Temporal dynamics in barrier-involved crashes: Determining shifts in driving behavior and injury risk across multi-year periods

Barrier-involved crashes are shaped by evolving behavioral patterns, roadway conditions, and systemic changes in transportation environments. This study investigates temporal shifts in the factors influencing injury severity in barrier-related traffic crashes over three multi-year periods: 2017–2018, 2019–2020, and 2021–2022. A Multinomial Logit (MNL) model and its extensions, including the Random Parameter Logit (RPL), Random Parameter Logit with Heterogeneity in Means (RPLHM), Correlated Random Parameter Logit with Heterogeneity in Means (CRPLHM), and Random Parameter Logit with Heterogeneity in Means and Variance (RPLHMV), are employed to capture unobserved heterogeneity and temporal changes in predictor effects. The analysis reveals that contextual factors such as roadway alignment, posted speed limits, weather, lighting, and vehicle type exhibit time-varying associations with crash outcomes. Moderate speed zones (35–55 mph), previously not significant, became increasingly associated with lower severity in later periods. Meanwhile, the influence of lane departure, marked lanes, positive median barriers, and collisions with fixed objects also varied across periods. Notably, crashes outside intersections that initially corresponded with lower injury risk began to show elevated severity in the later years. Driver failure to maintain a single lane consistently emerged as a strong predictor of severity, with its effect intensifying over time. These findings highlight the importance of capturing temporal instability in crash determinants and support the need for adaptive, evidence-driven safety policies that reflect shifting roadway and behavioral dynamics.