Development of Speed Crash Modification Factors (CMFs) Using SHRP2 Roadway Information Database (RID), Volume I: Final Report
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Summary
This research addresses the gap in the Highway Safety Manual (HSM) regarding the direct impact of operating speed on crash frequency and severity. While speed is a critical safety factor, existing HSM crash prediction models largely ignore direct speed metrics, relying instead on traffic volume and geometric characteristics. The study aims to develop speed-related Crash Modification Factors (CMFs) for 12 roadway facility types, including rural highways, urban/suburban arterials, and freeways, to improve the precision of safety predictions. The researchers utilized a data-driven safety analysis approach, integrating three major databases for Washington and North Carolina: the SHRP2 Roadway Information Database (RID) for geometric data, the National Performance Management Research Dataset (NPMRDS) for operating speeds, and the Highway Safety Information System (HSIS) for crash records. The methodology involved conflating the RID and NPMRDS networks, assigning crash data to these segments, and selecting specific speed measures. The team excluded facility types with insufficient speed data coverage, such as ramps and local roads. Crash data were categorized by severity (fatal, injury, property damage) and type (single-vehicle, multiple-vehicle). The analysis focused on two primary speed metrics: speed variation (standard deviation of operating speed) and speed differential (difference between posted and average operating speed). The results indicate that the dominant speed measure varies by facility type. For rural highways and freeways, speed variation was the primary predictor of crashes, whereas speed differential was dominant for urban and suburban arterials. In most cases, higher speed variation or differential was positively associated with increased crash frequencies. The researchers developed CMFs for 129 crash types and severity levels, with most achieving three-star ratings in the CMF Clearinghouse, indicating high quality. Validation using Mean Absolute Deviation (MAD), Root Mean Squared Error (RMSE), and Cumulative Residuals (CURE) plots demonstrated that incorporating these speed CMFs significantly improved model precision compared to HSM defaults that excluded speed factors. The significance of this work lies in providing a validated framework for integrating speed data into safety performance functions. The findings suggest that including speed-related CMFs enhances the accuracy of crash prediction models, offering better tools for roadway designers and safety professionals. Although the study focused on Washington and North Carolina, the documented methodology allows other jurisdictions to develop similar speed CMFs if required data are available. This research supports more nuanced speed management strategies by quantifying the specific safety impacts of speed variability and differentials across diverse roadway environments.
Key finding
Inclusion of speed-related crash modification factors improves crash prediction precision for various roadway facility types, with speed variation dominating rural and freeway models and speed differential dominating urban arterial models.
Methodology
field_study
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
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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