Abstract
This paper adopts an unsupervised learning approach, -means clustering, to analyze the arterial traffic flow data over a high-dimensional spatiotemporal feature space. As part of the adaptive traffic control system deployed around the East Liberty area in Pittsburgh, high-resolution traffic occupancies and counts are available at the lane level in virtually any time resolution. The -means clustering method is used to analyze those data to understand the traffic patterns before and after the closure and reopening of an arterial bridge. The modeling framework also holds great potentials for predicting traffic flow and detect incidents. The main findings are that clustering on high-dimensional spatiotemporal features can effectively distinguish flow patterns before and after road closure and reopening and between weekends and weekdays. On arterial streets, clustering based on 5-min data is sufficient to eliminate potential distortion on measurements caused by signals. Either of the two, count or occupancy, is adequate to serve as a feature for effective pattern clustering. It is plausible to use data from only selected locations and time periods to infer representative flow patterns and detect arterial incidents, which allows applications in large-scale networks with cheap sensing. In addition, for some lanes, there exists a transitional time period (1 week in this case study) immediately following the closure and reopening when traffic flow is transformed to a new type of daily pattern.
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Acknowledgments
This research is funded in part by Carnegie Mellon University’s Technologies for Safe and Efficient Transportation, a National USDOT University Transportation Center for Safety (T-SET UTC), which is sponsored by the U.S. Department of Transportation. The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. The U.S. Government assumes no liability for the contents or use thereof.
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Published In
Journal of Transportation Engineering
Volume 142 • Issue 9 • September 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: May 22, 2015
Accepted: Feb 4, 2016
Published online: Apr 15, 2016
Published in print: Sep 1, 2016
Discussion open until: Sep 15, 2016
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