Spatiotemporal analysis of roadway terrains impact on large truck driver injury severity outcomes using random parameters with heterogeneity in means and variances approach

• Published in: Accident analysis and prevention 2025-02, Vol.210, p.107849, Article 107849
• Main Authors: Habib, Muhammad Faisal, Alnawmasi, Nawaf, Motuba, Diomo, Huang, Ying
• Format: Article
• Language: English
• Subjects: Accidents, Traffic - statistics & numerical data; Adult; Automobile Driving - statistics & numerical data; California - epidemiology; Driver-Injury Severity; Female; Humans; Large Trucks Safety; Logistic Models; Male; Middle Aged; Motor Vehicles - statistics & numerical data; Partially Temporally Constrained Modeling; Spatio-Temporal Analysis; Spatiotemporal Analysis; Temporal Instability; Trauma Severity Indices; Truck Drivers; Unobserved Heterogeneity; Wounds and Injuries - epidemiology
• ISSN: 0001-4575; 1879-2057; 1879-2057
• DOI: 10.1016/j.aap.2024.107849

•Spatio-temporal analysis explored terrain impacts and yearly variations in large truck crash severity across flat, rolling, and mountainous areas.•Partially constrained temporal modeling was employed to test for shifts in crash outcomes over time.•Simulation revealed more injuries when flat terrain parameters were replaced with those from rolling or mountainous terrains.•Stable factors influencing crash severity over time were identified, highlighting consistent risks across different terrains.•The findings emphasized the need for terrain-specific safety interventions to mitigate severe injuries in large truck crashes. This study employs a partially temporally constrained modeling approach to examine spatiotemporal variations in driver injury severity in single-vehicle large truck crashes across different terrains in California, allowing for a nuanced understanding of how specific factors influencing injury outcomes may change over time. Utilizing crash data from January 1st, 2015, to December 31st, 2017, obtained from the Highway Safety Information System, this study categorizes terrains as flat, rolling, and mountainous terrain and employs a random parameter multinomial logit model with heterogeneity in means and variance to account for potential heterogeneity in crash injury outcomes. This approach helps understand how different terrains influence injury severities while allowing for parameter variability across observations. The analysis is further enriched by likelihood ratio tests to verify the stability and temporal transferability of the model estimates across different terrains and years. Notably, the study identifies truck overturning as the first and second event in a crash as a consistent parameter influencing injury severity across all years, emphasizing its importance regardless of terrain or time in single-vehicle large truck crashes. Furthermore, this study takes into account a wide range of variables, including driver characteristics, crash attributes, roadway characteristics, vehicle features, and environmental and temporal aspects. The findings highlight the importance of terrain-specific elements in traffic safety assessments and the need for focused measures to reduce serious injuries in truck crashes. The out-of-sample simulation revealed a significant increase in minor and severe injuries when flat terrain parameters were replaced with those from rolling or mountainous terrains. This research not only contributes to the existing lit