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Journal of Probability and Statistics
Volume 2011, Article ID 798953, 13 pages
Research Article

Nonlinear Autoregressive Conditional Duration Models for Traffic Congestion Estimation

Transportation Planning and Engineering Departement, School of Civil Engineering, National Technical University of Athens, 5 Iroon Polytechniou Street, Athens, Attica 157 73, Greece

Received 22 December 2010; Accepted 7 March 2011

Academic Editor: Kelvin K. W. Yau

Copyright © 2011 Eleni I. Vlahogianni et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.