A Comprehensive Study of Single and Multiple Truck Crashes Using Violation and Crash Data
DOI: 10.2174/1874447801812010043
archive: archived pipeline: cataloged verified
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Summary
This study addresses the lack of research distinguishing between factors contributing to single-vehicle truck crashes versus multiple-vehicle crashes involving trucks, particularly regarding the integration of violation data. While truck crashes impose significant economic and safety burdens, previous studies often analyzed crash severity without separating crash types or incorporating driver violation histories. The authors aimed to identify contributory factors for injury or fatal crashes in both single-truck and multiple-vehicle scenarios and to determine which driver groups are at higher risk of committing violations that lead to such crashes. The researchers utilized data from Wyoming interstates I-80, I-25, and I-90 for the years 2011–2014. Crash data was obtained from the Wyoming Department of Transportation, while violation data came from the Wyoming court reported violation database. The study employed logistic regression models with stepwise selection to analyze binary outcomes: injury/fatal versus property damage-only crashes for crash analysis, and receipt of specific citations versus no citation for violation analysis. The crash analysis separated single-truck crashes from multiple-vehicle crashes involving at least one truck. The violation analysis focused on three specific violations linked to primary crash causes: failing to keep proper lane, driving too fast for conditions, and following too closely. For single-truck crashes, the results indicated that dry roadway conditions, higher posted speed limits (greater than 65 mi/hr), rollover or jackknife events, and driver distraction significantly increased the odds of injury or fatality. Conversely, male drivers had lower odds of injury compared to female drivers, likely due to physical trauma susceptibility differences. For multiple-vehicle crashes, speed compliance failure and driving on dry-road surfaces were identified as factors increasing injury odds. The violation analysis revealed that non-resident drivers, those driving during off-peak hours, and those driving on weekends were at increased risk of committing violations associated with truck crashes. These findings suggest that enforcement efforts could be targeted toward these specific temporal and demographic groups to mitigate crash risks. The significance of this study lies in its comprehensive approach, combining crash and violation data to provide actionable insights for traffic enforcement agencies, such as the Wyoming Highway Patrol. By identifying distinct risk factors for single and multiple crashes and linking them to specific violation patterns, the research supports the "enforcement" pillar of traffic safety. The findings imply that targeted enforcement against non-residents and weekend drivers, along with heightened attention to speed compliance and distraction, could reduce the frequency and severity of truck-related crashes. This work fills a gap in literature by demonstrating that single and multiple truck crashes have different underlying factors and that violation history is a viable predictor for identifying high-risk driver groups.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-06 |
| archive | success | canonical_url | — | — | 7 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| promote | success | — | — | — | 1 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Empirical Findings: crash risk outcomes