Incident Duration Modeling Using Flexible Parametric Hazard-Based Models

Joint Authors

Shang, Pan
Li, Ruimin

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2014-11-04

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Biology

Abstract EN

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.

American Psychological Association (APA)

Li, Ruimin& Shang, Pan. 2014. Incident Duration Modeling Using Flexible Parametric Hazard-Based Models. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034654

Modern Language Association (MLA)

Li, Ruimin& Shang, Pan. Incident Duration Modeling Using Flexible Parametric Hazard-Based Models. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1034654

American Medical Association (AMA)

Li, Ruimin& Shang, Pan. Incident Duration Modeling Using Flexible Parametric Hazard-Based Models. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034654

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1034654