Using fixed-parameter and random-parameter ordered regression models to identify significant factors that affect the severity of drivers’ injuries in vehicle-train collisions

Dabbour, Essam; Easa, Said M.; Haider, Murtaza

doi:10.1016/j.aap.2017.07.017

Cited by 21 publications

(10 citation statements)

References 40 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: 66%

“…Furthermore, Table 2 shows that female drivers are more probable to be injured when compared to their male counterparts. This finding may also be explained by the difference in body size and structure (Dabbour et al, 2017;Haleem and Gan, 2015;Zhao and Khattak, 2015). However, the age and gender of the striking driver are not significant factors related to the severity of a rear-end collision.…”

Section: Resultsmentioning

confidence: 97%

“…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: 66%

Section: Resultsmentioning

confidence: 97%

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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“…Ordinal regression has been widely used to address related transportation issues. Dabbour et al [25] applied ordinal regression to determine the key factors affecting drivers' injury severity resulting from collision accidents between motor vehicles and trains. ey developed fixedparameter and random-parameter ordered regression models based on data acquired over a period of 15 years.…”

Section: Regression Approachmentioning

confidence: 99%

Driver Preference and Level of Trust Related to Real-Time Road Freezing Risk Information

Khedher

Yang

Kim

et al. 2020

Journal of Advanced Transportation

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Bad weather conditions can affect normal driving by substantially hindering visibility. Among all adverse weather conditions, road freezing is probably the most dangerous to drivers because slippery roads reduce surface friction and can lead to loss of vehicle control. This paper evaluates driver preference of receiving real-time road freezing risk information and explores the factors that would most influence drivers’ trust in a future road freezing information service. A survey was conducted in the metropolitan areas of South Korea during January and February 2019. The survey was completed by 231 driver’s license holders of 18 years or older, and the results were used for statistical analysis. According to the survey results, the variable message sign (VMS) is a very important system from the perspective of public benefit. Car-navigation systems are preferred for age categories of 21∼30 and over 50. In addition, ordinal regression was used to analyze the causal relationship between the level of trust regarding road freezing risk information and its controlling factors. The ordered log odds of drivers with previous accident experience due to slippery roads exhibit a higher level of trust in road freezing risk information because the coefficient is positive. Moreover, drivers with a constant commute time show a lower level of trust in road freezing risk information. These findings provide a foundation for planning the scope of future road freezing risk information service, as well as the specific service targets and type of information, especially during the winter season.

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“…Another paper employing the ordered regression framework with fixed and random coefficients for accident severity analysis is worth mentioning, since it deals with the problem of parameter temporal stability [19]. In particular, the authors investigated the significance and stability of the factors affecting drivers’ injury severity when road vehicles collide with trains at railroad‐grade crossings over a period of 15 years in the USA.…”

Section: Introductionmentioning

confidence: 99%