Examination of Recent Pedestrian Safety Patterns at Intersections through Crash Data Analysis

Autor: Dania Ammar, Yueru Xu, Bochen Jia, Shan Bao
Rok vydání: 2022
Předmět:
Zdroj: Transportation Research Record: Journal of the Transportation Research Board. 2676:331-341
ISSN: 2169-4052
0361-1981
Popis: Pedestrians are the most vulnerable road users and are at risk of severe consequences when involved in traffic accidents. The purpose of this research is to determine the factors that have significant impacts on the increasing likelihood of pedestrians being seriously injured or killed when involved in a collision with a single vehicle at an intersection over a recent 6-year period. Both 2013–2015 General Estimates System (GES) and 2016–2018 Crash Report Sampling System (CRSS) crash data were used in the analysis. Logistic regression models for the two crash datasets showed that there were four common significant variables affecting pedestrians’ injury levels. The following pairwise comparisons of these common significant factors using the Wald chi-square statistic test showed similar log-odds with few exceptions, suggesting that these affecting factors share similar effects from 2013 through 2018. In both datasets, results showed that a high likelihood of pedestrians’ severe injuries was associated with pedestrians older than 25, dark lighting conditions, light trucks and buses, and vehicles’ straight maneuver. Furthermore, the GES data distinguished further factors imposing higher threats on pedestrians as being drivers’ 19–25 age group, speeding, pedestrians’ roadway crossings maneuvers, and rain conditions. Crashes that occurred at intersections with more than two lanes or during summertime had significantly higher odds of resulting in severe injuries for pedestrians than crashes at two-lane intersections or during wintertime, respectively, in the CRSS dataset. Results of this study contribute to a better understanding of the recent changes in pedestrian safety at intersections and potential countermeasure design suggestions.
Databáze: OpenAIRE