A Data-Driven Method for Congestion Identification and Classification
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 4
Abstract
Congestion detection is one of the key steps in reducing delays and associated costs in traffic management. With the increasing use of global positioning system (GPS)-based navigation, promising speed data are now available. This study used extensive historical probe data (year 2016) in Des Moines, Iowa. We used Bayesian change point detection to segment the speed signal and detect temporal congestion. The detected congestion events were then classified as recurrent congestion (RC) or nonrecurrent congestion (NRC). This paper thus presents a robust statistical, big-data-driven expert system and a big-data-mining methodology for identifying both recurrent and nonrecurrent congestion.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 4 • April 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Apr 21, 2021
Accepted: Dec 8, 2021
Published online: Feb 9, 2022
Published in print: Apr 1, 2022
Discussion open until: Jul 9, 2022
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