Usage Guidelines of SHRP 2 Naturalistic Driving Study Data for Nevada
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Summary
This report establishes usage guidelines for the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS) data, specifically tailored for researchers and transportation agencies in Nevada. The study addresses the challenge of applying national naturalistic driving data to local traffic safety analysis. Because the SHRP 2 NDS collected data from only six specific U.S. sites, local agencies must determine which site’s data best matches their regional conditions to ensure valid safety insights. The report aims to answer three core questions: how to select appropriate NDS data for local analysis, how to navigate the complex data request process, and how to apply the data for traffic safety research. The methodology involves developing a decision procedure to match local study areas with the most comparable NDS site. The authors identified eight key site attributes for comparison: geographic characteristics, population, education attainment, household income, weather, traffic safety laws, median driver age, and historical crash data. Using Reno and Las Vegas as case studies, the team collected background data for these attributes and compared them against the six NDS sites (Bloomington, IN; Buffalo, NY; Durham, NC; Seattle, WA; State College, PA; and Tampa, FL). A decision matrix was constructed using weight values derived from a survey of transportation professionals via the Institute of Transportation Engineers (ITE) web forum. The report also details the administrative requirements for data access, including Institutional Review Board (IRB) approvals and the three-tiered security levels for data handling managed by the Virginia Tech Transportation Institute (VTTI). The findings identify Bloomington, Indiana, as the most comparable NDS site for Reno, and Seattle, Washington, as the best match for Las Vegas, based on the weighted attribute rankings. The report provides a comprehensive guide to the data request process, outlining steps from proposal submission to data receipt, including cost considerations and security protocols for personally identifiable information (PII). Additionally, the authors demonstrate the application of NDS data through several traffic safety analyses, including assessing factors influencing pedestrian-turning crashes, multi-vehicle intersection crashes, lane departures, and pre-crash events. These examples illustrate how microscopic data on driver behavior and environmental conditions can be used to develop surrogate measures for crash risk. The significance of this work lies in providing a replicable framework for state and local transportation agencies to leverage high-resolution naturalistic driving data for local safety improvements. By offering a structured method for site selection and a clear roadmap for data acquisition, the report lowers the barrier to entry for using SHRP 2 NDS data. It emphasizes that while the data is not publicly available due to privacy concerns, the outlined procedures enable researchers to obtain and apply these detailed datasets to understand the microscopic interactions between drivers, vehicles, roadways, and the environment, ultimately supporting more targeted traffic safety countermeasures.
Key finding
Bloomington, Indiana was identified as the most comparable NDS site for Reno, while Seattle, Washington was determined to be the best match for Las Vegas based on weighted attribute rankings.
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 | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- naturalistic crash near crash
- incidence prevalence
- exposure measurement
- induced exposure
- urban rural setting
- sex gender
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, observational prevalence
- Methodological Resource: dataset resource