN, 28922 Madrid, Spain
    These authors contributed equally to this work.)

  • Fernando Sebastián-Huerta

    (Innova-TSN, 28020 Madrid, Spain)

  • Jose Vélez-Serrano

    (Department of Computer Science, Rey Juan Carlos University, 28922 Madrid, Spain
    These authors contributed equally to this work.)

Abstract

Due to the need to predict traffic congestion during the morning or evening rush hours in large cities, a model that is capable of predicting traffic flow in the short term is needed. This model would enable transport authorities to better manage the situation during peak hours and would allow users to choose the best routes for reaching their destinations. The aim of this study was to perform a short-term prediction of traffic flow in Madrid, using different types of neural network architectures with a focus on convolutional residual neural networks, and it compared them with a classical time series analysis. The proposed convolutional residual neural network is superior in all of the metrics studied, and the predictions are adapted to various situations, such as holidays or possible sensor failures.

Suggested Citation

  • Daniel Vélez-Serrano & Alejandro Álvaro-Meca & Fernando Sebastián-Huerta & Jose Vélez-Serrano, 2021. "Spatio-Temporal Traffic Flow Prediction in Madrid: An Application of Residual Convolutional Neural Networks," Mathematics, MDPI, vol. 9(9), pages 1-16, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:1068-:d:551614
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    References listed on IDEAS

    as
    1. Wang, Wei & Zhang, Hanyu & Li, Tong & Guo, Jianhua & Huang, Wei & Wei, Yun & Cao, Jinde, 2020. "An interpretable model for short term traffic flow prediction," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 264-278.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
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