Analysis of Vehicle Lateral Position in Curves Using a Driving Simulator: Road Markings, Human Factors and Road Features
DOI: 10.3390/app15179851
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 investigates the influence of traffic-calming measures, human factors, and road geometry on vehicle lateral position (LP) in curved sections of rural two-lane roads. Motivated by high crash rates on curves in Spain, particularly those involving road departures and head-on collisions, the research aims to identify interventions that reduce hazardous lateral deviations. The study specifically evaluates three road marking treatments: checkerboard patterns (CBP), red peripheral transverse bars (PTB), and red-coloured transverse bands (CTB). The experiment utilized a fixed-base driving simulator with 48 volunteer drivers. The simulated road featured ten curves with varying radii (26 m to 190 m) and turning directions, separated by 500 m tangents. Drivers navigated four scenarios: a baseline without measures and three scenarios incorporating the respective traffic-calming markings. Data analysis focused on two primary indicators: mean lateral position (LP), measuring distance from the lane center, and standard deviation of lateral position (SDLP), indicating trajectory stability. Driver characteristics, including gender, age, annual driving exposure, and additional license possession, were also analyzed. Results indicated significant variations based on driver demographics and road features. Male drivers positioned their vehicles further from the lane center compared to female drivers, who exhibited lower SDLP values, indicating less weaving. Older drivers adopted more centered trajectories, though their SDLP increased with age. Drivers with higher annual exposure tended to drive further from the lane center. Regarding road geometry, larger curve radii were associated with lower SDLP values. Among the interventions, red-coloured transverse bands (CTB) reduced lateral position by approximately 0.12 m in left-hand curves. Red peripheral transverse bars (PTB) were the most effective measure for reducing lateral variability (SDLP). Checkerboard patterns showed less consistent impact on lateral positioning compared to the other measures. The findings suggest that specific road markings can effectively modify driver behavior to enhance safety on curves. PTB markings are particularly beneficial for stabilizing vehicle trajectories, while CTB markings help center vehicles in left curves. The study highlights the importance of considering driver demographics and road geometry when designing traffic-calming measures. These insights contribute to road safety strategies by providing evidence-based recommendations for reducing curve-related crashes through targeted infrastructure improvements.
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 | 2026-08-09 |
| archive | success | openalex | — | — | 5 | 2026-08-09 |
| extract | success | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; 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: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: computational model