Analysis of Incident-Induced Capacity Reductions for Improved Delay Estimation
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 145, Issue 2
Abstract
This paper investigates the randomness in incident-induced capacity reductions and discusses the further impacts on delay calculation and modeling. For this purpose, incident and traffic count data sets from four important freeways in California, i.e., I-80, I-280, I-580, and, I-880, dating from February 1 to June 20, 2017, are utilized to analyze the incident capacity reductions. Accordingly, the capacity reduction distributions are identified and compared with the findings from the literature. In addition, the impact of variation in capacity reduction on incident delay is derived analytically for a deterministic queuing model. Through further analysis of the data, it is shown that capacity reduction values vary with respect to traffic conditions (e.g., volume). Accordingly, it is discussed that capacity reduction tables that do not incorporate traffic flow conditions and variance may provide incorrect estimations of delay. Considering the widely used capacity reduction tables for delay calculation, a regression tree approach is utilized to provide similar tables that provide traffic-dependent capacity reduction and variance for practitioner use.
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Acknowledgments
This research was partially supported by The New York State Energy Research and Development Authority (NYSERDA) and New York State Department of Transportation (NYSDOT). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NYSERDA or NYSDOT.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 145 • Issue 2 • February 2019
Copyright
©2018 American Society of Civil Engineers.
History
Received: Jan 20, 2018
Accepted: Jul 25, 2018
Published online: Nov 26, 2018
Published in print: Feb 1, 2019
Discussion open until: Apr 26, 2019
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