Decoding Pedestrian Severity at Crosswalks using Hybrid Clustering and Random Parameter Models

Pedestrian crashes at crosswalks represent a critical safety concern due to their disproportionate contribution to severe injuries and fatalities relative to their overall frequency. This study analyzes pedestrian-involved crosswalk crashes reported in the Texas Crash Records Information System (CRIS) from 2017 to 2022 to examine context-specific determinants of injury severity. A two-stage analytical framework was employed. First, Cluster Correspondence Analysis (CCA) identified three distinct crash environments: (1) intersection crashes associated with turn-phase right-of-way violations, (2) low-speed yield-phase property-damage-only collisions at intersections, and (3) distraction-related driveway departure crashes at non-intersections. Each cluster exhibited unique combinations of movement patterns, roadway characteristics, and behavioral attributes. Second, cluster-specific Random Parameter Logit with Heterogeneity in Means (RPLHM) and Multinomial Logit (MNL) models were estimated to evaluate injury severity determinants within each context. Results indicate that weather, lighting conditions, roadway functional type, posted speed limits, and inattentive driving significantly influence severity outcomes, with effects varying across clusters. Several parameters, including daylight conditions and undivided roadway configurations, demonstrated substantial unobserved heterogeneity, suggesting context-dependent risk shifts influenced by roadway environment and demographic factors. These findings highlight the limitations of uniform countermeasures and support cluster-specific, context-sensitive interventions to mitigate pedestrian injury severity at crosswalks.