Effect of Socioeconomic Factors on Crash Occurrence
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Summary
This study investigates the relationship between socioeconomic characteristics of driver residence and the likelihood of being at fault in traffic crashes. Motivated by Kentucky’s persistently high crash rates and fatalities compared to national averages, the research seeks to explain whether regional socioeconomic conditions—such as income, education, and rurality—contribute to these safety disparities. While prior research often focused on crash location characteristics, this study specifically examines the socioeconomic attributes of the zip codes where drivers reside, aiming to identify at-risk driver groups to inform targeted safety interventions. The researchers utilized an extensive historical crash dataset from Kentucky, combining crash records with socioeconomic and demographic data from the U.S. Census Bureau and traffic conviction records. The methodology employed the quasi-induced exposure approach to estimate crash exposure, allowing for the calculation of the Relative Accident Involvement Ratio (RAIR). Binary logistic regression models were developed to predict the probability of a driver being at fault in both two-unit and single-unit crashes. Variable selection techniques, including correlation tests and recursive partitioning, were used to identify significant predictors. Additionally, spatial analysis was conducted to map correlations between county-level socioeconomic characteristics and crash involvement across the state. Statistical analysis revealed that multiple socioeconomic and demographic variables significantly influence the likelihood of a driver being at fault. Key factors included income, education level, poverty level, employment status, age, gender, rurality, and the number of traffic-related convictions within the driver’s zip code. For instance, drivers residing in areas with lower median incomes, higher poverty rates, and lower educational attainment showed increased probabilities of crash involvement. The models distinguished between two-unit and single-unit crashes, identifying specific risk profiles for each crash type. However, the spatial analysis did not uncover robust correlations between county-level socioeconomic characteristics and at-fault driver involvement across the state, suggesting that while individual socioeconomic factors are predictive, broader geographic patterns may be less distinct or more complex. The findings indicate that socioeconomic status is a significant predictor of crash risk, supporting the hypothesis that drivers from disadvantaged backgrounds are more likely to be involved in crashes. This evidence allows transportation agencies to identify specific at-risk populations based on residence characteristics rather than just crash locations. The results provide a basis for developing targeted and efficient safety programs, such as the Kentucky Safety Circuit Rider Program, by focusing resources on groups and areas with higher predicted crash probabilities. By linking crash occurrence to residence-based socioeconomic data, the study offers a framework for proactive safety management that addresses underlying social determinants of traffic safety.
Key finding
Socioeconomic and demographic variables including income, education, poverty, employment, age, gender, rurality, and traffic convictions significantly influence the likelihood of a driver being at fault in a crash.
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 | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- demographic disparities
- incidence prevalence
- sex gender
- induced exposure
- pre crash contributing factors
- causation analyses
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