Traffic anomaly detection in urban mobility data flows based on Random Forest Adaptive

Abstract

Urban mobility systems face increasing challenges due to congestion, unexpected traffic incidents, and ineffective control strategies that impact the sustainability and livability of cities. This study proposes an adaptive machine learning approach for real-time anomaly detection in vehicular data flows collected in a medium-sized Latin American city. The methodology integrates an Adaptive Random Forest (ARF) classifier for continuous learning on non-stationary data, addressing conceptual drift caused by dynamic traffic conditions. A public dataset of GPS vehicle trajectories and sensor readings was processed to identify anomalous patterns, such as congestion, accidents, or irregular flow behavior. Model performance was evaluated using confusion matrices, accuracy-recall analysis, and F1 score metrics, demonstrating robust adaptability to temporal variations in traffic density. The results highlight the potential of adaptive learning algorithms to improve sustainable traffic management, urban mobility planning, and decision support systems in smart cities by enabling early detection of anomalies and improving traffic efficiency