Driving Performance in a Simulator as a Function of Pavement and Shoulder Width, Edge Line Presence, and Oncoming Traffic
DOI: 10.17077/drivingassessment.1186
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Summary
This paper addresses the application of driving simulation to traffic engineering problems, specifically evaluating how roadway geometric designs affect driver performance on rural two-lane roads. The research was motivated by high crash rates in Texas, where two-lane rural roads account for a significant portion of fatalities, particularly single-vehicle run-off-the-road crashes. Preliminary data indicated that roadways with paved surface widths of 20 feet or less experienced higher fatal and serious injury crash rates compared to those with widths of 24 feet or greater. The study aimed to determine how travel lane width, shoulder width, edge line presence, and oncoming traffic influence driver errors, while also assessing the validity and limitations of using simulators for such evaluations. The experimental design utilized the Texas Transportation Institute’s DriveSafety™ fixed-base driving simulator, featuring a full-size 1995 Saturn SL vehicle and a 150-degree field of view. Eight roadway geometries were developed, varying in pavement and shoulder widths, with and without edge lines. Each condition included a 200-meter tangent segment and a 600-meter segment with horizontal curves. Thirty-six volunteer drivers, predominantly young males to reflect crash demographics, participated in the study. To mitigate simulator sickness, which had been a significant issue in prior studies, the researchers implemented several procedural changes: removing the vehicle’s windshield to equalize luminance, using filler segments with attention-checking questions, engaging in light conversation, and eliminating hard braking scenarios. The paper does not present specific statistical results regarding driver performance metrics, referring readers to a separate technical report for those findings. Instead, it focuses on methodological lessons learned. The authors reported a dramatic reduction in simulator sickness, attributing this success to the procedural modifications mentioned above, as well as potential pre-screening of participants. However, significant limitations in the simulator’s visual rendering were identified. The perspective required for 3-D rendering on flat screens exaggerated shoulder widths, making it difficult for participants to distinguish between two- and four-foot shoulders. Additionally, traffic engineering colleagues criticized the simulator’s lack of verisimilitude, citing issues such as "pop-up" objects on the horizon, inaccurate traffic signs, and the inability to render nighttime scenes, which are critical for studying rural crashes. The significance of this work lies in its demonstration of the challenges and potential of using driving simulators for traffic engineering research. While simulators offer a safe, replicable environment for studying driver behavior, their acceptance by traffic engineers depends heavily on visual accuracy and adherence to design standards. The authors conclude that while current simulators may struggle with geometric fidelity, they remain valuable tools for other traffic operations studies, such as evaluating changeable message signs and traffic signal operations, where the driving task serves as a background load rather than the primary variable of interest.
Key finding
The study demonstrates that driving simulators can be effectively used to evaluate roadway geometric designs, though researchers must address challenges related to simulator sickness, visual rendering limitations, and data interpretation to ensure validity.
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. Discovered via openalex_abstract on 2026-05-08 (2 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | openalex | — | — | 3 | 2026-05-08 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| 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.
- simulator validity fidelity
- perceptual countermeasures
- road geometry
- simulator sickness
- simulator training transfer
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: observational prevalence
- Methodological Resource: validation psychometrics, tool software