Roadway safety analysis methodology for Utah : final report.
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Summary
This report presents the development of a three-part Roadway Safety Analysis methodology for the Utah Department of Transportation (UDOT), designed to automate and streamline highway safety research. The project was motivated by UDOT’s goal to reduce motor vehicle fatalities to zero and the need for efficient tools to prioritize safety improvements across the state roadway network. While Utah’s fatality rates have declined and remain below the national average, the volume of crash data requires systematic analysis to identify high-risk segments. The methodology integrates previously developed statistical models—the Utah Crash Prediction Model (UCPM) and the Utah Crash Severity Model (UCSM)—into a unified, automated workflow accessible via graphical user interfaces (GUIs). The methodology consists of three sequential phases. First, roadway and crash data are prepared and segmented. This involves standardizing datasets, including Annual Average Daily Traffic (AADT), functional classification, and crash records from 2010–2015, and segmenting roadways based on changes in geometric or operational characteristics. Second, statistical network screening is performed using the UCPM and UCSM within an R-based environment. This step ranks roadway segments by state, UDOT region, and county, identifying "segments of interest" with statistically significant safety issues. Third, automated report compilation generates detailed safety analysis reports for these segments. These reports combine roadway characteristics, crash data summaries, and potential countermeasures derived from the NCHRP Report 500 series, allowing analysts to quickly assess safety problems and recommend interventions. The study demonstrates the methodology through an example application using 2010–2015 crash data. The automation tools successfully processed data, executed statistical models, and generated ranked lists of high-risk segments. For instance, the UCPM and UCSM analyses produced top-20 segment rankings that were spatially displayed using ArcMap tools. The system also facilitated iterative analysis, showing how updated crash data (e.g., adding 2015 data) could be reprocessed efficiently. The resulting reports provided micro-analyses of crash factors and suggested specific countermeasures, such as shoulder rumble strips or median improvements, tailored to the identified safety issues. The automation significantly reduced the time and resources required for data processing and report generation compared to manual methods. The significance of this work lies in its provision of a scalable, user-friendly framework for highway safety analysis. By automating the integration of predictive models and data processing, the methodology enables UDOT engineers and regional directors to consistently identify and prioritize safety improvements. The report concludes with recommendations for future research, including expanding the analysis to intersections and horizontal curves, implementing the methodology in other states, and advancing geospatial tools for crash analysis. This approach supports evidence-based decision-making in transportation safety, aligning with national standards like the Highway Safety Manual and enhancing the efficiency of the Highway Safety Improvement Program.
Key finding
The Roadway Safety Analysis methodology successfully automates the preparation, statistical network screening, and report compilation processes for Utah roadway segments, enabling efficient identification of safety issues and potential countermeasures.
Methodology
other
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.
- incidence prevalence
- naturalistic crash near crash
- causation analyses
- comparative international
- regulatory evaluation
- induced exposure
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