Investigating temporal trends in the explanatory variables related to the severity of drivers' injuries in single-vehicle collisions

Dabbour, Essam

doi:10.1016/j.jtte.2016.03.010

Cited by 16 publications

(12 citation statements)

References 18 publications

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“…The effect of using seatbelt is consistently significant throughout all analysis years. This finding adds to the growing number of research studies with similar findings (see for example Chen et al, 2016;Chen et al, 2015;Dabbour, 2017b;Dabbour et al 2017). a) At least one person was visibly injured.…”

Section: Resultssupporting

confidence: 67%

“…Risk factors identified by those prior research studies might have been significant at the time of the analysis, but their significance might change afterward due to the ongoing changes in vehicle technologies, driver behavior, traffic volumes, road conditions, law-enforcement technologies, and implemented policies. (Dabbour, 2017a;Dabbour, 2017b;Dabbour et al, 2017;Behnood and Mannering, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Understanding how drivers are injured in rear-end collisions

Dabbour¹

2020

European Transport/Trasporti Europei

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Rear-end collisions represent the most common type of collisions with more than 2.5 million rear-end collisions reported every year in the United States. Most of the previous research studies that identified risk factors related to the probability of injury in rear-end collisions were based on pooling data from few years (usually 2 – 4 years) into a single dataset for analysis. This approach carries the risk of introducing aggregation bias in the analysis. Risk factors identified using that approach might have been significant at the time of the analysis, but their significance might change afterward due to the ongoing changes in vehicle technologies, driver behavior, traffic volumes, road conditions, law-enforcement technologies, and implemented policies. Thus, understanding the temporal stability trends of the factors influencing how drivers are injured in rear-end collisions would help researchers in evaluating the effectiveness of implementing different safety treatments so that researchers could understand whether any safety improvements, observed after applying a certain safety treatment, are attributed to the specific treatment or simply attributed to the temporal instability of the factors being addressed. This study investigates the temporal stability of the factors related to drivers’ injuries in rear-end collisions. The research is based on utilizing logistic regression modelling to analyze all rear-end collisions that occurred in North Carolina from January 1, 2007 to December 31, 2013. A logistic regression model is developed for each year, and the models are compared together to identify the most temporally stable factors related to drivers’ injuries in rear-end collisions. The findings of this research have the potential to help decision makers develop policies and countermeasures that improve roadway safety by focusing on risk factors that consistently increase the probability of injuries for drivers in rear-end collisions.

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Section: Resultssupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Understanding how drivers are injured in rear-end collisions

Dabbour¹

2020

European Transport/Trasporti Europei

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“…The results show that minor injury outcome is highly probable in speed-related single vehicle crashes in both urban and rural areas in the state. Several studies have documented the role of DUI in crash occurrence and crash injury severity (e.g., Abdel-Aty, 2003;Dabbour, 2017;Tavris, Kuhn, & Layde, 2001). Further, studies have found a high proportion of severe injury crashes involving single-vehicle crashes to have a good chance to be DUI-related (e.g., Adanu, Smith, Powell, & Jones, 2017;Ostrom & Eriksson, 1993;Schneider, Savolainen, & Zimmerman, 2009).…”

Section: Discussionmentioning

confidence: 99%

A comprehensive analysis of factors that influence interstate highway crash severity in Alabama

Adanu

Agyemang

Islam

et al. 2021

Journal of Transportation Safety & Security

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This paper identifies factors that influence the severity of interstate crash outcomes and how they vary depending on the location and manner of collision. Four separate injury severity models were developed to explore the differences and similarities in crash factors between single-and multi-vehicle crashes that occurred in rural and urban areas of the state. Random parameters multinomial logit with heterogeneity in means and variances modeling approach was used to account for unobserved heterogeneity in the crash data. The model estimation results show that some driver behavioral factors such as speeding, aggressive driving, failure to use seatbelt, and driving without a valid license were found to significantly contribute to some form of injury outcome. The influence of roadway features such as type of opposing lane separation, collision type, temporal and lighting conditions on crash outcomes were also explored. Some differences and similarities in the associations between these factors and crash injury severity based on the manner and location of crash were unraveled. These findings are expected to guide the implementation of crash countermeasures on interstates. The findings of this study further support the evidence for the analysis of subsets of crash data to unravel underlying complex relationships within factors that influence crash injury severity.

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“…With the help of the automatic vehicle identification and weather detection systems installed in the corridor, the study provided valuable insights on how intelligent transport systems can give more focus on traffic safety improvement and effective traffic management. Dabbour (2017) analysed the effects of various factors on the severity of drivers' injuries in singlevehicle collisions involving light vehicles by using collision data from North Carolina during the period from 2007 to 2013. Factors related to the driver, the roadway, the vehicle, and the environmental conditions were identified, and temporal stability of these variables were also analysed.…”

Section: List Of Tablesmentioning

confidence: 99%

“…The event is coded as a dichotomous outcome variable and its value is dependent on the various explanatory variables (Dabbour 2017). The logit function takes the general form:…”

Section: Logistic Regression Conceptmentioning

confidence: 99%

Investigating the Temporal Trends and Risks Imposed by Different Driver Groups on other Drivers

Philip¹

2023

Preprint

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Multi-vehicle traffic collisions usually result in increased injury severities to the more vulnerable drivers involved in those accidents. This research study aims at investigating the temporal trends and risks imposed by different driver groups on other drivers using logistic regression. The study is based on analysing accident data for all light-duty two-vehicle collisions in North Carolina from January 1, 2004 to December 31, 2013. Two logistic regression models are developed for each year. The first model, evaluates the probability that a certain driver sustains at least a visible injury caused by the other driver and the second model, evaluates the probability that a driver will cause at least a visible injury to the other driver. The findings of this research may help decision makers identify driver groups that are more dangerous to other drivers so that necessary precautionary measures can be adopted to make our roads a safer place.

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Investigating temporal trends in the explanatory variables related to the severity of drivers' injuries in single-vehicle collisions

Cited by 16 publications

References 18 publications

Understanding how drivers are injured in rear-end collisions

Understanding how drivers are injured in rear-end collisions

A comprehensive analysis of factors that influence interstate highway crash severity in Alabama

Investigating the Temporal Trends and Risks Imposed by Different Driver Groups on other Drivers

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