Abstract
Pedestrian safety is becoming a global concern and an understanding of the contributing factors to severe pedestrian crashes is crucial. This study analyzed crash data for San Antonio, TX, over a six-year period to understand the effects of pedestrian–vehicle crash-related variables on pedestrian injury severity based on the party at fault and to identify high-risk locations. Bivariate analysis and logistic regression were used to identify the most significant predictors of severe pedestrian crashes. High-risk locations were identified through heat maps and hotspot analysis. A failure to yield the right of way and driver inattention were the primary contributing factors to pedestrian–vehicle crashes. Fatal and incapacitating injury risk increased substantially when the pedestrian was at fault. The strongest predictors of severe pedestrian injury include the lighting condition, the road class, the speed limit, traffic control, collision type, the age of the pedestrian, and the gender of the pedestrian. The downtown area had the highest crash density, but crash severity hotspots were identified outside of the downtown area. Resource allocation to high-risk locations, a reduction in the speed limit, an upgrade of the lighting facilities in high pedestrian activity areas, educational campaigns for targeted audiences, the implementation of more crosswalks, pedestrian refuge islands, raised medians, and the use of leading pedestrian interval and hybrid beacons are recommended.
Highlights
Pedestrian–motor vehicle crashes are a common global occurrence [1] and every year millions of people get injured or killed from these crashes [2]
(−0.63) for all pedestrians in Table 8 implies that a change from dark lighting conditions to daylight conditions decreases the log odds of a severe pedestrian injury by 0.63, and the significance of the variable expressed in the form of asterisks indicates that daylight lighting conditions significantly reduce severe pedestrian injury crashes
The odds ratio is determined from the regression coefficient estimate and represents the strength of the association of a predictor variable with severe pedestrian crashes
Summary
Pedestrian–motor vehicle crashes are a common global occurrence [1] and every year millions of people get injured or killed from these crashes [2]. Pedestrians are fragile and usually travel at a much slower speed compared to motor vehicles, putting themselves at a disadvantage when a crash occurs compared to drivers or vehicle occupants, and they are much more susceptible to severe injuries and fatalities from crashes [3]. With the increase in automobile usage and the current trends in accommodating pedestrians and bicyclists on city streets, vulnerable road users (bicyclists, pedestrians, and motorcyclists) are expected to become more susceptible to traffic crashes, especially in places where traffic laws are poorly enforced [1]. An understanding of the effects of crash-related variables (human/temporal/roadway/environmental) on pedestrian injury severity is of great importance in the process of robust countermeasure planning.
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