Regionalized Safety Performance Functions
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Summary
This study addresses the need for localized safety performance functions (SPFs) that accurately reflect driving conditions in Pennsylvania, rather than relying solely on the generic national models provided in the American Association of State Highway and Transportation Officials’ (AASHTO) Highway Safety Manual (HSM). The primary motivation was to develop Pennsylvania-specific, regionalized SPFs that capture geographic variations in safety performance across the Commonwealth. The research focused on three roadway classes: rural two-lane highways, rural multilane highways, and urban/suburban arterials (non-freeways). For each class, the study aimed to predict both total crash frequency and the frequency of fatal and injury crashes for roadway segments and common intersection types. The methodology involved compiling extensive data from Pennsylvania Department of Transportation (PennDOT) sources, including Roadway Management System (RMS) data for geometric characteristics and traffic volumes, and electronic crash records from 2010 to 2014. Supplemental data, such as roadside hazard ratings and the presence of safety improvements, were collected using PennDOT’s video photolog system and Google Earth. The researchers employed negative binomial regression to estimate SPFs at three levels of regionalization: county, metropolitan/rural planning organization, and PennDOT engineering district. The study utilized previously collected data for rural two-lane highways and newly collected data for multilane and urban/suburban arterials. The results demonstrated that the optimal level of regionalization depended on sample size availability. When sufficient data were available, district-level SPFs combined with county adjustment factors provided the highest predictive power, outperforming both statewide models and the standard HSM models. Conversely, when sample sizes were inadequate for estimating regionalized SPFs, the study recommended using statewide models adjusted by district-level factors to account for geographic differences. Specific SPFs were developed for various segment types (e.g., two-lane undivided, four-lane divided) and intersection configurations (e.g., signalized, stop-controlled), with detailed modification factors provided for each. The significance of this work lies in its provision of more accurate safety prediction tools for Pennsylvania transportation professionals. By establishing that local SPFs, when data permits, offer superior predictive accuracy compared to national standards, the study underscores the importance of utilizing local crash and roadway inventory data. The findings provide a framework for implementing regionalized safety analysis in project development, allowing for better-informed safety countermeasure decisions that are tailored to specific geographic contexts within Pennsylvania.
Key finding
District-level safety performance functions with county adjustment factors outperformed other regional or statewide models in predicting crash frequencies when adequate sample sizes were available.
Methodology
modeling
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 author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 5 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: crash risk outcomes