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Public Transportation Prediction with Convolutional Neural Networks

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Intelligent Transport Systems. From Research and Development to the Market Uptake (INTSYS 2019)

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Abstract

Good, efficient and reliable public transportation systems are of crucial importance for all major cities today. In this paper, we propose a concrete solution to a particular problem: improve the prediction of the bus arrival time at each bus stop station on a given itinerary, by taking to account global and local traffic contexts. The main principle consists of modeling the traffic data as an image structure, adapted for applying CNN deep neural networks. The results obtained shows that the proposed approach outperforms traditional machine learning techniques, such as OLS (Ordinary Least Squares) or SVR (Support Vector Regression) with different kernels (RBF or Polynomial), with more than 18% better accuracy prediction, while being computationally faster.

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Acknowledgment

Part of this work has been carried out within the framework of the French FUI project ETS (Electronic Ticketing System), supported by the Conseil Départemantal de l’Essonne and the Systematic Paris Region competitivity cluster.

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Authors and Affiliations

  1. IP Paris, Télécom SudParis, ARTEMIS Department, UMR CNRS 5157 SAMOVAR, Évry, France

    Dancho Panovski & Titus Zaharia

Authors
  1. Dancho Panovski
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  2. Titus Zaharia
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Corresponding author

Correspondence to Dancho Panovski .

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Editors and Affiliations

  1. ISCTE-IUL, Lisbon, Portugal

    Ana Lúcia Martins

  2. ISCTE-IUL, Lisbon, Portugal

    Joao Carlos Ferreira

  3. University of Pisa, Pisa, Italy

    Alexander Kocian

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Panovski, D., Zaharia, T. (2020). Public Transportation Prediction with Convolutional Neural Networks. In: Martins, A., Ferreira, J., Kocian, A. (eds) Intelligent Transport Systems. From Research and Development to the Market Uptake. INTSYS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 310. Springer, Cham. https://doi.org/10.1007/978-3-030-38822-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-38822-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38821-8

  • Online ISBN: 978-3-030-38822-5

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