Modeling heterogeneity in fault attribution of Pedestrian-Vehicle crashes using a Random parameter Binary Logit...

Crashes between pedestrians and motor vehicles remain a significant traffic safety problem, especially in urban areas. This study aims to analyze factors that systematically influence whether the driver or the pedestrian is considered at fault in crashes, affecting them in terms of road, vehicle, environmental, temporal, and behavioral factors. The study uses data from 7,213 crashes that occurred in Istanbul between 2022 and 2023, where fault was attributed solely to the pedestrian or solely to the driver. First, a Binary Logit Model was created as a benchmark model. Subsequently, a Random Parameter Binary Logit Model was constructed using the Likelihood Ratio test to identify heterogeneity not observed in fixed-parameter models. As a result, "intersection", "traffic sign", "traffic lights", "adverse weather", "public transport", and "the number of vehicles" were determined as random parameters. Crashes occurring at intersections, in areas with traffic signs, in multi-vehicle crashes, and in adverse weather conditions increase the likelihood of attributing fault to the driver, while crashes at traffic lights and involving public transport vehicles increase the likelihood of attributing fault to pedestrians. These findings suggest that interventions addressing different road user-related and behavioral risks, rather than uniform safety measures, are more effective.