The Influence of Aggressive Driving Behavior and Impulsiveness on Traffic Accidents
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Summary
This study investigates the predictive relationship between impulsiveness, aggressive driving behavior, and traffic accident involvement, while also comparing these traits across bus drivers, truck drivers, and non-professional drivers. Motivated by the fact that human factors account for 90–95% of traffic accidents, the research aims to determine if personality traits and specific driving behaviors can forecast crash risk more accurately than demographic variables alone. The researchers collected data from 305 drivers (202 professional and 103 non-professional) who regularly use a high-risk road section in Serbia. Participants completed the Barratt Impulsiveness Scale (BIS-11) to measure impulsivity and the Aggressive Driving Behavior Questionnaire (ADBQ) to assess aggression. They also reported their total number of traffic accidents throughout their driving careers. Statistical analyses included Spearman’s correlations, Kruskal-Wallis tests for group differences, and hierarchical regression analyses controlling for age and driving experience. Results indicated a statistically significant positive correlation between impulsiveness and aggressive driving behaviors. Hierarchical regression analyses revealed that both impulsiveness and aggressiveness are strong predictors of traffic accidents. After controlling for age and driving experience, impulsiveness explained an additional 20.9% of the variance in accident occurrence, while aggressiveness explained an additional 18.6%. When comparing driver groups, bus and truck drivers exhibited significantly higher levels of impulsiveness than non-professional drivers, with bus drivers scoring at the upper limit of normal values. Conversely, non-professional drivers displayed the highest levels of aggressive driving behavior, followed by truck drivers and then bus drivers. No statistically significant differences in aggression were found among the groups. The findings suggest that impulsiveness and aggressive driving are robust predictors of traffic accidents, independent of age and experience. The study highlights a distinction between professional and non-professional drivers: professionals show higher impulsivity, potentially linked to occupational stress and fatigue, whereas non-professionals exhibit higher aggression. The authors conclude that these psychological constructs are critical for understanding risky driving and suggest that future research should address limitations such as gender imbalance and reliance on self-reported data.
Key finding
Impulsiveness and aggressive driving behavior are significant predictors of traffic accidents, with professional drivers exhibiting higher impulsiveness and non-professional drivers exhibiting higher aggression.
Methodology
survey
Sample size: 305
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.
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Information type
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- Empirical Findings: observational prevalence, behavioral performance data
- Theoretical Contribution: computational model