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Complementary parametric probit regression and nonparametric classification tree modeling approaches to analyze factors affecting severity of work zone weather-related crashes

Authors
  • Ghasemzadeh, Ali1
  • Ahmed, Mohamed M.1
  • 1 University of Wyoming, Department of Civil and Architectural Engineering, Laramie, WY, 82071, USA , Laramie (United States)
Type
Published Article
Journal
Journal of Modern Transportation
Publisher
Springer Singapore
Publication Date
Dec 21, 2018
Volume
27
Issue
2
Pages
129–140
Identifiers
DOI: 10.1007/s40534-018-0178-6
Source
Springer Nature
Keywords
License
Green

Abstract

Identifying risk factors for road traffic injuries can be considered one of the main priorities of transportation agencies. More than 12,000 fatal work zone crashes were reported between 2000 and 2013. Despite recent efforts to improve work zone safety, the frequency and severity of work zone crashes are still a big concern for transportation agencies. Although many studies have been conducted on different work zone safety-related issues, there is a lack of studies that investigate the effect of adverse weather conditions on work zone crash severity. This paper utilizes probit–classification tree, a relatively recent and promising combination of machine learning technique and conventional parametric model, to identify factors affecting work zone crash severity in adverse weather conditions using 8 years of work zone weather-related crashes (2006–2013) in Washington State. The key strength of this technique lies in its capability to alleviate the shortcomings of both parametric and nonparametric models. The results showed that both presence of traffic control device and lighting conditions are significant interacting variables in the developed complementary crash severity model for work zone weather-related crashes. Therefore, transportation agencies and contractors need to invest more in lighting equipment and better traffic control strategies at work zones, specifically during adverse weather conditions.

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