Measurement and classification of transit delays using GTFS-RT data

Zack Aemmer (), Andisheh Ranjbari () and Don MacKenzie ()
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Zack Aemmer: University of Washington
Andisheh Ranjbari: University of Washington
Don MacKenzie: University of Washington

Public Transport, 2022, vol. 14, issue 2, No 1, 263-285

Abstract: 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.

Keywords: Public transit; GTFS real time; Performance (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s12469-022-00291-7

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