Task acceptability and workload of driving city streets, rural roads, and expressways : ratings from video clips.

Schweitzer, Jason; Green, Paul A. · 2007 · ROSA P / University of Michigan. Transportation Research Institute

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Summary

This study addresses the challenge of developing quantitative models for driver workload and task acceptability to support the design of real-time workload managers in vehicles. Motivated by the need to mitigate driver distraction from secondary tasks like phone use and navigation entry, the research aims to establish predictive equations that estimate driving workload based on road characteristics, traffic conditions, and driver demographics. The work is part of the SAVE-IT project, which seeks to create adaptive interface technologies that determine when it is safe for drivers to engage in non-driving activities. The methodology involved subjects rating the workload of video clips depicting various driving scenarios, including city streets, rural roads, and expressways under different Levels of Service (LOS) A, C, and E. Participants rated workload on a 1-to-10 scale, anchored by light and heavy traffic clips, and indicated their willingness to perform three secondary tasks: dialing a phone, tuning a radio, or entering a destination. Additionally, subjects provided post-test ratings for described situations not shown in clips and assessed the relative contribution of factors such as road geometry, traffic, visibility, and traction to overall workload. The researchers used logistic regression to model task refusal and linear regression to relate workload ratings to real-time driving statistics derived from an Advanced Collision Avoidance System (ACAS) field operational test dataset. Key findings include the development of predictive equations for task refusal based on workload, driver age, and sex, revealing that older drivers and females were generally less willing to engage in secondary tasks at lower workload levels compared to younger males. The study identified that mean workload ratings varied significantly by road type and traffic density, with urban intersections and heavy traffic (LOS E) yielding the highest workload scores. A robust regression model accounting for 87% of the variance in workload ratings was derived using real-time data: Mean Workload Rating = 8.87 - 3.01(LogMeanRange) + 0.48(MeanTrafficCount) + 2.05(MeanLongitudinalAcceleration). Furthermore, post-test ratings indicated that traffic, visibility, and road geometry contributed approximately 29%, 28%, and 17% respectively to total workload, while traction contributed 28%. The significance of this research lies in providing the empirical basis and specific mathematical formulations necessary for implementing real-time workload managers. The results demonstrate that workload can be estimated using real-time sensor data (such as radar-derived range and traffic counts), look-up tables based on clip ratings, or post-test situational adjustments. By quantifying the relationship between driving conditions and driver willingness to perform secondary tasks, the study offers a practical framework for vehicle systems to dynamically restrict or permit in-vehicle interactions, thereby enhancing safety by preventing distraction during high-workload driving scenarios.

Key finding

Logistic regression models accurately predicted the probability of drivers refusing secondary tasks based on workload ratings, age, and sex, while real-time driving statistics explained 87% of the variance in subjective workload ratings.

Methodology

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Provenance

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

Topics

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• mental demand
• traffic density
• road complexity
• stress driving
• workload measurement
• urban rural setting

Information type

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• Empirical Findings: physiological data
• Methodological Resource: metric or index
• Theoretical Contribution: theory or model