Analysis of Pedestrian–Motor Vehicle Crashes in San Antonio, Texas

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.

Analysis of Bicycle-Motor Vehicle Crashes in San Antonio, Texas

“…Nighttime during weekend period had a substantially high severe injury risk, probably due to the higher frequency of DWI and distracted drivers [33], encouraging the use of ridesharing services during this period. Bicyclists exhibited reduced faulty behavior during the weekend, whereas the proportion of faulty motor vehicle drivers of San Antonio was greater during the weekend [44].…”

“…where Y1, Y2, Y3, Y4, and Y5 represent the total number of crashes involving a fatality, serious injury, non-serious injury, possible injury, and no injury, respectively. Bicycle-motor vehicle crash-associated variables selected for statistical analyses (Table 1) were based on a literature review [12,30,33,39,40,[42][43][44]55] to study their effect on the severity of bicycle crashes as stand-alone variables and in conjunction with other. Bivariate analysis is utilized as an exploratory tool for hypothesis of the association test between a dependent and an independent variable, and it was used in this study to explore the relationships between the bicyclist injury severity and bicycle-motor vehicle crash-associated variables (as standalone variables).…”

“…However, studies focused on the effect of bicycle facilities (e.g., bicycle lanes, curb lanes, shared lane arrows, and road signage), as well as the effect of combination of these strategies, are still limited [6]. Previous studies found several factors to be significantly affecting bicyclist injury severity resulting from bicycle-motor vehicle crashes such as bicyclist race and gender, bicyclist old age, speeding, alcohol influence of driver/bicyclist, use of helmet, lighting condition, day of week, road type, and presence of intersection [12,33,[39][40][41][42][43][44].…”

Analysis of Crash Severity of Texas Two Lane Rural Roads Using Solar Altitude Angle Based Lighting Condition

“…Two lane rural roads crash records (about 55,700 crash cases) of the state of Texas collected over a two year period (2017-2018), provided by the Texas Department of Transportation (TxDOT), are used in this paper [39,40]. According to the necessary information for the calculation of the SAA provided by police and other supplemental sources, the crash dataset is classified to four lighting conditions, namely, daylight, dark, dark lit and twilight.…”

Applying Association Rules Mining to Investigate Pedestrian Fatal and Injury Crash Patterns Under Different Lighting Conditions

“…According to the literature, pedestrian crashes and their severity are influenced by several crash variables (12). Some of these variables are related to pedestrian demographics (age, gender, alcohol use, actions, primary contributing factors), driver demographics (age, gender, condition, violation type), roadway features (number of lanes, posted speed limits, intersection presence, functional classification), and other factors such as land-use pattern, day of the week, weather, and lighting condition (13)(14)(15)(16). Ferguson et al reported that poor lighting conditions combined with alcohol intake by pedestrians and high speed limits on limited-access routes were found to greatly exacerbate the risk of pedestrian injury severity (17).…”