Factors Involved in Fatal Vehicle Crashes

NHTSA · 2010 · ROSA P / United States. Department of Transportation. Bureau of Transportation Statistics

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Summary

This technical report analyzes the factors contributing to fatal motor vehicle crashes in the United States, aiming to identify common precursors to improve safety strategies. The study utilizes data from the National Highway Traffic Safety Administration’s (NHTSA) Fatality Analysis Reporting System (FARS) for the years 2004–2008. The analysis categorizes crashes by size: single-vehicle, two-vehicle, and multi-vehicle (three or more). Single-vehicle crashes accounted for 58.5% of all fatal incidents during this period, followed by two-vehicle crashes at 34.9% and multi-vehicle crashes at 6.5%. The authors employed principal component analysis to determine the relative weight of various factors influencing these fatalities, distinguishing between transportation-related factors (infrastructure, weather) and human-related factors (behavior, error). The findings indicate that transportation-related factors generally carry more weight than human-related ones in predicting fatal crashes. Across all crash sizes, the functionality of traffic controls was the most significant factor. However, the specific importance of other variables varied by crash type. For single-vehicle crashes, the primary factors were traffic controls and crash characteristics, such as the first harmful event (e.g., rollover or striking a barrier). In two-vehicle crashes, traffic controls and roadway characteristics, such as alignment and surface type, were dominant. Multi-vehicle crashes were primarily influenced by road characteristics, vehicle speed, and road type. Specific data revealed that 86.3% of single-vehicle fatal crashes occurred in areas with no traffic controls, whereas this figure was lower for multi-vehicle crashes (70.9%). Additionally, a high proportion of fatal crashes occurred on state highways and in 55 mph zones. Most crashes took place on straight, level roadways paved with bituminous material, though single-vehicle crashes were more likely to occur on curves than multi-vehicle crashes. Human-related factors, specifically the number of persons involved and drunk driving combined with light conditions, were less influential in the statistical model but remained critical. The majority of fatal crashes resulted in only one fatality. While most crashes occurred during daylight hours without alcohol involvement, single-vehicle crashes after dark were more likely to involve a drunk driver than not. In contrast, two- and multi-vehicle crashes were less likely to involve alcohol. The report notes that human-related variables are often underreported in FARS data due to difficulties in quantifying behaviors like distraction, which limits their statistical weight compared to concrete infrastructure data. The study concludes that while transportation infrastructure plays a major role in crash severity, further analysis is needed to fully understand the impact of human behavior. The authors suggest that examining higher-ranked factors, such as roadway characteristics, could inform strategies to reduce fatalities. However, they emphasize that improving data collection on human factors is essential for a complete understanding of crash precursors, as current databases may underrepresent behavioral contributions.

Key finding

Traffic controls and speed/route type were the most influential factors in fatal crashes, with single-vehicle crashes accounting for 58.5 percent of all fatal incidents between 2004 and 2008.

Methodology

dataset

Sample size: 109936

Provenance

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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.

• pre crash contributing factors
• incidence prevalence
• motorcycle crash typology
• crash typology
• comparative international
• demographic disparities

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