Abstract
As the amount of traffic congestion continues to grow, pinpointing freeway bottleneck locations and quantifying their impacts are crucial activities for traffic management and control. Among the previous bottleneck identification methods, limitations still exist. The first key limitation is that they cannot determine precise breakdown durations at a bottleneck in an objective manner. Second, the input data often needs to be aggregated in an effort to ensure better robustness to noise, which will significantly reduce the time resolution. Wavelet transform, as a powerful and efficient data-processing tool, has already been implemented in some transportation application scenarios to much benefit. However, there is still a wide gap between existing preliminary explorations of wavelet analysis in transportation research and a completely automatic bottleneck identification framework. This paper addresses several key issues in existing bottleneck identification approaches and also fills a gap in transportation-related wavelet applications. The experimental results demonstrate that the proposed method is able to locate the most severe bottlenecks and comprehensively quantify their impacts.
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Acknowledgments
This research was supported in part by the National Natural Science Foundation of China (Grant No. 51329801) and the Shenzhen Science and Technology Planning Project (Grant No. GJHZ20150316154158400).
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 144 • Issue 9 • September 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Aug 13, 2017
Accepted: Mar 5, 2018
Published online: Jun 19, 2018
Published in print: Sep 1, 2018
Discussion open until: Nov 19, 2018
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