Modeling Passing Behavior on Two-Lane Rural Highways: Evaluating Crash Risk under Different Geometric Condition
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This study investigates passing behavior on rural two-lane highways to evaluate crash risk and quality of service under varying geometric conditions. The research addresses a critical gap in transportation engineering: existing standards for passing sight distance (PSD) and highway capacity models, such as the Highway Capacity Manual, lack empirical relationships between driver perception, passing decisions, and highway geometry. Previous field studies were limited by a lack of control over variables and an inability to capture driver perceptions. Consequently, the authors aimed to develop data-driven models that quantify how drivers manage crash risk, specifically examining the influence of oncoming traffic density and road geometry on passing decisions. The methodology employed a mixed factorial design combining field data validation and driving simulation. Field data were collected via video footage from a two-mile segment of US 95 near Ferdinand, Idaho, and used to calibrate the CORSIM traffic simulation model. The primary experimental component utilized the University of Idaho’s National Advanced Driving Simulator (NADS) MiniSim. Twenty-four licensed participants, divided into experienced and inexperienced groups, drove a 50-mile simulated route. The experiment manipulated two within-subject factors: road geometry (straight/level vs. horizontal/vertical curves) and passing zone gap size (0.25 to 1 mile). One between-subject factor was oncoming traffic density between passing zones (high: 5.5 vehicles/minute; low: 2.75 vehicles/minute). Performance measures included vehicle speed, lane position, passing frequency, and time-to-collision (TTC). The results demonstrated that both oncoming traffic density and road geometry significantly influence passing behavior. Higher oncoming traffic density reduced the frequency of passing maneuvers, particularly at shorter gap distances, suggesting a "calming" effect where drivers adjust their risk thresholds based on traffic expectations. Conversely, drivers in low-density conditions were more likely to execute risky passes with shorter TTCs. Regarding geometry, straight and level roadways increased the likelihood of passing compared to curved segments, even when sight distance was not obstructed. However, passes on straight segments were executed at lower maximum speeds. Simulation data validated against field observations showed that simulator behaviors were representative of real-world driving patterns. The study concludes that current highway capacity models fail to account for the strategic and tactical factors influencing driver passing decisions. The findings imply that incorporating traffic density and geometric context into macrosimulation models could improve the prediction of highway performance and safety. Specifically, the "calming" effect of high traffic density and the increased passing propensity on straight geometries suggest that quality of service is not solely determined by sight distance but also by driver perception of traffic flow and road alignment. These insights provide a foundation for refining PSD standards and pavement marking guidelines to better align with actual driver behavior and risk management.
Key finding
Higher oncoming traffic density reduces passing frequency and increases safety, while lower density and straight road geometry encourage riskier passing maneuvers with shorter time-to-collision.
Methodology
mixed_methods
Sample size: 24
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.
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: crash risk outcomes, behavioral performance data
- Methodological Resource: dataset resource