Comparative evaluation of VAE-based monitoring statistics for real-time anomaly detection in AIS data

Real-time tracking and anomaly detection in vessel navigation using AIS (Automatic Identification System) data is a critical issue in maritime logistics. However, the substantial volume, inherent noise, and irregular temporal intervals of AIS data pose challenges in applying traditional Statistical Process Monitoring (SPM) methods. To overcome these challenges, recent research has proposed the use of deep probabilistic latent variable models, specifically variational autoencoders (VAE). However, the monitoring statistics based on VAE employed in previous studies may vary. In this study, we conduct a comprehensive comparison and evaluation of various monitoring statistics based on VAE within the context of statistical process monitoring. Furthermore, we propose a new real-time monitoring method by integrating VAE-based monitoring statistics with the CUSUM chart for monitoring AIS data. By utilizing both simulated and real-world AIS data, our proposed method demonstrates superior detection performance and robustness compared to traditional methods.