Uncovering Individual Heterogeneity in Pedestrian Crash Severity with Mixed Logit Models: A Louisiana Case Study

Abstract

The rising number of pedestrian-vehicle collisions in the U.S. has become a significant safety concern. Pedestrians face a heightened risk of severe injuries because of their immediate exposure to collision forces. This research addresses the critical public health issue of pedestrian-involved collisions by examining factors contributing to injury severity, focusing on Louisiana State. Gathering 5 years of pedestrian crash data (2017–2021), totaling 8,213 unique incidents, the study performed pedestrian injury analysis by using a wide range of variables. The study employed several mixed logit models to assess the impact of contributing factors on pedestrian injury types, with the random parameters logit with heterogeneity in means and variances model identified as the most suitable, based on model performance metrics. The analysis of this model revealed key insights, identifying four random parameters influencing pedestrian crash severity, such as business/residential areas and undivided two-way roads decreasing the probability of fatal crashes. However, factors such as trucks/vans and drivers’ alcohol impairment increased the likelihood of INJ and FSI, respectively. Certain conditions, including darkness with continuous lights on, age group 25–45 years, and vehicles with headlights off, had notable effects on random parameters. By translating these research findings into evidence-based policies, authorities can work toward creating safer pedestrian-friendly environments and reducing the severity of injuries in pedestrian crashes.