Learning about injury severity from no-injury crashes: A random parameters with heterogeneity in means and variances approach. (March 2023)

• Record Type: Journal Article
• Authors: Adanu, Emmanuel Kofi; Powell, Lawrence; Jones, Steven; Smith, Randy
• Is Part Of: Accident analysis and prevention. Volume 181(2023)
• Publication Date: 2023-03
• Subjects: Vehicle damage -- Crash severity -- Random parameters -- Unobserved heterogeneity; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2022.106952
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Factors that contribute to injury severity are also likely to contribute to no-injury crashes. Random parameters logit models have been used to investigate factors that are associated with vehicle damage severity in no-injury crashes. No-injury crashes provide a deeper knowledge into injury-related crashes. The extent of vehicle damage in no-injury crashes can be used to understand differences in no-injury crashes and how they relate to injury-related crashes. Abstract: The traditional approach to injury-severity analyses does not allow in-depth understanding of no-injury crashes, as crash factors found to contribute to the various injury severities may have similar effects on the severity of vehicle damage even if no injury is recorded. Viewing no-injury crashes using the vehicle damage severities as sub-categories and bases for potential injuries can improve understanding of future injury crashes. To better understand the mechanism of no-injury crashes and the crash factors that contribute to the extent of vehicle damage beyond the single categorization of these crashes in injury severity analysis, this study presents a vehicle damage severity analysis for no-injury crashes. To compare the effects of crash contributing factors on crash outcomes, two injury severity models were also estimated. Random parameters multinomial logit models with heterogeneity in means and variances were developed to account for unobserved heterogeneity. Model estimation results revealed that several common factors (e.g., unsafe speed, distracted driving, driving under influence, vehicle age, and run-off-road) are correlated with both injury severity in injury crashes and vehicle damage severity in no-injury crashes. Therefore, the sub-categorization of no-injury crashes by vehicle damage severity can potentially improve estimates of injury severity considered in resource allocation decisions for traffic safety.