Enhancing Crash Data Reporting to Highway Safety Partners in Wyoming by Utilizing Big Data Analysis and Survey Techniques
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Summary
This study addresses the need to improve road safety data reporting practices within Wyoming by aligning data provision with the specific needs of highway safety partners. The Wyoming Department of Transportation (WYDOT) collects crash data from police reports and roadway inventories, distributing this information to various partner groups, including the Wyoming Seat Belt Coalition, the Wyoming Highway Patrol, and the Governor’s Council on Impaired Driving. The research was motivated by identified gaps in current reporting, specifically regarding the quality of data provided and the appropriateness of reporting frequencies. The goal was to enhance the utility of these data for partners to better implement safety countermeasures, such as enforcement campaigns and roadway redesigns, to reduce crash counts and fatalities. The methodology combined survey techniques with big data analytics. Researchers developed and distributed tailored surveys to eight distinct partner groups to assess their satisfaction with current data quality, identify unreported data that would be beneficial, and determine optimal data provision frequencies. Concurrently, the study utilized big data analysis on crash records to evaluate human factors influencing crash occurrences and to identify appropriate time frames for data reporting. The analysis focused on specific crash categories, including those involving improper restraint use, speeding, adverse weather, animal strikes, driving under the influence, non-motorist incidents, and motorcycle crashes. Statistical models, including logistic regression and random forest algorithms, were employed to identify critical human factors and estimate probabilities of fluctuations in crash counts throughout 2019. The findings revealed detailed insights into crash dynamics and partner needs. Survey responses from each partner group highlighted specific deficiencies in current reporting and provided targeted recommendations for WYDOT. For instance, the Wyoming Seat Belt Coalition and motorcycle groups received specific recommendations based on their survey feedback. The big data analysis identified significant correlations between human factors and crash severity. The study produced extensive visualizations and statistical tables detailing crash counts by demographic factors (age, gender), environmental conditions (lighting, weather, road surface), and driver behaviors (distraction, impairment, speeding). The analysis also generated heat maps for 2017 crash locations and estimated probabilities of crash count fluctuations for various categories in 2019, providing a granular view of temporal and spatial crash patterns. The significance of this research lies in its actionable recommendations for enhancing the efficiency and effectiveness of WYDOT’s data dissemination. By identifying critical human factors and establishing appropriate reporting time frames, the study enables partner groups to receive higher-quality data at frequencies that support their specific safety objectives. This alignment facilitates more precise targeting of safety interventions, such as focused enforcement on impaired driving or improved infrastructure for pedestrian and bicycle safety. Ultimately, the report provides a framework for continuous improvement in road safety assessments, ensuring that data-driven decisions are supported by timely and relevant information, thereby contributing to the reduction of roadway fatalities and injuries in Wyoming.
Key finding
The study produced tailored recommendations for the Wyoming Department of Transportation to improve the quality and frequency of crash data reporting to its safety partners based on survey feedback and big data analysis of human factors.
Methodology
mixed_methods
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
Ranked by relevance to this paper. Hover a topic for its definition.
- incidence prevalence
- demographic disparities
- causation analyses
- naturalistic crash near crash
- sex gender
- fatality injury trends
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