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Measurement and classification of transit delays using GTFS-RT data

Author

Listed:
  • Zack Aemmer

    (University of Washington)

  • Andisheh Ranjbari

    (University of Washington)

  • Don MacKenzie

    (University of Washington)

Abstract

This paper presents a method for extracting transit performance metrics from a General Transit Feed Specification’s Real-Time (GTFS-RT) component and aggregating them to roadway segments. A framework is then used to analyze this data in terms of consistent, predictable delays (systematic delays) and random variation on a segment-by-segment basis (stochastic delays). All methods and datasets used are generalizable to transit systems which report vehicle locations in terms of GTFS-RT parameters. This provides a network-wide screening tool that can be used to determine locations where reactive treatments (e.g., schedule padding) or proactive infrastructural changes (e.g., bus-only lanes, transit signal priority) may be effective at improving efficiency and reliability. To demonstrate this framework, a case study is performed regarding one year of GTFS-RT data retrieved from the King County Metro bus network in Seattle, Washington. Stochastic and systematic delays were calculated and assigned to segments in the network, providing insight to spatial trends in reliability and efficiency. Findings for the study network suggest that high-pace segments create an opportunity for large, stochastic speedups, while the network as a whole may carry excessive schedule padding. In addition to the static analysis discussed in this paper, an online interactive visualization tool was developed to display ongoing performance measures in the case study region. All code is open-source to encourage additional generalizable work on the GTFS-RT standard.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:pubtra:v:14:y:2022:i:2:d:10.1007_s12469-022-00291-7
    DOI: 10.1007/s12469-022-00291-7
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    References listed on IDEAS

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    1. Watkins, Kari Edison & Ferris, Brian & Borning, Alan & Rutherford, G. Scott & Layton, David, 2011. "Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(8), pages 839-848, October.
    2. Hyun Kim & Yena Song, 2018. "An integrated measure of accessibility and reliability of mass transit systems," Transportation, Springer, vol. 45(4), pages 1075-1100, July.
    3. John M. Gleason & Darold T. Barnum, 1982. "Toward Valid Measures of Public Sector Productivity: Performance Measures in Urban Transit," Management Science, INFORMS, vol. 28(4), pages 379-386, April.
    4. Alexander Webb & Pramesh Kumar & Alireza Khani, 2020. "Estimation of passenger waiting time using automatically collected transit data," Public Transport, Springer, vol. 12(2), pages 299-311, June.
    5. Åse Jevinger & Jan A. Persson, 2019. "Exploring the potential of using real-time traveler data in public transport disturbance management," Public Transport, Springer, vol. 11(2), pages 413-441, August.
    6. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    7. 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.
    8. Koragot Kaeoruean & Santi Phithakkitnukoon & Merkebe Getachew Demissie & Lina Kattan & Carlo Ratti, 2020. "Analysis of demand–supply gaps in public transit systems based on census and GTFS data: a case study of Calgary, Canada," Public Transport, Springer, vol. 12(3), pages 483-516, October.
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