An Analysis of Drivers Most Responsible for Fatal Accidents versus a Control Sample [1975]

NHTSA · 1975 · ROSA P / United States. Department of Transportation. National Highway Traffic Safety Administration

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Summary

This 1975 report by the Boston University Traffic Accident Research Special Study Team, sponsored by the National Highway Traffic Safety Administration, investigates the psychosocial and behavioral characteristics of drivers deemed "most responsible" for fatal highway accidents. The primary research question was to determine how these high-risk drivers differed from the general population and to identify predictive variables for future fatal accident involvement. To address this, the study compared an experimental sample of 267 drivers involved in fatal crashes against a matched control sample of 801 drivers with no history of fatal accident responsibility. The experimental sample was collected sequentially between September 1971 and February 1974 in the greater Boston area. These drivers were categorized into three accident types: Type I (103 drivers, 38%), where the driver was killed; Type II (63 drivers, 24%), where the driver survived but another occupant died; and Type III (101 drivers, 38%), where the driver struck and killed a pedestrian. The sample was also divided by alcohol involvement: 103 drivers (39%) had alcohol involvement, while 164 (61%) did not. The control sample, collected in early 1975, was randomly selected from the same geographic area and matched to the experimental group by sex, age, and residential township to ensure comparability. Data were gathered through extensive interviews with multiple informants for the experimental group and single interviews for the control group, covering variables such as education, occupation, alcohol and drug use, and legal history. The findings revealed distinct profiles for each group. Type I operators were typically single, Irish, high school-educated males in their thirties with histories of alcohol-related problems. Type II operators were younger, less educated, and employed in lower-status jobs, with marked histories of heavy use of alcohol, marijuana, and other drugs. Type III operators resembled the general population more closely but still occupied a middle ground between the high-risk groups and controls. Drivers involved in alcohol-related accidents were the least accomplished, with lower educational and occupational attainment and significant histories of inappropriate alcohol use. In contrast, non-alcohol-involved experimental drivers were slightly better educated and employed, with fewer antisocial behaviors. Control operators were the most educated and employed, though they exhibited heavier drinking patterns than non-alcohol-involved experimental drivers but lighter patterns than alcohol-involved ones. Discriminant function analysis identified key variables that differentiated high-risk drivers from the general population. The most significant predictors included previous arrests for driving under the influence (DWI) and speeding, specific alcohol use patterns, levels of education, and occupational status. These variables formed the basis of a "Boston Predictive Formula" designed to identify potentially high-risk operators from the general population. The study concludes that specific psychosocial and behavioral markers, particularly legal infractions and substance use histories, can effectively distinguish drivers likely to be involved in fatal accidents, offering a framework for pre-identification and targeted safety interventions.

Key finding

Discriminant function analysis identified previous arrests for DWI and speeding, alcohol use patterns, education, and occupation as the most significant variables for differentiating high-risk fatal accident operators from a matched control sample.

Methodology

field_study

Sample size: 1068

Provenance

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

Topics

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• sex gender
• demographic disparities
• incidence prevalence
• induced exposure
• pre crash contributing factors

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, observational prevalence
• Methodological Resource: dataset resource