Relationship between Socio-Demographic of Drivers and Traffic Violations and Crashes Involvements
DOI: 10.11159/icte17.113
archive: archived pipeline: cataloged verified
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Summary
This study investigates the interrelationships between drivers’ socio-demographic characteristics, historical traffic violations, and crash involvements. Motivated by the finding that human error contributes to approximately 90–95% of traffic crashes, the research aims to identify specific demographic and behavioral factors that predict crash risk. The authors seek to determine how variables such as gender, age, nationality, education, occupation, and violation history influence the frequency of both property damage only (PDO) and severe crashes. The analysis utilized eight years of historical data (2008–2015) from the Abu Dhabi Traffic Police databases in the United Arab Emirates. After filtering for completeness and driving experience, the final dataset comprised 608,611 individual driver records, encompassing 4.4 million violations and over 550,000 crashes. The researchers first calculated crash rates for various demographic groups to identify trends. Subsequently, they developed two Negative Binomial Regression models to estimate the significant predictors for PDO and severe crash frequencies, incorporating variables such as total violation counts, specific violation types (speeding, tailgating, mobile usage, seat belt usage), and demographic attributes. The results demonstrate a strong positive correlation between the number of traffic violations and crash rates for both PDO and severe incidents. Demographic analysis revealed that male drivers, young drivers (18–24 years), older drivers (>65 years), local nationals, individuals with low education levels, professional drivers, and those owning multiple vehicles exhibit higher crash rates. The regression models confirmed that total violation counts, gender, age, experience, occupation, and vehicle ownership significantly predict both crash types. Speeding and tailgating were significant predictors for both PDO and severe crashes. However, Asian nationality, mobile usage, and seat belt violations were significant only for PDO crashes, while education level was not a significant predictor for either crash type. The study concludes that driver demographics and violation histories are critical indicators of crash risk, supporting the use of historical records to identify high-risk drivers for targeted interventions. The findings highlight that while certain violations like speeding universally increase crash risk, others like mobile usage primarily correlate with minor property damage incidents. This distinction suggests that road safety measures should be tailored to specific violation types and demographic groups to effectively reduce both minor and severe crash occurrences.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | partial | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- sex gender
- demographic disparities
- cultural cross national
- incidence prevalence
- novice drivers
- induced exposure
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