Potential Uses of Reduced Datasets from the Roadway Information Database: A White Paper
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Summary
This white paper addresses the need to make the Strategic Highway Research Program 2 (SHRP2) Roadway Information Database (RID) more accessible for highway safety research. While the RID was originally designed to link with Naturalistic Driving Study data, its high accuracy and coverage make it a powerful stand-alone resource for analyzing crash frequency and severity relative to roadway geometry and traffic characteristics. However, using the full RID requires advanced Geographic Information System (GIS) expertise. To lower this barrier, the Federal Highway Administration (FHWA) developed "reduced datasets"—simplified, pre-processed data layers that allow users with minimal programming skills to conduct robust safety analyses. The paper aims to describe the potential research applications of five new reduced datasets: average annual daily traffic (AADT), intersection widths, intersection crashes, curve crashes, and homogenous segments. The document details the construction and utility of these datasets, which integrate mobile-collected mobile data with supplemental sources like historical crash records and traffic counts. The RID covers 12,500 centerline-miles of detailed mobile data and 200,000 miles of compiled existing data across six states, ensuring uniformity and accuracy that is difficult to achieve through traditional state-specific data requests. The new reduced datasets streamline complex data integration tasks. For instance, the AADT dataset consolidates inconsistent traffic data into a single layer per segment; the intersection and curve crash datasets merge historical crash data with precise geometric features using standardized spatial thresholds (e.g., 250 feet for curves); and the intersection width dataset provides longitudinal extents for intersections and access points. These datasets are designed to be used independently or in combination with previously developed RID reduced datasets, such as those for speed limits and lane configurations. The paper identifies seven specific areas where these reduced datasets can advance highway safety research. These include crash prediction, assessing the safety performance impacts of horizontal curve features (such as radius and superelevation), evaluating intersection features (including width and signal timing implications), analyzing access management impacts, identifying risk factors for systemic safety analysis, improving crash assignment accuracy, and studying driver awareness at signalized intersections when entering urban areas from rural zones. By providing consistent, multi-state data, the RID allows researchers to develop directly comparable datasets and reduce omitted variable bias in safety models. The significance of this work lies in its potential to influence policies and practices that reduce traffic fatalities and serious injuries. Approximately 50 percent of U.S. traffic fatalities are associated with horizontal curves or intersections, areas where the RID provides historically unavailable high-quality data. By making these data accessible to a broader audience, including novice GIS users, the FHWA aims to increase the application of RID data in research. This accessibility enables researchers to focus on expanding the breadth of their questions rather than spending resources on data assembly, ultimately supporting better design criteria and safety performance functions for highway infrastructure.
Key finding
The FHWA developed five new reduced datasets from the SHRP2 Roadway Information Database to provide standardized, high-quality data for seven specific highway safety research areas, thereby reducing processing time and enabling robust multi-state comparisons.
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.
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Information type
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- Empirical Findings: crash risk outcomes, observational prevalence
- Methodological Resource: dataset resource