Missouri Highway Safety Manual Recalibration [2018-01]
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Summary
This study addresses the need to update and recalibrate the Highway Safety Manual (HSM) for Missouri, ensuring that national safety models accurately reflect local driving conditions, geometric designs, and crash patterns. The HSM, first published in 2010, recommends that states regularly calibrate its safety performance functions to local data. This project builds upon an initial statewide calibration finalized in 2013, utilizing more recent crash data and updated methodologies to improve prediction accuracy. Additionally, the research aims to develop crash severity distribution functions, allowing for the estimation of crash frequencies categorized by severity: fatal, severe injury, minor injury, and property damage only. The methodology involved a comprehensive data collection and calibration process for 16 distinct facility types. These included rural two-lane and multilane segments with associated intersections, urban arterial segments (two-, four-, and five-lane), rural and urban freeway segments (four- and six-lane), and both signalized and unsignalized urban and rural intersections. Researchers gathered detailed data on road geometrics, traffic volumes (AADT), signalization, land use, and crash history. The calibration process derived specific factors to adjust the national HSM models to Missouri conditions. The study also analyzed crash severity distributions for each facility type to provide a more granular understanding of safety outcomes beyond total crash frequency. The results indicated that the national HSM models generally predicted crash frequencies in Missouri reasonably well across most facility types. However, the calibration revealed discrepancies for a few specific site types, suggesting that the national models may not fully capture local nuances in those cases. The study successfully generated updated calibration values and severity distribution factors for all 16 facility types. For instance, specific calibration outputs were provided for rural two-lane undivided segments, urban signalized intersections, and various freeway configurations, distinguishing between single-vehicle and multi-vehicle crashes as well as injury and property damage only incidents. The significance of this work lies in providing the Missouri Department of Transportation with updated, locally calibrated safety performance functions. These values are intended for use in the MoDOT Engineering Policy Guide, enabling more accurate safety analyses for highway design and improvement projects. By incorporating crash severity distributions, the manual allows practitioners to assess not just the likelihood of crashes, but the potential severity of outcomes, thereby supporting more targeted safety interventions. The report concludes that while the HSM is a robust tool, continued local calibration and potential development of state-specific functions for certain site types are necessary to maintain high prediction accuracy.
Key finding
The Highway Safety Manual predicted Missouri crashes reasonably well for most of the 16 calibrated facility types, though some sites may require locally developed safety performance functions.
Methodology
dataset
Provenance
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| 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.
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- Empirical Findings: crash risk outcomes
- Methodological Resource: dataset resource