2018Open Journal of Applied SciencesOpen access

Research on the Drunk Driving Traffic Accidents Based on Logistic Regression Model

Shaohua Wang, Yanyan Chen, Jianling Huang, Yuyang Zhou, Yao Lu

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Abstract

Most of the current studies on drunk driving accidents focus on law making and public education. However, especially in China, there is less statistical analysis on the severity of drunk driving accidents between driving under the influence of alcohol (DUI) and driving while intoxicated (DWI). 3368 drunk driving related crashes were collected from the blood-alcohol test report in a city of China at 2012 and 2013. After data pre-processing, Chi-square tests were used to analyze the association between different variables and the type of drunk driving. The logistic regression model is conducted to estimate the effect of the variables under DUI and DWI. The results show that Hour of the day, Driver’s age, Driver’s casualties and Accident area have significant correlation with drunk driving. There was a slightly decrease by 0.995 per year with age and a slightly increase by 1.014 with time in the possibility of DWI. DWI is more likely to cause death in traffic accidents (OR = 1.316) than DUI. Driver’s deaths (OR = 2.346) is more likely to happen than the injuries (OR = 1.910) under DWI cases. These findings show that more attention should be paid to strengthen controls on the DWI. It also can provide important basis for accident prevent, traffic law enforcement and traffic management.

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What this paper is about

Most of the current studies on drunk driving accidents focus on law making and public education. However, especially in China, there is less statistical analysis on the severity of drunk driving accidents between driving under the influence of alcohol (DUI) and driving while intoxicated (DWI). 3368 drunk driving related crashes were collected from the blood-alcohol test report in a city of China at 2012 and 2013. After data pre-processing, Chi-square tests were used to analyze the association between different variables and the type of drunk driving. The logistic regression model is conducted to estimate the effect of the variables under DUI and DWI. The results show that Hour of the day, Driver’s age, Driver’s casualties and Accident area have significant correlation with drunk driving. There was a slightly decrease by 0.995 per year with age and a slightly increase by 1.014 with time in the possibility of DWI. DWI is more likely to cause death in traffic accidents (OR = 1.316) than DUI. Driver’s deaths (OR = 2.346) is more likely to happen than the injuries (OR = 1.910) under DWI cases. These findings show that more attention should be paid to strengthen controls on the DWI. It also can provide important basis for accident prevent, traffic law enforcement and traffic management.

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Available abstract

Most of the current studies on drunk driving accidents focus on law making and public education. However, especially in China, there is less statistical analysis on the severity of drunk driving accidents between driving under the influence of alcohol (DUI) and driving while intoxicated (DWI). 3368 drunk driving related crashes were collected from the blood-alcohol test report in a city of China at 2012 and 2013. After data pre-processing, Chi-square tests were used to analyze the association between different variables and the type of drunk driving. The logistic regression model is conducted to estimate the effect of the variables under DUI and DWI. The results show that Hour of the day, Driver’s age, Driver’s casualties and Accident area have significant correlation with drunk driving. There was a slightly decrease by 0.995 per year with age and a slightly increase by 1.014 with time in the possibility of DWI. DWI is more likely to cause death in traffic accidents (OR = 1.316) than DUI. Driver’s deaths (OR = 2.346) is more likely to happen than the injuries (OR = 1.910) under DWI cases. These findings show that more attention should be paid to strengthen controls on the DWI. It also can provide important basis for accident prevent, traffic law enforcement and traffic management.

Key concepts: Drunk driving, Drunk drivers, Driving under the influence, Law enforcement, Logistic regression, Poison control, Injury prevention, Environmental health

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