Arizona Local Government Safety Project Analysis Model
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Summary
This report addresses the challenge local governments in Arizona face in identifying and prioritizing highway safety projects due to limited resources for data collection and site assessment. The research was motivated by the need to ensure that high-incident locations receive mitigation despite the availability of federal Hazard Elimination and Safety (HES) funding, which requires detailed benefit-to-cost analyses. The primary objective was to develop an efficient, automated tool to help local jurisdictions identify hazardous sites, assign priorities, and evaluate potential safety treatments impartially. The study focused on developing implementation strategies for local safety projects by synthesizing variables such as traffic volumes, average speed, roadway design, and crash history. The core methodological output is the Arizona Local Government Safety Project (LGSP) Model, a Microsoft Access database designed to automate site identification and project selection. The model incorporates Arizona-specific estimators, including crash cost conversions and crash reduction factors for various treatments. The report details the theoretical framework for safety evaluation, covering methods for identifying high-risk locations (e.g., crash frequency, rate, and severity), estimating crash costs, and selecting appropriate countermeasures. To validate the model, a case study was conducted in the Central Arizona Association of Governments (CAAG) region, utilizing crash data from 1995 to 1999 for Gila and Pinal Counties. Key findings include the successful development of the LGSP Model, which provides a structured process for inputting crash data, identifying priority locations, and generating evaluation reports for hypothetical safety projects. The case study demonstrated the model’s ability to identify priority locations in Gila and Pinal Counties and assess the potential benefits of specific improvements. The report also provides comprehensive appendices containing user instructions, Arizona-specific crash reduction factors for various roadway and intersection improvements, and guidelines for estimating treatment effectiveness. The model allows users to perform benefit/cost analyses to determine project eligibility, requiring a ratio of at least 1.0. The significance of this work lies in providing local governments with a standardized, justifiable rationale for decision-making regarding highway safety investments. By automating the identification of hazards and the evaluation of treatments, the LGSP Model reduces the time and expense associated with preliminary data collection. This enables local jurisdictions to address safety needs more timely and direct attention to the most hazardous locations, thereby improving the overall safety of Arizona’s roadway system. The tool ensures that federal aid is utilized effectively by supporting data-driven prioritization of safety projects.
Key finding
The Arizona LGSP model provides local jurisdictions with an automated, efficient method for identifying high-risk crash sites, prioritizing them, and evaluating the benefit-to-cost ratio of safety treatments to overcome resource limitations in highway 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.
- incidence prevalence
- regulatory evaluation
- comparative international
- demographic disparities
- naturalistic crash near crash
- roadway lighting effects
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).
- Applied Guidance: countermeasure evaluation
- Empirical Findings: crash risk outcomes
- Methodological Resource: dataset resource