Forecasting High-Risk Traffic Zones Using Machine Learning for Enhanced Road Safety
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Abstract
Road traffic accidents continue to pose serious global public health and economic challenges. In Malaysia alone, traffic-related incidents caused an estimated RM25 billion in losses in 2023. This study presents a two-part machine learning framework: Part A focuses on predicting accident severity, while Part B uses these predictions to forecast high-risk traffic zones through spatial and temporal analysis. Accident data from 2023 was selected from the UK Road Safety dataset to reflect current traffic patterns, infrastructure, and enforcement efforts. Five classifiers, Logistic Regression, Decision Tree, Random Forest, XGBoost, and K-Nearest Neighbors, were trained and evaluated. A stacking ensemble combining the top three models was constructed to enhance predictive accuracy. The models were assessed using accuracy, precision, recall, and F1-score, with results showing that the ensemble method outperformed individual classifiers. The findings demonstrate the potential of ensemble learning in identifying high-risk zones and supporting proactive road safety planning.
Manuscript received: 3 Aug 2025 | Revised: 21 Sep 2025 | Accepted: 28 Sep 2025 | Published: 30 Nov 2025
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