Analysis of the Frequency and Severity of Rear-End Crashes in Work Zones

Qi, Yi; Srinivasan, Raghavan; Teng, Hualiang; Baker, Robert F.

doi:10.1080/15389588.2012.675109

Cited by 57 publications

(30 citation statements)

References 21 publications

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“…These drivers tend to be more aware of the danger and are better prepared to slow down or take other measures to reduce the crash risk. These findings are consistent with a number of previous researches [31,[38][39][40][41]. However, Zhao and Garber found no major differences between the day and nighttime crashes in the work zone [42].…”

Section: Model Estimation Resultssupporting

confidence: 93%

Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

Zhang

Hassan

2019

Journal of Advanced Transportation

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Egypt’s National Road Project is a large infrastructure project aiming to upgrade the existing network of 2500 kilometers as well as constructing new roads of 4000 kilometers to meet today’s need. Increasing highway work zones eventually direct the challenges for traffic safety and mobility. Realizing the need for mitigating the impact of such a challenging scenario, this paper aims to investigate and identify the factors of work zone rear-end crash severity. In this regard, a random parameter ordered probit model was applied to analyze data on the Egyptian long-term highway work zone projects during the period of 2010 to 2017. The factors of speeding and foggy weather conditions are found to be the key indicators for modeling the random parameters. Besides, during the weekend and at nighttime, there is a higher risk of rear-end crash in work zones, while heavy and passenger vehicles are at greater risk in this regard. It is anticipated that the findings of this study would facilitate transport agencies in developing effective measures to ensure safe mobility across work zones.

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Section: Model Estimation Resultssupporting

confidence: 93%

Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

Zhang

Hassan

2019

Journal of Advanced Transportation

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“…Hazards in work zones have typically been studied through analyses of historical crash data to identify the factors contributing to the frequency of work zone crashes (Chen and Tarko, 2012;Daniel et al, 2000;Harb et al, 2008;Khattak et al, 2002;Khattak and Targa, 2004;Qi et al, 2013;Venugopal and Tarko, 2000) and their injury severity (Khattak et al, 2002;Khattak and Targa, 2004;Li and Bai, 2008b;Qi et al, 2013). However, the crash data-based approach is often hampered by the lack of detail in official datasets (Chen and Tarko, 2014;Cheng et al, 2012) and the likelihood of under-reporting of work zone crashes (Debnath et al, 2013;Schrock et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Common hazards and their mitigating measures in work zones: A qualitative study of worker perceptions

2015

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Road construction and maintenance activities present challenges for ensuring the safety of workers and the traveling public alike. Hazards in work zones are typically studied using historical crash records but the current study took a qualitative approach by interviewing 66 workers from various work zones in Queensland, Australia. This supplemented and enhanced the limited available data regarding the frequency and nature of work zone crashes in Australia, provided worker insights into contributing factors, and assessed their opinions on the likely effectiveness of current or future approaches to hazard mitigation. Workers may not be aware of objective data regarding effectiveness, but their attitudes and consequent levels of compliance can influence both the likelihood of implementation and the outcomes of safety measures. Despite the potential importance of worker perceptions, they have not been studied comprehensively to date, and thus this study fills a significant gap in the literature. Excessive vehicle speeds, driver distraction and aggression towards roadworkers, working in wet weather, at night and close to traffic stream were among the most common hazards noted by workers. The safety measures perceived to be most effective included police presence, active enforcement, and improving driver awareness and education about work zones. Worker perceptions differed according to their level of exposure to hazards.

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“…For the identification of black spots, numerous indicators are available, the most traditional being a crash frequency analysis (used with the simple ranking [SR] method) due to its highly consistent results across consecutive years of crash history (AASHTO, 2010; Qi et al, 2013). However, this indicator can result in segments of road with a low potential benefit being identified as black spots due to the fact that it does not take into account the severity of the crashes it analyzes, unless a severity specific analysis is used (i.e., crash frequency analysis using only major injury and fatal crash data).…”

Section: Introductionmentioning

confidence: 99%

A Crash Severity-Based Black Spot Identification Model

Costa

Parajuli³

2014

Journal of Transportation Safety & Security

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The objective of this research is to develop a new black spot model incorporating the severity of crashes with high consistency. Crash history from 2005 to 2009 of the Ipswich Motorway, in Australia, was used to identify black spots by using simple ranking and empirical Bayes approach. The results of these analyses were then compared via two consistency tests. These tests revealed that for the simple ranking method and the empirical Bayes method, the crash type analysis was more consistent than the crash frequency analysis and the collective risk analysis. Further research into the validity of the proposed indicator would be recommended due to its incorporation of the crash severity and the high consistency it demonstrated in this study, both of which are of great benefit from a practical point of view.

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Analysis of the Frequency and Severity of Rear-End Crashes in Work Zones

Cited by 57 publications

References 21 publications

Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

Common hazards and their mitigating measures in work zones: A qualitative study of worker perceptions

A Crash Severity-Based Black Spot Identification Model

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