Nonlinear Autoregressive Conditional Duration Models for Traffic Congestion Estimation
Joint Authors
Kepaptsoglou, Konstantinos
Karlaftis, Matthew G.
Vlahogianni, Eleni I.
Source
Journal of Probability and Statistics
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-08-15
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Vlahogianni, Eleni I.& Karlaftis, Matthew G.& Kepaptsoglou, Konstantinos. 2011. Nonlinear Autoregressive Conditional Duration Models for Traffic Congestion Estimation. Journal of Probability and Statistics،Vol. 2011, no. 2011, pp.1-13.
https://search.emarefa.net/detail/BIM-499075
Modern Language Association (MLA)
Vlahogianni, Eleni I.…[et al.]. Nonlinear Autoregressive Conditional Duration Models for Traffic Congestion Estimation. Journal of Probability and Statistics No. 2011 (2011), pp.1-13.
https://search.emarefa.net/detail/BIM-499075
American Medical Association (AMA)
Vlahogianni, Eleni I.& Karlaftis, Matthew G.& Kepaptsoglou, Konstantinos. Nonlinear Autoregressive Conditional Duration Models for Traffic Congestion Estimation. Journal of Probability and Statistics. 2011. Vol. 2011, no. 2011, pp.1-13.
https://search.emarefa.net/detail/BIM-499075
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-499075