Evaluating Driver Performance on Rural Two-Lane Horizontal Curved Roadways Using a Driving Simulator
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Summary
This study addresses the high rate of single-vehicle run-off-road (ROR) fatalities on rural two-lane horizontal curves, which averaged 4,748 deaths annually between 2005 and 2009. Crash investigations identify driver inattention, driving in a hurry, and familiarity with the roadway as critical precrash factors that significantly increase crash odds. The research aims to evaluate methods for eliciting these specific conditions in a driving simulator to determine if they produce measurable changes in driving behavior, thereby validating the simulator as a tool for assessing engineering countermeasures. The experimental design utilized a MiniSim™ driving simulator featuring a rural two-lane route with nine unique horizontal curves repeated in both directions. Fourteen participants drove the test route multiple times to establish familiarity, defined as completing seven runs. To elicit inattention, participants performed secondary cognitive tasks: mental mathematics problems or noun classification. To elicit time pressure ("in a hurry"), participants were subjected to conditions with and without visual travel time feedback. Performance was measured using mean average speed (MAS), mean standard deviation of lane position (MSDLP), and subjective workload assessments via the NASA Task Load Index. The results demonstrated that the method for eliciting familiarity—repeated exposure to the route—produced significant behavioral changes compared to unfamiliar driving. Regarding inattention, solving mental mathematics problems was more effective than noun classification in producing driving behaviors associated with cognitive distraction. Both methods for inducing time pressure, whether with or without visual feedback, successfully generated noticeable changes in behavior compared to baseline driving without time constraints. Specifically, drivers under time pressure exhibited increased speeds and altered lane-keeping patterns, consistent with the expected speed-accuracy trade-offs. The study concludes that driving simulators can effectively replicate the critical precrash conditions of familiarity, inattention, and haste. The validated elicitation methods provide a reliable framework for future research into driver performance on rural curves. These findings support the use of simulation environments to evaluate engineering countermeasures aimed at reducing ROR crashes, offering a safe and controlled setting to study human factors that contribute to roadway departures.
Key finding
Mental mathematics problems were more effective than noun classification for inducing inattention, and repeated driving significantly changed behavior compared to unfamiliar conditions.
Methodology
simulator
Sample size: 14
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. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 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.
- simulator validity fidelity
- traffic density
- mental demand
- perceptual countermeasures
- cognitive capacity variation
- temporal
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: behavioral performance data
- Methodological Resource: validation psychometrics
- Theoretical Contribution: computational model