Spatio-Temporal Analysis and Severity Analysis using Machine Learning Classifiers for Electric Vehicle Crashes Data of Metro Manila, Philippines

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This study examines the factors affecting electric vehicle (EV) crash severity in Metro Manila by utilizing spatio-temporal analysis tools and machine learning classifiers, such as Random Forest (RF), K-Nearest Neighbors (KNN), Naïve-Bayes (NB), Artificial Neural Network (ANN), and an experimental Random Forest Classifier with Genetic Algorithm tuning (RF-GA). The spatio-temporal analysis reveals that most EV crashes occur during off-peak hours and are concentrated in areas with high population density. Crash hotspots were identified in locations without dedicated infrastructure. The results indicate that the NB and RF-GA models outperform the other classifiers in predicting crash severity, with car involvement as the most important variable. These findings hold significant implications for developing targeted interventions, identifying high-risk areas, and implementing measures to reduce the severity of EV crashes in Metro Manila, Philippines.