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Journal Article

Citation

Li R, Shang P. Comput. Intell. Neurosci. 2014; 2014: 723427.

Affiliation

Institute of Transportation Engineering, Department of Civil Engineering, Tsinghua University, Heshanheng Building, Tsinghua, Beijing 100084, China.

Copyright

(Copyright © 2014, Hindawi Publishing)

DOI

10.1155/2014/723427

PMID

25530753

Abstract

Assessing and prioritizing the duration time and effects of traffic incidents on major roads present significant challenges for road network managers. This study examines the effect of numerous factors associated with various types of incidents on their duration and proposes an incident duration prediction model. Several parametric accelerated failure time hazard-based models were examined, including Weibull, log-logistic, log-normal, and generalized gamma, as well as all models with gamma heterogeneity and flexible parametric hazard-based models with freedom ranging from one to ten, by analyzing a traffic incident dataset obtained from the Incident Reporting and Dispatching System in Beijing in 2008.

RESULTS show that different factors significantly affect different incident time phases, whose best distributions were diverse. Given the best hazard-based models of each incident time phase, the prediction result can be reasonable for most incidents. The results of this study can aid traffic incident management agencies not only in implementing strategies that would reduce incident duration, and thus reduce congestion, secondary incidents, and the associated human and economic losses, but also in effectively predicting incident duration time.


Language: en

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