Impact assessment of contributing factors in direct-impact work zone crashes using heterogeneity and...

Work zone intrusion crashes, where vehicles enter active work areas and strike workers or equipment, remain a critical safety concern with high potential for severe outcomes. This study analyzes five years of Texas crash data (2020-2024) to identify the roadway, environmental, driver, vehicle, and crash dynamics influencing injury severity in these events. Using both multinomial logit (MNL) and advanced Random Parameters Logit (RPL) models with heterogeneity in means and variances, this study accounts for unobserved heterogeneity and evaluate temporal stability across individual and pooled years. The analysis incorporates a partially constrained estimation approach, which allows selected parameters to remain fixed across time periods while permitting others to vary, balancing statistical efficiency with flexibility in capturing year-to-year differences. Results indicate that environmental conditions such as cloudy or rainy weather and nighttime lighting affect crash severity, potentially due to reduced visibility or altered driver behavior. Roadway context, including intersection type, ramp versus main lane location, and rural versus urban settings, shows distinct temporal patterns, highlighting the importance of context-specific countermeasures. Driver and vehicle attributes are also critical, with male drivers, younger drivers, and heavier vehicle types such as pickups and SUVs more frequently associated with fatal or serious injury outcomes, while some passenger car crashes are linked to reduced severity. Incorporating unobserved heterogeneity and partial constraint improved model fit and revealed risk factors that standard models overlooked. Temporal stability testing demonstrated that certain effects shift over time, underscoring the need for continuous monitoring. These findings provide targeted, evidence-based strategies for engineering, enforcement, and education to reduce injury severity in work zone intrusions, while demonstrating the value of advanced, partially constrained modeling for safety policy and decision-making.