Predicting Speeding Behavior of Long-Haul Freight Truck Drivers Using Machine Learning Models
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The behavior of long-haul truck drivers is shaped by the weak enforcement of working-hour rules, tight deadlines, and heavy workloads. Over-dimensioning and overloading practices further increase risks by forcing drivers to handle excessive loads and work for prolonged periods. This study predicts speeding behavior among long-haul freight truck drivers using statistical and machine learning models. Data was collected from 370 respondents at two weigh stations in South Sulawesi, Indonesia, covering eight socio-demographic, economic, and operational predictors. Three models were tested: Binary Logistic Regression (BLR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The dataset was balanced and split into 70% training and 30% testing, with performance assessed using accuracy, recall, F1-score, and AUROC. XGBoost delivered the best results, achieving 97.3% accuracy, 93.2% recall, a 96.4% F1-score, and a perfect AUROC of 1.000. RF also showed strong performance with 94.05% accuracy and an AUROC of 0.973, while BLR served as a relevant baseline despite weaker predictions. Key predictors of speeding violations were daily sleep duration, monthly income, and driving experience. This study demonstrates how machine learning can be effectively integrated alongside transportation data under imbalanced conditions, providing evidence-based insights to strengthen freight transport safety.
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