The use of a driving simulator to determine how time pressures impact driver aggressiveness.

Knodler, Michael A.; Samuel, Siby; Fitzpatrick, Cole · 2017 · ROSA P / Safety Research Using Simulation (SAFER-SIM) University Transportation Center

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Summary

This study investigates how time pressures influence driver aggressiveness and speed choices, addressing the gap between self-reported survey data and quantifiable driving performance. Motivated by the fact that nearly one-third of fatal crashes in the United States are speeding-related and that "being late" is the most common reason drivers cite for speeding, the research aims to determine if hurried driving induces risky behaviors beyond simple speed increases. The authors hypothesized that drivers subjected to time pressures would select higher speeds and exhibit more aggressive maneuvers, such as accepting smaller gaps and running yellow lights, compared to unhurried drivers. The researchers employed a between-subjects experimental design using a full-cab driving simulator at the University of Massachusetts Amherst. Thirty-six licensed drivers were recruited and divided into three groups: a control group, a "Hurried" group, and a "Very Hurried" group. The control group drove a 14–16 minute virtual route on a rural two-lane road with no time constraints. The experimental groups were given financial incentives to complete the drive within specific time limits calibrated from the control group’s performance: the Hurried group had a goal based on the 15th percentile completion time (approx. 16 minutes), while the Very Hurried group had a stricter goal based on the 85th percentile (approx. 14 minutes). Progress updates displayed elapsed time to simulate real-time pressure. The study measured speed at five checkpoints, acceleration after red lights, gap acceptance during unprotected left turns, willingness to pass a slow-moving vehicle, and behavior in dilemma zones where lights turned yellow. The results demonstrated that time pressure significantly increased aggressive driving behaviors, particularly in the Very Hurried group. Compared to the control group, Very Hurried drivers selected statistically higher speeds after the first progress update, accelerated faster after red lights (mean acceleration of 1.963 ft/sec² vs. 1.579 ft/sec² for controls), and accepted significantly smaller gaps when making left turns (mean accepted gap of 6.7 seconds vs. 8.5 seconds for controls). Additionally, Very Hurried drivers were significantly more likely to pass a slow-moving truck (45% vs. 8.3% for controls) and run yellow lights in dilemma zones (68% vs. 38% for controls). The Hurried group exhibited similar trends—higher speeds, faster acceleration, and smaller gap acceptance—but these differences were not statistically significant, likely due to the smaller sample size. The study concludes that time pressures not only increase driver speeds but also trigger subconscious, risky decision-making, such as aggressive passing and intersection violations. These findings have practical implications for transportation safety and funding. The authors suggest that projects aimed at reducing congestion may also yield safety benefits by alleviating the time pressures that drive aggressive behavior. Furthermore, the results highlight potential risks in commercial motor vehicle operations, where financial incentives tied to delivery times may encourage unsafe driving practices. The study underscores the need for further research into how incentive structures and real-time navigation tools impact driver risk tolerance.

Key finding

Very Hurried drivers selected higher speeds, accelerated faster after red lights, accepted smaller gaps on left turns, and were more likely to pass slow vehicles and run yellow lights compared to the control group.

Methodology

simulator

Sample size: 36

Provenance

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

Topics

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• stress driving
• aggressive driving
• anger road rage
• traffic density
• following distance

Information type

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• Empirical Findings: behavioral performance data
• Methodological Resource: validation psychometrics
• Theoretical Contribution: computational model