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Discovering the space–time dimensions of schedule padding and delay from GTFS and real-time transit data

Author

Listed:
  • Nate Wessel

    (University of Toronto)

  • Michael J. Widener

    (University of Toronto)

Abstract

Schedule padding is the extra time added to transit schedules to reduce the risk of delay. Where there is more random delay, there should be more schedule padding. While schedule padding is a product of transit planners, a method for detecting when and where it exists could provide valuable feedback as transit agencies continually develop their networks. By analyzing transit schedules and real-time vehicle location data at the level of stop-to-stop segments, we can locate padding in space and time and identify the places that may be most effected by stochastic delay. Such information could be used to target delay-reduction interventions such as fare prepayment or transit-only rights of way. The Toronto Transit Commission is used as a case study, and initial results suggest that highly delayed segments appear mostly in the expected, but some surprising, places.

Suggested Citation

  • Nate Wessel & Michael J. Widener, 2017. "Discovering the space–time dimensions of schedule padding and delay from GTFS and real-time transit data," Journal of Geographical Systems, Springer, vol. 19(1), pages 93-107, January.
    • Handle: RePEc:kap:jgeosy:v:19:y:2017:i:1:d:10.1007_s10109-016-0244-8
    DOI: 10.1007/s10109-016-0244-8
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    References listed on IDEAS

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    1. Jiamin Zhao & Maged Dessouky & Satish Bukkapatnam, 2006. "Optimal Slack Time for Schedule-Based Transit Operations," Transportation Science, INFORMS, vol. 40(4), pages 529-539, November.
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    4. Bookbinder, James H. & Ahlin, Frank J., 1990. "Synchronized scheduling and random delays in urban transit," European Journal of Operational Research, Elsevier, vol. 48(2), pages 204-218, September.
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    Cited by:

    1. Goliszek Sławomir & Połom Marcin & Duma Patryk, 2020. "Potential and cumulative accessibility of workplaces by public transport in Szczecin," Bulletin of Geography. Socio-economic Series, Sciendo, vol. 50(50), pages 133-146, December.
    2. Juan Godfrid & Pablo Radnic & Alejandro Vaisman & Esteban Zimányi, 2022. "Analyzing public transport in the city of Buenos Aires with MobilityDB," Public Transport, Springer, vol. 14(2), pages 287-321, June.
    3. Zack Aemmer & Andisheh Ranjbari & Don MacKenzie, 2022. "Measurement and classification of transit delays using GTFS-RT data," Public Transport, Springer, vol. 14(2), pages 263-285, June.

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    More about this item

    Keywords

    Public transportation; Geographic information systems; Scheduling; Transit networks; Planning; Spatiotemporal analysis;
    All these keywords.

    JEL classification:

    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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