HIGHWAY ACCIDENT RISK PREDICTION AND BLACKSPOT DETECTION USING MACHINE LEARNING

Road traffic accidents continue to be one of the greatest challenges to highway safety, and there is a need for proactive approaches that can be used to identify locations where accidents are likely to occur and to predict accident risk. In this study, an integrated framework for highway accident risk prediction and blackspot detection is presented by applying machine learning with spatial analysis and supervised learning. The haversine distance metric is used for DBSCAN to detect spatial concentration of accidents in blackspots. Kernel Density Estimation (KDE) is used to visualise a continuous representation of the accident-density pattern. Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers are used to predict the risk and are evaluated by accuracy, precision, recall, F1-score and AUC-ROC. In the principal model evaluation reported in the study, XGBoost obtained 91.2% accuracy, 90.5% precision, 89.8% recall, 90.1% F1-score and 0.947 AUC-ROC, while Random Forest got 89.4% accuracy. The DBSCAN algorithm found 14 clusters corresponding to highway black spots with an optimised epsilon value of 1.0 km and a minimum of 5 samples, and a silhouette score of 0.58.