Modelling the effects of stress on gap-acceptance decisions combining data from driving simulator and physiological sensors
DOI: 10.1016/j.trf.2018.09.019
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Summary
This study addresses the gap in driving behavior research regarding the quantification of how stress influences driver decision-making. While previous studies have linked stress to risky driving, they largely relied on self-reported surveys prone to bias or focused merely on detecting stress via physiological sensors without modeling its impact on specific maneuvers. The authors aim to develop gap-acceptance models that explicitly incorporate objective physiological measures of stress to understand how acute stress affects a driver’s willingness to accept gaps in traffic when crossing an intersection. The research utilized data from the University of Leeds Driving Simulator, involving 41 participants who completed an urban driving scenario. To induce stress, participants were subjected to time pressure in the second of two identical gap-acceptance tasks, signaled by a dashboard emoji indicating they were running late. Physiological stress levels were continuously monitored using Empatica E4 wristbands, measuring Electrodermal Activity (EDA) and heart rate. The experimental design presented drivers with a sequence of ten gaps of varying sizes, recording whether they accepted or rejected each gap. Discrete choice models were developed to link the accept-reject decisions to driver demographics, traffic conditions, and the continuous physiological stress indicators. The results demonstrate that increased stress levels significantly increase the probability of a driver accepting a gap. Statistical analysis revealed that the mean size of accepted gaps was significantly smaller under time pressure compared to the baseline condition without time pressure. Specifically, six participants who rejected all gaps in the low-stress condition accepted a gap when stressed, and those who accepted gaps in both conditions tended to accept shorter gaps under stress. The discrete choice models confirmed that physiological indicators of stress are strong predictors of gap-acceptance behavior, improving model fit compared to models excluding these variables. The significance of this work lies in its integration of objective physiological data into behavioral modeling, offering a more reliable alternative to self-reported stress measures. By quantifying the link between stress and risky gap-acceptance decisions, the findings provide evidence that stress leads to more aggressive driving behaviors. These insights are critical for road safety research and can inform the design of intervention strategies, such as in-vehicle systems that detect driver stress and adjust assistance or warnings to mitigate safety risks.
Key finding
Increased stress levels significantly increase the probabilities of drivers accepting gaps in traffic.
Methodology
simulator
Sample size: 41
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-06 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-06-06 |
| promote | success | — | — | — | 1 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Empirical Findings: physiological data, behavioral performance data
- Theoretical Contribution: computational model