Using APC-AVL Data to Improve Transit Reliability and Accessibility Analysis
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 1
Abstract
Real-world transit service often deviates substantially from schedules, leading to variability in route-choice behaviors and experienced travel times that are not adequately reflected in existing accessibility analyses based on general transit feed specification (GTFS) schedule data alone. This study presents a methodology for more accurately assessing origin–destination (OD) transit reliability and accessibility by using GTFS data to generate candidate itineraries, then using automatic passenger counting and automatic vehicle location (APC-AVL) data to determine which routes are selected in real time and calculate the experienced travel time. The methodology is applied across March 2016–2020 from the perspectives of two groups in Allegheny County, Pennsylvania: residents in origins of high socioeconomic need traveling to opportunity employment hotspots, and prospective employers looking to establish a transit-accessible worksite. The worst OD pairs in practice achieved less than 15% of the accessibility captured by 95% of scheduled trips. At the 90-min threshold, the lowest observed accessibility corresponded to a realized loss of 17% of the eligible workforce due to service unreliability. These findings reveal the need to incorporate APC-AVL data to accurately model the route choice process and evaluate rider experiences.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies. Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This research is supported by U.S. Department of Education Graduate Assistance in Areas of National Need Program, and by Mobility 21 at Carnegie Mellon University, a National University Transportation Center sponsored by the U.S. Department of Transportation. The contents of this paper reflect the views of the authors only, who are responsible for the facts and the accuracy of the information presented herein.
Data from Allegheny County, Pennsylvannia, used in this study are available in the following online repositories: GTFS files were obtained from Pittsburgh Regional Transit (2022), employment data by NAICS code were obtained from Ruggles et al. (2020), and job count data were obtained from US Census Bureau (2020). APC-AVL data were provided by Pittsburgh Regional Transit (2022). Direct requests for APC-AVL data may be made to the provider. Code used in this study is available from the corresponding author upon reasonable request.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 1 • January 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Apr 10, 2023
Accepted: Aug 28, 2023
Published online: Oct 31, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 31, 2024
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