Identification of Countermeasures for Unsafe Driving Actions. Volume 2, a Review of Selected Literature

Marks, M. E. (Mary E.); Ruschmann, P. A. (Paul A.); Halstead-Nussloch, R.; Bennett, R. R.; Jones, R. K. (Ralph K.) · 1981 · ROSA P / United States. National Highway Traffic Safety Administration

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Summary

This report, Volume II of a larger study commissioned by the National Highway Traffic Safety Administration (NHTSA), reviews selected literature on decision-making and social control to identify countermeasures for unsafe driving actions (UDAs). Specifically, the study focuses on speeding as the primary target UDA, defined as a conscious and intentional act that increases crash risk. The research aims to understand the psychological and sociological mechanisms behind driver decisions to commit UDAs and to determine how these decisions can be influenced to reduce incidence rates. The review synthesizes theories from behavioral sciences, law, and sociology to provide a scientific foundation for developing deterrence-based countermeasures. The methodology involves a comprehensive review of existing literature rather than primary data collection. The authors analyze decision-making theories, including Expected Value Theory, Utility Theory, and Subjective Probability, alongside social control frameworks such as learning theories, developmental theories, and legal deterrence. The review examines how drivers process risk, assign utility to outcomes, and estimate probabilities. It also explores the role of social control at primary, secondary, and tertiary levels, focusing on how incentives, punishments, and attitude-change techniques influence behavior. The analysis highlights the limitations of rational models in describing actual human behavior, emphasizing the impact of cognitive biases, heuristics, and social factors on driver decision-making. Key findings indicate that drivers do not adhere to rational decision-making models. Instead, they rely on "bounded rationality," using simplified internal world-models to make choices. Drivers often overestimate the utility of unsafe driving, such as the time saved by speeding, while underestimating the disutility of potential accidents or citations. Probability estimation is particularly flawed; drivers use heuristics like the "availability heuristic," judging risk based on how easily instances of crashes or citations come to mind, rather than on objective statistical likelihoods. This leads to significant errors in risk perception, with drivers often overestimating the likelihood of apprehension while underestimating the true probability of crashes. Furthermore, the review notes that drivers are poor at performing "vigilance tasks," struggling to allocate appropriate caution for low-probability, high-cost events like traffic accidents. The significance of this work lies in its implications for designing effective traffic safety countermeasures. The authors conclude that strategies focusing on specific target groups with specific decision problems are supported by the literature. Because drivers rely on subjective utilities and flawed probability estimates, countermeasures must address these cognitive biases. The review suggests that general deterrence efforts should be informed by an understanding of how social control mechanisms, such as enforcement and education, interact with driver psychology. By targeting the specific decision-making processes and social influences that lead to UDAs, policymakers can develop more effective interventions to reduce speeding and other intentional unsafe driving behaviors.

Key finding

Countermeasure strategies focusing on specific target groups with specific decision problems, utilizing incentives, punishments, and attitude-change techniques at secondary and tertiary levels of social control, are supported by the reviewed literature for reducing speeding.

Methodology

review

Provenance

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Topics

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• decision making risk perception
• risk taking
• human error taxonomy
• behavioral adaptation risk compensation
• mental model of traffic
• public messaging

Information type

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• Empirical Findings: observational prevalence
• Theoretical Contribution: theory or model, computational model