Operational Scenario Definition in Traffic Simulation-Based Decision Support Systems: Pattern Recognition Using a Clustering Algorithm
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 145, Issue 4
Abstract
This paper is intended to mine historical data by presenting a scenario clustering approach to identify appropriate scenarios for mesoscopic simulation as a part of the evaluation of transportation projects or operational measures. It provides a systematic and efficient approach to select and prepare effective input scenarios for a given traffic simulation model. The scenario clustering procedure has two primary applications: travel time reliability analysis, and traffic estimation and prediction systems. The ability to systematically identify similarity and dissimilarity among weather scenarios can facilitate the selection of critical scenarios for reliability studies. It can also support real-time weather-responsive traffic management (WRTM) by quickly classifying a current or predicted weather condition into predefined categories and suggesting relevant WRTM strategies that can be tested via real-time traffic simulation before deployment. A detailed method for clustering weather time series data is presented and demonstrated using historical data. Two clustering algorithms with different similarity measures are compared. Clustering results using a -means clustering algorithm with squared Euclidean distance are illustrated in the travel time reliability application.
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Acknowledgments
This paper is based in part on work funded by the US Department of Transp. under contract DTFH61-06-D-00005, T-10-005 to Northwestern University’s Transportation Center (NUTC). The authors acknowledge helpful comments provided by Roemer Alfelor of the US Department of Transportation and Robert Haas of SAIC, Inc.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 145 • Issue 4 • April 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jul 24, 2016
Accepted: Aug 27, 2018
Published online: Feb 8, 2019
Published in print: Apr 1, 2019
Discussion open until: Jul 8, 2019
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