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Analysis and Modeling of Motor Vehicle Crashes Involving Air Force Military Personnel
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The primary purpose of this thesis was to improve the data analysis of Motor Vehicle Crashes (MVCs). This thesis employed the Air Force Safety Center (AFSC) data, collected over 20 years, of MVCs in which US Air Force (USAF) military personnel are involved when off duty and off base. Categorical Data Analysis and Analysis of Variance (ANOVA) were applied to identify risk factors related to MVCs and influence the severity of injuries and those factors associated with the alcohol consumption before driving, and affect the number of lost workdays resulting from MVCs. Categorical Data Analysis sho...
The primary purpose of this thesis was to improve the data analysis of Motor Vehicle Crashes (MVCs). This thesis employed the Air Force Safety Center (AFSC) data, collected over 20 years, of MVCs in which US Air Force (USAF) military personnel are involved when off duty and off base. Categorical Data Analysis and Analysis of Variance (ANOVA) were applied to identify risk factors related to MVCs and influence the severity of injuries and those factors associated with the alcohol consumption before driving, and affect the number of lost workdays resulting from MVCs. Categorical Data Analysis showed that male USAF members aged 17-24 years or with the rank of Airman were more prone to experience a fatal MVC. Moreover, fatal MVCs peaked between the hours of 2200 pm to 0559 am, and USAF female drivers seemed to wear seatbelts more than USAF male drivers. These thesis results revealed the value of wearing seatbelts for the prevention of severe injuries during crashes. Finally, ANOVA results exposed that the more severe the Type of Injury, the greater the number of Lost Days and that 2-wheeled vehicle MVCs have the most significant effect on the number of Lost Days.
