Techniques for Predicting High-Risk Drivers for Alcohol Countermeasures. Volume 2, User Manual

Council, Forrest M.; Lacey, John H.; Stewart, J. Richard · 1978 · ROSA P / United States. National Highway Traffic Safety Administration

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This document, Volume II of a 1978 report by the University of North Carolina Highway Safety Research Center for the National Highway Traffic Safety Administration (NHTSA), provides a user manual for predicting high-risk drivers involved in alcohol-related crashes. The research addresses the challenge of efficiently identifying specific drivers with elevated crash probabilities to target countermeasures, rather than applying broad interventions to the general population. The study was motivated by the need to help program administrators select cost-effective treatments for individuals or small groups, while managing the statistical problem of "false positives"—drivers incorrectly identified as high-risk. The methodology relies on statistical prediction models developed in the companion Volume I report, based on North Carolina driving records, accident data, and demographic information. Six specific high-risk subpopulations were identified: males aged 16–20, males aged 21–24, recently divorced persons, recently released prisoners, drivers convicted of driving under the influence (DUI), and drivers with three or more traffic violations in five years. The manual provides probability tables that estimate the likelihood of an alcohol-related crash within one year for individuals within these groups, using inputs such as age, sex, marital status, and prior driving behavior. The manual outlines a comprehensive framework for implementing countermeasures. It catalogs twelve potential treatment programs, ranging from warning letters and driver improvement clinics to license suspension and chemotherapy. A key component is an economic analysis methodology that calculates the Net Discounted Present Value of program costs versus benefits, where benefits are defined as dollar savings from prevented fatal, injury, and property damage crashes. This allows administrators to determine if a specific countermeasure is cost-effective for a given driver profile. The manual includes computational forms and a computer program to facilitate this analysis. Additionally, it provides guidelines for evaluating trial programs, recommending modified before/after designs with control groups to assess effectiveness. The significance of this work lies in its provision of a structured decision-making tool for highway safety administrators. It acknowledges that while prediction models inevitably produce false positives, they offer a more accurate identification method than simplistic criteria like prior DUI convictions alone. The authors emphasize that the accuracy of the economic analysis depends on the availability of data regarding countermeasure effectiveness, noting significant gaps in current knowledge. Consequently, the manual urges administrators to conduct rigorous evaluations of implemented programs to refine these models and improve the cost-effectiveness of alcohol countermeasures in the future.

Key finding

The manual provides a structured methodology combining predictive models for six high-risk driver groups with an economic analysis tool to help administrators determine whether the predicted reduction in alcohol-related crashes justifies the cost of specific countermeasures.

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.

Topics

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

• induced exposure
• driver education effectiveness
• incidence prevalence
• telematics crash prediction
• exposure measurement
• regulatory evaluation

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).

• Applied Guidance: countermeasure evaluation
• Empirical Findings: crash risk outcomes
• Theoretical Contribution: computational model