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Title: A Bayesian modeling framework for crash severity effects of active traffic management systems

Abstract

Transportation agencies utilize Active traffic management (ATM) systems to dynamically manage recurrent and non-recurrent congestion based on real-time conditions. While these systems have been shown to have some safety benefits, their impact on injury severity outcomes is currently uncertain. In this paper, we used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities. The estimation results revealed lower severities with ATM deployment. Marginal effects for ATM deployments that featured hard shoulder running (HSR) revealed lower likelihoods for severe and moderate injury crashes of 15.9 % and for minor injury crashes of 10.1 %. The likelihood of severe and moderate injury crashes and minor injury crashes reduced by 12.4 % and 8.33 % with ATM without HSR. The models were observed to be temporally transferable and had forecast error of 0.301 and 0.304 for the two models, revealing better performance with validation data. These results have implications for improving freeway crash risk at critical locations.

Authors:
 [1];  [2]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Virginia Transportation Research Council, Charlottesville, VA (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1649155
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Accident Analysis and Prevention
Additional Journal Information:
Journal Volume: 145; Journal Issue: 1; Journal ID: ISSN 0001-4575
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; active traffic management; safety; injury severity; Bayesian modeling; variable speed limit; lane control signals

Citation Formats

Khattak, Zulqarnain H., and Fontaine, Michael D. A Bayesian modeling framework for crash severity effects of active traffic management systems. United States: N. p., 2020. Web. https://doi.org/10.1016/j.aap.2020.105544.
Khattak, Zulqarnain H., & Fontaine, Michael D. A Bayesian modeling framework for crash severity effects of active traffic management systems. United States. https://doi.org/10.1016/j.aap.2020.105544
Khattak, Zulqarnain H., and Fontaine, Michael D. Tue . "A Bayesian modeling framework for crash severity effects of active traffic management systems". United States. https://doi.org/10.1016/j.aap.2020.105544.
@article{osti_1649155,
title = {A Bayesian modeling framework for crash severity effects of active traffic management systems},
author = {Khattak, Zulqarnain H. and Fontaine, Michael D.},
abstractNote = {Transportation agencies utilize Active traffic management (ATM) systems to dynamically manage recurrent and non-recurrent congestion based on real-time conditions. While these systems have been shown to have some safety benefits, their impact on injury severity outcomes is currently uncertain. In this paper, we used full Bayesian mixed logit models to quantify the impact that ATM deployment had on crash severities. The estimation results revealed lower severities with ATM deployment. Marginal effects for ATM deployments that featured hard shoulder running (HSR) revealed lower likelihoods for severe and moderate injury crashes of 15.9 % and for minor injury crashes of 10.1 %. The likelihood of severe and moderate injury crashes and minor injury crashes reduced by 12.4 % and 8.33 % with ATM without HSR. The models were observed to be temporally transferable and had forecast error of 0.301 and 0.304 for the two models, revealing better performance with validation data. These results have implications for improving freeway crash risk at critical locations.},
doi = {10.1016/j.aap.2020.105544},
journal = {Accident Analysis and Prevention},
number = 1,
volume = 145,
place = {United States},
year = {2020},
month = {9}
}

Journal Article:
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This content will become publicly available on September 1, 2021
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