Exploring Naturalistic Driving Study (NDS) Data and Roadway Information Database (RID) for Emerging Applications in Traffic Safety
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 addresses the underutilized potential of linking the Naturalistic Driving Study (NDS) data with the Roadway Information Database (RID) to advance traffic safety research. While the NDS provides extensive data on driver behavior during typical commutes, and the RID offers detailed geometric and environmental characteristics of the roads where these drives occurred, few studies have effectively integrated these two datasets. The authors aim to explore the RID dataset, demonstrate its linkage to NDS data, and identify emerging research applications that combine driver behavior with roadway design factors. The methodology involved a thorough exploration of the RID dataset, which was compiled by the Center for Transportation Research and Education at Iowa State University using mobile data collection vehicles. The RID contains detailed information on roadway geometry, features, and environmental conditions for over 25,000 centerline miles across six study sites. To demonstrate the linkage process, the researchers used a representative sample from Tampa, Florida. Since purchasing the full NDS dataset was outside the project scope, the team created a "PseudoNDS" dataset using coordinate points to simulate NDS event locations. They utilized ArcGIS software to map the RID data and employed the "Near" tool to align the PseudoNDS points with the correct road geometry, correcting for GPS misalignment by calculating proximity and updating coordinates. The results confirmed that the RID database is highly compatible with NDS data for spatial analysis. The researchers successfully mapped various roadway attributes—including lane widths, signage, guardrails, curvature, and lighting—onto the geospatial network. They demonstrated that using ArcGIS tools, such as Street View and Bird’s Eye View add-ins, allows for visual verification of roadway conditions at specific event locations. The alignment process effectively corrected positional errors, ensuring that driver behavior data could be accurately associated with specific roadway segments and features. The significance of this work lies in establishing a framework for future research that integrates human factors with roadway infrastructure. The authors propose several potential applications, including quantifying the effect of warning and regulatory signs on driver compliance, measuring the effectiveness of safety campaigns, and examining statistically significant clusters of crashes and near-crashes in relation to roadway conditions. By bridging the gap between driver behavior data and roadway geometry, this study enables more comprehensive safety analyses that account for both human performance and environmental design, offering new avenues for improving traffic safety countermeasures.
Key finding
The study demonstrates that NDS driver behavior data can be successfully linked to RID roadway geometric data using ArcGIS alignment tools to enable integrated safety analysis.
Methodology
dataset
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
- perceptual countermeasures
- external distraction
- exposure measurement
- urban rural setting
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