Context-Aware Anomaly Detection of Pedestrian Trajectories in Urban Back Streets Using a Variational Autoencoder

Detecting anomalous pedestrian behaviors is critical for enhancing safety in dense urban environments, particularly in complex back streets where movement patterns are irregular and context-dependent. While extensive research has been conducted on trajectory-based anomaly detection for vehicles, ships, and aircraft, few studies have focused on pedestrians, whose behaviors are strongly influenced by surrounding spatial and environmental conditions. This study proposes a pedestrian anomaly detection framework based on a Variational Autoencoder (VAE), designed to identify and interpret abnormal trajectories captured by large-scale Closed-Circuit Television (CCTV) systems in urban back streets. The framework extracts 14 movement features across point, trajectory, and grid levels, and employs the VAE to learn normal movement patterns and detect deviations from them. A total of 1.88 million trajectories were analyzed, and approximately 1.05% were identified as anomalous. These were further categorized into three behavioral types—wandering, slow-linear, and stationary—through clustering analysis. Contextual interpretation revealed that anomaly types differ substantially by time of day, spatial configuration, and weather conditions. The final optimized model achieved an accuracy of 97.80% and an F1-score of 94.63%, demonstrating its strong capability to detect abnormal pedestrian movement while minimizing false alarms. By integrating deep learning with contextual urban analytics, this study contributes to data-driven frameworks for real-time pedestrian safety monitoring and spatial risk assessment in complex urban environments.