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

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.

Investigating factors affecting injury severity of single-vehicle run-off-road crashes.

This study aimed to identify and investigate the contributing factors influencing injury severity in single-vehicle run-off-road (ROR) crashes, which are known for their high severity. The primary objective was to analyze and compare the impact of these factors across three distinct vehicle classes: passenger cars, sport utility vehicles (SUVs), and pickups. A mixed logit model with heterogeneity in mean and variance was developed to analyze the injury severity outcomes in ROR crashes for the three vehicle classes.

Beyond the surface: Exploring the temporally stable factors influencing injury severities in large-truck crashes using mixed logit models.

An analysis of crash data spanning four years (January 1, 2015, to December 31, 2018) from the State of Washington is conducted to investigate factors influencing injury severity outcomes in large truck-involved crashes. The study utilizes a mixed logit model that accounts for unobserved heterogeneity to capture the variation influenced by other variables. Transferability and temporal stability across the years are assessed using the likelihood ratio test.