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

• Published in: Journal of the Eastern Asia Society for Transportation Studies Vol. 15; pp. 3207 - 3227
• Main Authors: BALLARTA, Jerome, JAVIER, Sheila Flor, SALANG, Aaron Michael, TAGUIAM, Jebus Edrei
• Format: Journal Article
• Language: English
• Published: Eastern Asia Society for Transportation Studies 2024
• Subjects: Crash Severity; Genetic Algorithm; Machine Learning Classifier; Road Safety; Spatio-temporal Analysis
• ISSN: 1881-1124
• DOI: 10.11175/easts.15.3207

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.