Mapping crash types across work zone functional areas: A context-aware predictive framework.

OBJECTIVE: Highway work zones introduce spatial and operational disruptions that elevate crash risk. Although prior research has examined work zone crashes at an aggregated level, less attention has been given to how crash types vary across functional work zone areas (Advance Warning, Transition, Activity, and Termination), where operating conditions and risk differ by segment. This study examines crash-type differentiation conditional on crash occurrence. METHODS: Using a multi-state work zone crash dataset of 20,617 records, segment-specific multinomial logistic regression classifiers were developed to classify five dominant crash types (rear-end, sideswipe, run-off-road, head-on/front, and rollover/overturn) and to identify the environmental and temporal factors associated with crash-type differentiation within each segment. RESULTS: Segment-specific models showed moderate and stable classification performance, with overall accuracies ranging from 0.74 to 0.77 across the four work-zone segments. Fold-level cross-validation summaries and state-specific testing were conducted to further assess model robustness. Crash-type-specific risk-factor assessment revealed that lighting and surface conditions consistently influenced crash patterns across segments, while weather, season, and time of day exerted segment- and crash-type-dependent effects. CONCLUSIONS: Segment-specific crash-type classification provides a context-aware basis for understanding heterogeneous crash mechanisms within work zones. The identified segment- and crash-type-dependent risk profiles support the design of targeted countermeasures (e.g., visibility enhancement, surface treatments, and time-responsive traffic control) to improve work zone safety management.