Tri-level Study of the Causes of Traffic Accidents. Volume 1, Causal Factor Tabulations and Assessments

Treat, John R.; Tumbas, Nicholas S.; Shinar, David; Hume, Rex D.; Mayer, R. E.; Stansifer, Rickey L.; Castellan, N. J.; McDonald, S.T. · 1977 · ROSA P / United States. National Highway Traffic Safety Administration

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Summary

This paper presents the final report of the "Tri-Level Study of the Causes of Traffic Accidents," a comprehensive research project conducted by the Institute for Research in Public Safety at Indiana University for the National Highway Traffic Safety Administration (NHTSA). The study aimed to identify and quantify the causal factors—human, vehicular, and environmental—contributing to motor vehicle accidents. The research sought to determine the relative frequency of these factors, assess the relationship between driver attributes (such as vision, knowledge, and psychological makeup) and accident involvement, and develop methodologies for assessing human factors in causation. The study employed a tri-level data collection methodology in Monroe County, Indiana, spanning from August 1972 to March 1977. Level A involved collecting baseline data on police reports, drivers, vehicles, and roadways. Level B consisted of on-scene investigations conducted by technicians immediately after accidents occurred, totaling 2,258 investigations across Phases II through V. Level C involved intensive, multidisciplinary in-depth examinations of 429 of these accidents. The researchers utilized a structured causal factor rating system to assign causes as definite, probable, or possible, analyzing data through tabulations, trend analyses, and comparisons between on-site and in-depth assessments. The findings indicate that human factors were the predominant cause of accidents, cited as probable causes in 92.6% of the in-depth investigations. The major human direct causes included improper lookout, excessive speed, inattention, improper evasive action, and internal distraction. Environmental factors were identified as probable causes in 33.8% of accidents, with view obstructions and slick roads being leading contributors. Vehicular factors accounted for 12.6% of probable causes, primarily due to brake failure, inadequate tread depth, side-to-side brake imbalance, under-inflation, and vehicle-related vision obstructions. Additionally, the study found that poor dynamic visual acuity and poor personal or social adjustment were related to accident involvement, whereas driver knowledge of the driving task showed no significant relationship to causation. The significance of this study lies in its detailed quantification of accident causation, providing empirical evidence that human error is the primary driver of traffic accidents, often interacting with environmental and vehicular conditions. The results offer critical insights for traffic safety interventions, highlighting the need for improved driver training regarding lookout and attention, better roadway design to mitigate view obstructions, and stricter vehicle maintenance standards. Furthermore, the study’s methodology for distinguishing between on-site and in-depth causal assessments contributes to the field of crash investigation by validating the reliability of different data collection levels and refining the understanding of how specific driver attributes correlate with crash risk.

Key finding

Human factors were identified as probable causes in 92.6% of investigated accidents, with improper lookout, excessive speed, and inattention being the most frequent direct causes.

Methodology

on_road

Sample size: 2258

Provenance

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

Topics

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• pre crash contributing factors
• causation analyses
• incidence prevalence
• naturalistic crash near crash
• crash typology
• human error taxonomy

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