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Abstract
The considerable impact of congestion on transportation networks is reflected by the vast amount of research papers dedicated to congestion identification, modeling, and alleviation. Despite this, the statistical characteristics of congestion, and particularly of its duration, have not been systematically studied, regardless of the fact that they can offer significant insights on its formation, effects and alleviation. We extend previous research by proposing the autoregressive conditional duration (ACD) approach for modeling congestion duration in urban signalized arterials. Results based on data from a signalized arterial indicate that a multiregime nonlinear ACD model best describes the observed congestion duration data while when it lasts longer than 18 minutes, traffic exhibits persistence and slow recovery rate.
Document type: Article
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The different versions of the original document can be found in:
- http://downloads.hindawi.com/journals/jps/2011/798953.pdf under the license https://creativecommons.org/licenses/by
- http://dx.doi.org/10.1155/2011/798953 under the license cc-by
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Published on 01/01/2011
Volume 2011, 2011
DOI: 10.1155/2011/798953
Licence: Other
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