Development of a Speeding-Related Crash Typology
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Summary
This study addresses the persistent safety challenge of speeding, which contributes to approximately 30–33% of fatal crashes annually in the United States. Motivated by the Federal Highway Administration’s Speed Management Strategic Initiative, the research aims to develop a speeding-related (SR) crash typology. The primary goal is to identify specific crash, vehicle, and driver characteristics—answering questions of what, where, when, and who—to guide the development of new countermeasures and improve the targeting of existing treatments. The researchers analyzed data from two national databases, the Fatality Analysis Reporting System (FARS) and the National Automotive Sampling System General Estimates System (NASS GES), alongside two state databases from North Carolina and Ohio. This multi-source approach allowed for comparisons between fatal and nonfatal crashes, as well as between national and state-level data. Additionally, the state data enabled an examination of differences between a combined definition of speeding (exceeding the limit or driving too fast for conditions) and a stricter definition (exceeding the limit only). The analysis employed two methodologies: single-variable table analysis to identify overrepresented factors, and Classification and Regression Tree (CART) analysis to determine critical variable combinations. Analyses were conducted on the full datasets and five high-priority subsets, including pedestrian, intersection, and lane departure crashes. Single-variable analyses revealed consistent patterns across databases: SR crashes were disproportionately associated with single-vehicle collisions, rural locations, curves, nighttime conditions, motorcycles, young and male drivers, unrestrained occupants, and drivers under the influence of alcohol. Conversely, no consistent speeding patterns were observed in pedestrian, bicycle, or work zone crashes. CART results were less consistent across databases but consistently identified single-vehicle crashes during adverse weather as a high-priority subgroup. Vehicle-based CART findings showed little consistency, though young male drivers appeared frequently. Subset analyses indicated that definitions of speeding had minimal impact on results, and findings varied significantly between fatal and total crash datasets. The study concludes that a robust SR crash typology can be established using single-variable analyses, which align with prior research, while CART analyses provide more complex, albeit less consistent, insights into critical factor combinations. The findings offer actionable guidance for traffic engineers and safety officials to target high-risk scenarios, such as rural curves and nighttime single-vehicle crashes, thereby supporting more effective speed management strategies.
Key finding
Speeding-related crashes were consistently overrepresented in single-vehicle incidents, rural locations, nighttime conditions, and among young male drivers not using restraints or under the influence of alcohol.
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 | 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
- crash typology
- pre crash contributing factors
- vru crash typology
- naturalistic crash near crash
- demographic disparities
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