RTAverse: A Machine Learning-Based Analysis and Forecasting of Road Traffic Accidents in Angeles City

Road traffic accidents (RTAs) in Angeles City remain a persistent public-safety concern, yet operational planning is often reactive because risk signals are not transformed into actionable, forward-looking evidence. This study presents RTAverse, a private web-based decision-support system that operationalizes official accident records into spatiotemporal forecasting and hotspot-level risk mapping for authorized stakeholders (e.g., LGU and traffic enforcement units). Following CRISP-DM, RTA records from Camp Tomas J. Pepito (2015–2024) were consolidated and preprocessed through automated cleaning (canonicalized headers, spatiotemporal deduplication, and barangay-name normalization), temporal transformation (datetime parsing and engineered season/time attributes), and feature reduction for modeling. Seven learning algorithms (Decision Tree, Random Forest, AdaBoost, XGBoost, k-NN, Naive Bayes, and SVM) were screened using error-based forecasting metrics; Random Forest and XGBoost achieved the lowest initial errors (MAE ≈ 0.22–0.25). Under sequential time-series evaluation, XGBoost produced the most consistent performance, and a Poisson-objective XGBoost achieved cross-validation MAE scores of 0.39, 0.19, 0.18, and 0.13 (overall MAE = 0.22), reflecting improved suitability for count-based outcomes. A hybrid variant strengthened spatial utility by integrating time-of-day clustering into hotspot forecasts, yielding absolute error 0–1 for 20 of 26 hotspots in the final period. Feature importance analysis indicated late-night time clusters as the strongest predictors (nearly 70% of the importance score), followed by rolling temporal trends. Expert evaluation using ISO/IEC 25010 and TAM affirmed the dashboard’s usability and perceived usefulness. Overall, RTAverse demonstrates how privacy-preserving, localized accident data can be modeled as an evolving urban risk system and translated into practical forecasts that support preventive traffic-safety planning in Angeles City