International Conference on Transportation and Development 2020
Identification of Traffic Congestion Patterns Using Connected Vehicle for Active Traffic Management
Publication: International Conference on Transportation and Development 2020
ABSTRACT
The diverted traffic from the freeways to alternative arterials due to incidents deteriorates the intersection movement performance causing delays, long queues, spillbacks, spillovers, and so on. At the critical intersections, the diversion most likely affects only specific movement(s) in the direction of the diverted traffic rather than the whole intersection. This study investigates the use of data from connected vehicle (CV) technology combined with the high-resolution controller (HRC) to automatically identify traffic congestion patterns at the signalized intersections due to diversion. Four different clustering techniques: K-means, K-means with principal component analysis (PCA), K-means with t-distributed stochastic neighboring embedding (t-SNE), and deep embedded clustering (DEC) are investigated for use to identify the patterns, and their performances in identifying the congestion patterns are evaluated. The results indicate the ability of the select method to identify distinctive patterns of congestions that agencies can use in implementing signal timing plans to better accommodate the diverted traffic without significantly deteriorating the performance of other movements at the intersection.
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Published In
International Conference on Transportation and Development 2020
Pages: 215 - 226
Editor: Guohui Zhang, Ph.D., University of Hawaii
ISBN (Online): 978-0-7844-8315-2
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Aug 31, 2020
Published in print: Aug 31, 2020
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