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Unsupervised approach towards analysing the public transport bunching swings formation phenomenon

Author

Listed:
  • Viktoriya Degeler

    (University of Groningen)

  • Léonie Heydenrijk-Ottens

    (Delft University of Technology)

  • Ding Luo

    (Delft University of Technology)

  • Niels Oort

    (Delft University of Technology)

  • Hans Lint

    (Delft University of Technology)

Abstract

We perform an analysis of public transport data from The Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location and automated fare collection data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques, namely clustering. We also show the correlation between bunching rate and passenger load, and bunching probability patterns for working days and weekends. We present the approach for extracting isolated bunching swings formations (BSF) and show different cases of BSFs, some of which can persist for a considerable time. We applied our approach to the tram line 1 of The Hague, and computed and presented four different patterns of BSFs, which we name “high passenger load”, “whole route”, “evening, end of route”, “long duration”. We analyse each bunching swings formation type in detail.

Suggested Citation

  • Viktoriya Degeler & Léonie Heydenrijk-Ottens & Ding Luo & Niels Oort & Hans Lint, 2021. "Unsupervised approach towards analysing the public transport bunching swings formation phenomenon," Public Transport, Springer, vol. 13(3), pages 533-555, October.
    • Handle: RePEc:spr:pubtra:v:13:y:2021:i:3:d:10.1007_s12469-020-00251-z
    DOI: 10.1007/s12469-020-00251-z
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    References listed on IDEAS

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    Cited by:

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    2. Stefan Voß, 2023. "Bus Bunching and Bus Bridging: What Can We Learn from Generative AI Tools like ChatGPT?," Sustainability, MDPI, vol. 15(12), pages 1-19, June.

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