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Public Transport Arrival Time Prediction Based on GTFS Data

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Machine Learning, Optimization, and Data Science (LOD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13164))

Abstract

Public transport (PT) systems are essential to human mobility. PT investments continue to grow, in order to improve PT services. Accurate PT arrival time prediction (PT-ATP) is vital for PT systems delivering an attractive service, since the waiting experience for urban residents is an urgent problem to be solved. However, accurate PT-ATP is a challenging task due to the fact that urban traffic conditions are complex and changeable. Nowadays thousands of PT agencies publish their public transportation route and timetable information with the General Transit Feed Specification (GTFS) as the standard open format. Such data provide new opportunities for using the data-driven approaches to provide effective bus information system. This paper proposes a new framework to address the PT-ATP problem by using GTFS data. Also, an overview of various ML models for PT-ATP purposes is presented, along with the insightful findings through the comparison procedure based on real GTFS datasets. The results showed that the neural network -based method outperforms its rivals in terms of prediction accuracy.

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Notes

  1. 1.

    https://svc.metrotransit.org/mtgtfs/gtfs.zip.

  2. 2.

    https://svc.metrotransit.org/mtgtfs/tripupdates.pb.

  3. 3.

    https://svc.metrotransit.org/mtgtfs/vehiclepositions.pb.

  4. 4.

    https://svc.metrotransit.org/mtgtfs/alerts.pb.

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Acknowledgements

This paper is one of the deliverables of the project with MIS 5050503, of the Call entitled “Support for researchers with emphasis on young researchers - cycle B” (Code: EDBM103) which is part of the Operational Program “Human Resources Development, Education and Lifelong Learning”, which is co-financed by Greece and the European Union (European Social Fund).

Author information

Authors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Eva Chondrodima, Harris Georgiou & Yannis Theodoridis

  2. Department of Statistics and Insurance Science, University of Piraeus, Piraeus, Greece

    Nikos Pelekis

Authors
  1. Eva Chondrodima
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  2. Harris Georgiou
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  3. Nikos Pelekis
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  4. Yannis Theodoridis
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Corresponding author

Correspondence to Eva Chondrodima .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. Department of Computer Science, University of Reading, Reading, UK

    Varun Ojha

  3. Department of Computer Science, University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. Cambridge Judge Business School, University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Giorgio Jansen

  6. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Department of Informatics, Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Chondrodima, E., Georgiou, H., Pelekis, N., Theodoridis, Y. (2022). Public Transport Arrival Time Prediction Based on GTFS Data. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science(), vol 13164. Springer, Cham. https://doi.org/10.1007/978-3-030-95470-3_36

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  • DOI: https://doi.org/10.1007/978-3-030-95470-3_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95469-7

  • Online ISBN: 978-3-030-95470-3

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