Copula-Based Method for Addressing Endogeneity in Models of Severity of Traffic Crash Injuries: Application to Two-Vehicle Crashes

2010 ◽  
Vol 2147 (1) ◽  
pp. 75-87 ◽  
Author(s):  
Tejsingh A. Rana ◽  
Sujan Sikder ◽  
Abdul Rawoof Pinjari
Keyword(s):  
Vehicle Crashes ◽  
Traffic Crash ◽  
Crash Injuries

scholarly journals Minimum Purchasing Age for Alcohol and Traffic Crash Injuries Among 15- to 19-Year-Olds in New Zealand

2006 ◽  
Vol 96 (1) ◽  
pp. 126-131 ◽  
Author(s):  
Kypros Kypri ◽  
Robert B. Voas ◽  
John D. Langley ◽  
Shaun C.R. Stephenson ◽  
Dorothy J. Begg ◽  
...  
Keyword(s):  
New Zealand ◽  
Traffic Crash ◽  
Crash Injuries ◽  
Alcohol And Traffic

Factors Related to More Severe Older Driver Traffic Crash Injuries

2002 ◽  
Vol 128 (3) ◽  
pp. 243-249 ◽  
Author(s):  
Aemal J. Khattak ◽  
Michael D. Pawlovich ◽  
Reginald R. Souleyrette ◽  
Shauna L. Hallmark
Keyword(s):  
Traffic Crash ◽  
Older Driver ◽  
Crash Injuries

Children in Alcohol-Related Motor Vehicle Crashes

PEDIATRICS ◽  
1986 ◽  
Vol 77 (6) ◽  
pp. 870-872
Author(s):  
Lewis H. Margolis ◽  
Jonathan Kotch ◽  
John H. Lacey
Keyword(s):  
North Carolina ◽  
Health Care Providers ◽  
Motor Vehicle ◽  
Motor Vehicle Crashes ◽  
Vehicle Crashes ◽  
Drinking And Driving ◽  
Care Providers ◽  
Traffic Crash ◽  
Crash Data ◽  
Alcohol Control

Review of North Carolina traffic crash data revealed that alcohol use, although associated with 7.9% of motor vehicle crashes involving children, accounted for 15.4% of the motor vehicle-related deaths and 10.4% of the injuries. The largest proportion of these deaths were child passengers in a vehicle in which the driver had been drinking, followed by child passengers in multiple-vehicle crashes in which the other driver had been drinking. The smallest proportion of deaths were child pedestrians. These findings suggest that, in addition to supporting more stringent alcohol control legislation, health care providers should be admonishing parents about the deadly hazards of drinking and driving to the children in their care.

Overloading among crash-involved vehicles in China: identification of factors associated with overloading and crash severity

2018 ◽  
Vol 25 (1) ◽  
pp. 36-46 ◽  
Author(s):  
Guangnan Zhang ◽  
Yanyan Li ◽  
Mark J King ◽  
Qiaoting Zhong
Keyword(s):  
Risk Factors ◽  
Motor Vehicle ◽  
Logistic Models ◽  
Guangdong Province ◽  
Motor Vehicles ◽  
Future Research ◽  
Crash Severity ◽  
Vehicle Crashes ◽  
Traffic Crash ◽  
Factors Associated

ObjectiveMotor vehicle overloading is correlated with the possibility of road crash occurrence and severity. Although overloading of motor vehicles is pervasive in developing nations, few empirical analyses have been performed on factors that might influence the occurrence of overloading. This study aims to address this shortcoming by seeking evidence from several years of crash data from Guangdong province, China.MethodsData on overloading and other factors are extracted for crash-involved vehicles from traffic crash records for 2006–2010 provided by the Traffic Management Bureau in Guangdong province. Logistic regression is applied to identify risk factors for overloading in crash-involved vehicles and within these crashes to identify factors contributing to greater crash severity. Driver, vehicle, road and environmental characteristics and violation types are considered in the regression models. In addition to the basic logistic models, association analysis is employed to identify the potential interactions among different risk factors during fitting the logistic models of overloading and severity.ResultsCrash-involved vehicles driven by males from rural households and in an unsafe condition are more likely to be overloaded and to be involved in higher severity overloaded vehicle crashes. If overloaded vehicles speed, the risk of severe traffic crash casualties increases. Young drivers (aged under 25 years) in mountainous areas are more likely to be involved in higher severity overloaded vehicle crashes.ConclusionsThis study identifies several factors associated with overloading in crash-involved vehicles and with higher severity overloading crashes and provides an important reference for future research on those specific risk factors.

Evaluation of Not-At-Fault Assumption in Quasi-Induced Exposure Method using Traffic Crash Data at Varied Geographical Levels

2019 ◽  
Vol 2673 (4) ◽  
pp. 593-604 ◽  
Author(s):  
Shanshan Zhao ◽  
Kai Wang ◽  
Eric Jackson
Keyword(s):  
Distribution Data ◽  
Good Representation ◽  
Vehicle Crashes ◽  
Chi Square ◽  
Promising Alternative ◽  
Traffic Crash ◽  
Crash Data ◽  
Vehicle Type ◽  
Statistical Support ◽  
Using Data

Acquiring real-world driver distribution data on roadways is a challenge. The quasi-induced exposure (QIE) method is a promising alternative as it only requires the available crash data. The question to be answered through this study is whether the not-at-fault driver assumption of the QIE still holds when the population is broken down to smaller geographical levels, such as counties, towns, or routes. This is important because the result will provide statistical support to choose for or against the application of QIE at disaggregate levels. In this study, the distributions of driver gender, age, and vehicle type between four groups of drivers in the crash data were examined, using data obtained from the state of Connecticut from 2015 to 2017. Namely, they are the not-at-fault drivers and at-fault drivers in two-vehicle crashes (NF2 and AF2) and the not-at-fault drivers and at-fault drivers in three-or-more vehicle crashes (NF3 and AF3). Chi-square tests and Wilcoxon Mann–Whitney tests were used to provide statistical evidence of whether the driver groups come from the same population. The evidence shows that there are no statistical differences between the distributions of NF2 and NF3. The QIE assumption of not-at-fault drivers is valid at all tested geographical levels. Driver characteristic distribution in the NF2 (and NF3) groups in the crash data should be a good representation of the driving population. The results also revealed the similarities of distributions between AF2 and AF3 and the significant differences between the not-at-fault drivers (NF2 and NF3) and at-fault-drivers (AF2 and AF3).

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