Measurement and classification of transit delays using GTFS-RT data

Aemmer, Zack; Ranjbari, Andisheh; MacKenzie, Don

doi:10.1007/s12469-022-00291-7

Case Study and Application
Published: 23 February 2022

Measurement and classification of transit delays using GTFS-RT data

Public Transport (2022)Cite this article

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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.

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Data availability

The datasets generated during and/or analyzed during this study are available from the corresponding author on reasonable request.

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Acknowledgements

This study was supported by Amazon, Uber, King County Metro, Sound Transit, the Seattle Department of Transportation, Challenge Seattle, and the Mobility Innovation Center at University of Washington CoMotion.

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Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, 98195, USA
Zack Aemmer, Andisheh Ranjbari & Don MacKenzie

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Zack Aemmer
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Andisheh Ranjbari
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Don MacKenzie
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Correspondence to Zack Aemmer.

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Aemmer, Z., Ranjbari, A. & MacKenzie, D. Measurement and classification of transit delays using GTFS-RT data. Public Transp (2022). https://doi.org/10.1007/s12469-022-00291-7

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Accepted: 23 January 2022
Published: 23 February 2022
DOI: https://doi.org/10.1007/s12469-022-00291-7

Keywords

Public transit
GTFS real time
Performance