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PREDICTORS OF TRAFFIC ROAD VIOLATORS
Keywords:
road traffic violators, predictors, binary logistic regressionDOI:
https://doi.org/10.17654/0972361725049Abstract
The factors influencing a driver’s tendency to encounter traffic violations are examined in this study. Age is one of the main factors that the investigation finds to have a substantial impact on driver behavior. Because they may find it difficult to follow traffic laws or make mistakes when driving, older drivers are more likely to commit traffic infractions. Additionally, the kind of driver’s license (TDL) was found to alter infraction dispositions, demonstrating that the level of training and experience varies across drivers, which can influence their adherence to traffic rules. Furthermore, how drivers view traffic enforcement (Q1) may have an impact on their compliance; a lower perceived effectiveness may encourage riskier behavior. Self-reported driving behaviors including speeding and aggressive driving are clear behavioral markers of the likelihood of infractions. The model uses these variables to create a risk profile for every respondent, which can guide the creation of policies and focused interventions. The model’s capacity to take situational and psychological factors of driving into account is improved by the incorporation of subjective reactions with empirical data. All things considered, this system provides a data-driven way to predict traffic infractions and enhance road safety tactics.
Received: March 19, 2025
Revised: May 9, 2025
Accepted: May 30, 2025
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