Risk Factors Associated With Crash Injury Severity Involving Trucks

Pulugurtha, Srinivas S.; Duvvuri, Sarvani; Mathew, Sonu · 2022 · ROSA P / Mineta Transportation Institute

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Summary

This study investigates the risk factors associated with crash injury severity involving trucks, addressing a gap in existing literature that often overlooks the influence of surrounding land use and demographic characteristics. Motivated by the high frequency of truck crashes in the United States—nearly 499,000 in 2018, with 22% resulting in fatalities or injuries—and the projected 36% increase in freight volume by 2031, the research aims to identify where and why severe truck crashes occur. By integrating off-network characteristics (land use, demographics) with on-network factors (road geometry, weather) and driver behaviors, the study seeks to provide actionable insights for proactive safety planning and resource allocation. The researchers utilized crash data from Mecklenburg County, North Carolina, spanning the years 2013 to 2017, obtained from the Highway Safety Information System. The dataset was filtered to include various truck types, such as single-unit trucks and truck-trailers. To capture surrounding area influences, parcel-level land use data and traffic analysis zone-level demographic data were joined to the crash records using a 0.50-mile circular buffer around each crash location. The study employed a partial proportionality odds model to analyze injury severity levels, using backward elimination to identify significant independent variables at a 90% confidence level. This methodological approach allowed for the simultaneous evaluation of crash, driver, road, lighting, weather, land use, and demographic variables. The findings reveal that several environmental and operational factors significantly increase the likelihood of severe and moderate injury crashes. Dark lighting conditions, inclement weather (specifically snow, smoke, and fog), and road sections with curves or speed limits exceeding 40 mph were positively associated with higher injury severity. Driver-related factors, including fatigue, impairment, inattention, and non-compliance with speed limits or traffic signals, also contributed to more severe outcomes. Regarding land use, areas with commercial, industrial, and resource land uses showed a positive association with severe crashes, likely due to high trucking activity. Conversely, office land uses were negatively associated with severe injuries. Demographically, areas with higher employment estimates (1,000 to 1,500 employees) exhibited a higher likelihood of severe injury crashes compared to areas with fewer employees. The study concludes that effective geometric design and improved visibility are critical for reducing truck crash severity. The results suggest specific countermeasures, such as variable speed limit signs, dynamic message signs, and advanced driver warning systems, particularly in high-risk areas near commercial and industrial zones. Additionally, the findings support targeted enforcement and education efforts to address driver impairment and inattention. By identifying these specific risk factors, transportation planners can prioritize safety improvements and allocate resources more effectively to mitigate the risks associated with the growing demand for freight transportation.

Key finding

Dark lighting, inclement weather, road curves, driver impairment, and surrounding commercial or industrial land uses are significant risk factors associated with increased injury severity in truck crashes.

Methodology

dataset

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• demographic disparities
• incidence prevalence
• fatality injury trends
• pre crash contributing factors
• induced exposure
• 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