IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v14y2022i2d10.1007_s12469-022-00291-7.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-022-00291-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12469-022-00291-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Merkebe Getachew Demissie & Lina Kattan, 2022. "Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory study," Public Transport, Springer, vol. 14(2), pages 385-417, June.
    3. 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.
    4. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    5. Tianxing Dai & Brian D. Taylor, 2023. "Three’s a crowd? Examining evolving public transit crowding standards amidst the COVID-19 pandemic," Public Transport, Springer, vol. 15(2), pages 321-341, June.
    6. Marina Lagune-Reutler & Andrew Guthrie & Yingling Fan & David Levinson, 2015. "Transit Riders' Perception of Waiting Time and Stops' Surrounding Environments," Working Papers 000142, University of Minnesota: Nexus Research Group.
    7. Sarker, Rumana Islam & Kaplan, Sigal & Mailer, Markus & Timmermans, Harry J.P., 2019. "Applying affective event theory to explain transit users’ reactions to service disruptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 593-605.
    8. Allard, Ryan F. & Moura, Filipe, 2018. "Effect of transport transfer quality on intercity passenger mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 89-107.
    9. Matsumoto, Takayuki & Hidaka, Kazuyoshi, 2015. "Evaluation the effect of mobile information services for public transportation through the empirical research on commuter trains," Technology in Society, Elsevier, vol. 43(C), pages 144-158.
    10. Kelly, J. Andrew & Fu, Miao, 2014. "Sustainable school commuting – understanding choices and identifying opportunities," Journal of Transport Geography, Elsevier, vol. 34(C), pages 221-230.
    11. Oliveira, Renata Lúcia Magalhães de & Dablanc, Laetitia & Schorung, Matthieu, 2022. "Changes in warehouse spatial patterns and rental prices: Are they related? Exploring the case of US metropolitan areas," Journal of Transport Geography, Elsevier, vol. 104(C).
    12. Bounie, Nathan & Adoue, François & Koning, Martin & L'Hostis, Alain, 2019. "What value do travelers put on connectivity to mobile phone and Internet networks in public transport? Empirical evidence from the Paris region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 158-177.
    13. Yang Chen & Arturo Ardila-Gomez & Gladys Frame, 2016. "Achieving Energy Savings by Intelligent Transportation Systems Investments in the Context of Smart Cities," World Bank Publications - Reports 24740, The World Bank Group.
    14. Liu, Luyu & Miller, Harvey J., 2020. "Does real-time transit information reduce waiting time? An empirical analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 167-179.
    15. Lisa Dang & Widar von Arx, 2021. "How Can Rail Use for Leisure and Tourism Be Promoted? Using Leisure and Mobility Orientations to Segment Swiss Railway Customers," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    16. Mulley, Corinne & Clifton, Geoffrey Tilden & Balbontin, Camila & Ma, Liang, 2017. "Information for travelling: Awareness and usage of the various sources of information available to public transport users in NSW," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 111-132.
    17. Thierry Blayac & Maïté Stéphan, 2022. "Travel information provision and commuter behavior changes: Evidence from a french metropolis," Post-Print hal-03649092, HAL.
    18. Cats, Oded & Loutos, Gerasimos, 2013. "Real-time bus arrival information system: an empirical evaluation," Working papers in Transport Economics 2013:25, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    19. Monika Bąk & Przemyslaw Borkowski, 2019. "Young Transport Users’ Perception of ICT Solutions Change," Social Sciences, MDPI, vol. 8(8), pages 1-17, July.
    20. Moran, Marcel E, 2022. "Are shelters in place? Mapping the distribution of transit amenities via a bus-stop census of San Francisco," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3gj1t495, Institute of Transportation Studies, UC Berkeley.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pubtra:v:14:y:2022:i:2:d:10.1007_s12469-022-00291-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.