How Gender Affects Motor Vehicle Crashes: A Case Study from San Antonio, Texas

Khondoker Billah, Hatim O. Sharif and Samer Dessouky
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Khondoker Billah: School of Civil and Environmental Engineering and Construction Management, University of Texas at San Antonio, San Antonio, TX 78249, USA
Hatim O. Sharif: Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
Samer Dessouky: Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA

Sustainability, 2022, vol. 14, issue 12, 1-21

Abstract: Traffic crashes are among the leading causes of injuries and fatalities worldwide. The main assumption of this study is that traffic crash rates, injury severity, and driving behaviors differ by the driver’s gender. Utilizing ten years (2011–2020) of data from the Texas Crash Record and Information System database, this study investigates how some of the most prominent driving behaviors leading to crashes and severe injuries (distracted driving, speeding, lane departure, and driving under influence) vary by gender in San Antonio, Texas. The spatial distribution of crashes associated with these driving behaviors by gender is also investigated, as well as the influence of some environmental and temporal variables on crash frequency and injury severity. This study adopted bivariate analysis and logistic regression modeling to identify the effect of different variables on crash occurrence and severity by gender. Male drivers were more likely to be involved in a speeding/DUI/lane departure-related crash and subsequent severe injuries. However, female drivers were slightly more associated with distracted-driving crashes and subsequent injuries. Nighttime, interstate/highway roads, the weekend period, and divider/marked lanes as the primary traffic control significantly increased the crash and injury risk of male drivers. Driving behavior-related crashes were mostly concentrated on some interstate road segments, major intersections, and interchanges. The results from this study can be used by authorities and policy-makers to prioritize the use of limited resources, and to run more effective education campaigns to a targeted audience.

Keywords: motor vehicle crashes; distracted driving; speeding; logistic regression; bivariate analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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