Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour
DOI: 10.1016/j.amar.2019.02.001
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study addresses the limitation of existing car-following models, which primarily account for traffic conditions while neglecting the significant impact of driver emotional states, particularly stress, on acceleration-deceleration decisions. Motivated by the need to improve the behavioral realism and safety evaluation capabilities of traffic microsimulation tools, the authors propose a novel framework that explicitly incorporates driver stress as a latent variable. The research bridges engineering-based models with human-factor approaches by quantifying the relative impact of stress on car-following behavior alongside traditional traffic variables and sociodemographic characteristics. The methodology utilizes data from a driving simulator experiment conducted at the University of Leeds. Thirty-six participants drove in a motorway setting while subjected to induced stressors, including aggressive surrounding vehicles, slow-moving traffic, and time pressure indicated by dashboard emojis. Driving behavior was recorded at 60Hz, and physiological stress indicators—heart rate, blood volume pulse, and electrodermal activity (skin conductance)—were collected continuously using a non-intrusive Empatica E4 wristband. These physiological signals were processed into standardized indicators using moving windows. The authors developed an extension of the Gazis-Herman-Revzin (GHR) stimulus-response car-following model, treating stress as an unobserved latent variable indicated by the physiological data. The model parameters were estimated using Maximum Likelihood techniques, comparing specifications with and without sociodemographic variables and latent stress. The results demonstrate that car-following behavior is significantly influenced by stress levels in addition to speed, headway, and driver characteristics. The hybrid model successfully integrated physiological measurements to quantify the latent stress effect, confirming that stress alters acceleration-deceleration decisions independently of traffic conditions. The study found that accounting for these human factors provides a more accurate representation of driver heterogeneity than models relying solely on traffic variables or statistical noise terms. The significance of this work lies in its contribution to more behaviorally realistic traffic simulation tools. By validating the use of physiological sensors to measure stress and incorporating it into car-following models, the research offers a pathway to improve the fidelity of microsimulation software. These findings have direct implications for safety analysis and the design of interventions, as they allow for a better understanding of how emotional states affect driving decisions under various traffic scenarios.
Key finding
Car-following behavior is significantly influenced by driver stress, as quantified through physiological indicators, in addition to standard traffic conditions and driver characteristics.
Methodology
simulator
Sample size: 36
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-06 |
| archive | success | semantic_scholar | — | — | 6 | 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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
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).
- Empirical Findings: physiological data, behavioral performance data
- Theoretical Contribution: computational model