Analysis of Injury Severity of Drivers Involved Different Types of Two-Vehicle Crashes Using Random-Parameters Logit Models with Heterogeneity in Means and Variances

Wu, Qiang; Song, Dongdong; Wang, Chenzhu; Chen, Fei; Cheng, Jianchuan; Easa, Said M.; Yang, Yitao; Yang, Wenchen · 2023 · DOAJ

DOI: 10.1155/2023/3399631

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Summary

This study investigates the factors influencing driver injury severity in two-vehicle crashes, specifically addressing how these factors vary across different vehicle combinations: car-car, car-truck, and truck-truck. The research is motivated by the high prevalence of two-vehicle crashes and the significant disparity in injury outcomes between light and heavy vehicles, particularly the vulnerability of passenger car occupants in collisions with trucks. Existing literature often fails to account for unobserved heterogeneity or differentiate between crash types, leading to potential biases. To address this gap, the authors employ advanced random-parameters multinomial logit models with heterogeneity in means and variances (RPLHMV), which allow for more flexible modeling of unobserved heterogeneity compared to traditional fixed-parameter models. The analysis utilizes three years (2016–2018) of crash data from the United Kingdom’s STATS19 database, the most comprehensive publicly available crash dataset in the UK. After merging accident, vehicle, and casualty files and removing unreliable records, the final dataset comprised 8,373 two-vehicle crashes: 4,992 car-car, 2,770 car-truck, and 681 truck-truck incidents. The dependent variable was recoded into two categories: slight injury and severe injury (combining serious injuries and fatalities due to low fatality rates). The models incorporated a wide range of explanatory variables covering driver characteristics (age, gender), vehicle attributes (type, age, maneuver), roadway features (speed limit, road type, junctions), and environmental conditions (weather, lighting, time of day). Likelihood ratio tests confirmed that separate models for each crash type provided a significantly better fit than a combined model, justifying the distinct analysis. The results indicate that unobserved heterogeneity is significant for specific variables, notably young drivers (aged 26–45) in car-car and truck-truck crashes, and the 30 mph speed limit across all three crash types. The study found remarkable variations in how factors impact injury severity depending on the vehicle types involved. Key significant factors included driver age and gender, speeding, sideswipe maneuvers, the presence of junctions, weekday occurrences, unlit conditions, and adverse weather. For instance, the impact of driver age and gender differed across crash types, and the effect of the 30 mph speed limit varied in magnitude and direction depending on the vehicle combination. The models demonstrated good statistical fit, with McFadden R-squared values ranging from 0.374 to 0.478. The significance of this research lies in its detailed differentiation of injury severity determinants across specific crash scenarios, providing more nuanced insights than aggregate analyses. By identifying that the influence of variables like driver age and speed limits is not uniform across all crash types, the findings offer targeted guidance for policymakers. These results can inform the development of specific safety countermeasures and highway safety improvements tailored to the unique risks associated with car-car, car-truck, and truck-truck collisions, ultimately aiming to reduce severe injuries and fatalities.

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Topics

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• sex gender
• demographic disparities
• incidence prevalence
• pre crash contributing factors
• crash typology
• vru crash typology

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: crash risk outcomes
• Methodological Resource: dataset resource