Virtual Road Safety Audits: Recommended Procedures for Using Driving Simulation and Technology to Expand Existing Practices
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Summary
This report addresses the limitations of traditional reactive safety monitoring, which relies on crash history to identify hazardous locations. Because crashes are rare events, this approach often leaves unsafe conditions unaddressed for long periods. To mitigate this, the authors propose Virtual Road Safety Audits (VRSAs), a proactive method that uses driving simulation technology to evaluate driver behavior and identify safety issues in existing or proposed designs before crashes occur. The primary motivation is to move beyond qualitative field observations and design plan reviews, which lack objective behavioral data, toward a controlled laboratory environment where specific driver responses can be measured and analyzed. The methodology involves converting the geometry and operational characteristics of a roadway site into a 3D virtual scenario for use in driving simulators or dynamic surveys. The report outlines a recommended workflow for conducting a VRSA, beginning with site selection. Traditionally, sites are selected based on crash data or user feedback; however, the authors introduce an alternative objective approach using radar-based data collection tools to measure surrogate safety measures, specifically Post-Encroachment Time (PET). Once a site is identified, the process involves creating a 3D model from 2D drawings and conducting experiments with human subjects. The fidelity of the simulation can range from low-fidelity single-screen setups to high-fidelity multi-screen driving simulators, depending on project complexity, budget, and timeline. The report details the procedural steps for scenario creation, including the integration of traffic control devices and the configuration of data collection instruments such as eye-tracking equipment. The findings demonstrate that VRSAs provide detailed, objective measurements of driver behavior that are not accessible through traditional Road Safety Audits (RSAs). By exposing multiple subjects to identical virtual conditions, engineers can isolate specific factors contributing to safety problems, such as confusion regarding traffic signals or geometric design flaws. The report presents an example of a VRSA conducted on a proposed interchange design, highlighting how eye-tracking and performance data can reveal behavioral insights. Additionally, the study illustrates the use of PET values derived from radar data to objectively identify candidate sites for VRSA, offering a more systematic alternative to relying solely on historical crash records. The results confirm that virtual environments allow for the rapid and inexpensive testing of design modifications and countermeasures. The significance of this work lies in its potential to transform safety evaluation from a reactive to a proactive discipline. By providing a framework for VRSAs, the report enables transportation engineers to identify and mitigate safety risks before construction or before crashes occur. The ability to quantify risk through surrogate safety measures and detailed behavioral data supports more informed decision-making regarding design improvements. The authors conclude that while cost feasibility must be considered, the marginal costs of VRSAs can be justified by the potential for significant safety improvements. This approach expands the utility of existing RSA practices by incorporating objective, data-driven insights into driver behavior, ultimately enhancing the safety of the transportation network.
Key finding
Virtual Road Safety Audits enable engineers to objectively characterize potential safety problems and identify corresponding solutions by measuring detailed driver behaviors in controlled simulation environments.
Methodology
mixed_methods
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.
- naturalistic crash near crash
- induced exposure
- simulator validity fidelity
- crash reconstruction hf
- incidence prevalence
- exposure measurement
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
- Methodological Resource: tool software, validation psychometrics