Transit Delay Estimation Using Stop-Level Automated Passenger Count Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 144, Issue 3
Abstract
Despite the potential use of global positioning system (GPS) based automatic vehicle location (AVL) data in the development of reliability improvement strategies for transit systems, issues with privacy and accuracy have presented challenges to the open sharing of the AVL data. However, data driven methods of estimating the performance measures of transit vehicles on the basis of the location data are still the most prominent means of reliability studies on transit systems. This paper aims to propose a new method for developing transit performance measures, namely traffic delay, based on the stop-level location data, which does not share the issues concerning privacy and accuracy that hinder the use of GPS-based data. The paper presents a case study on route 16 of the Metro Transit system in the Twin Cities, Minnesota. The delay measures that resulted from the proposed model are at the segment level of resolution, delimited by the route timepoints, and are found to be described well by the gamma distribution. These results allow the localization of excessive delay on a segment level, and the distinction between the traffic delay and dwell time delay.
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Acknowledgments
The authors would like to thank the Metro Transit staff for sharing the data and their valuable comments on an early stage of this research. This research is partially supported by the Center for Transportation Studies at the University of Minnesota under award 00057692: Modeling Reliability of Multimodal Transportation Networks.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 144 • Issue 3 • March 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Feb 22, 2017
Accepted: Aug 21, 2017
Published online: Jan 12, 2018
Published in print: Mar 1, 2018
Discussion open until: Jun 12, 2018
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