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Department of Industrial EngineeringWith the increased importance of maritime logistics, resource scheduling has become a vital part of maintaining efficient port operations, taking into consideration all of the stakeholders and resources associated with a vessel, as well as the complicated and long logistics involved. The maritime industry uses automatic identification system (AIS) data to discover answers to vessel behavior concerns. AIS data contains vital voyage information in AIS data about a vessel???s trajectory acquired by satellite. Vessel behavior assessment is one of the most prevalent subjects. On the other hand, the goal reflecting vessel behavior variesit might be, for example, the general operating condition, a route deviation, or a close approach between two boats. The assessment frameworks need a sufficient representation of AIS data, the building of appropriate analytical models, and the execution of an anomaly detection method in conjunction with the precise issue definition. The goal of this thesis is to track real-time vessel behavior evaluation and geometric vessel trajectory measurement. We employ a variational autoencoder (VAE) from a deep learning technique and elastic depths from a statistical approach to monitoring a vessel???s behavior. VAE can handle high-dimensional processes, whereas the elastic depth provided by [1] permits statistical quantification of spatial information on the geometric sphere space. Furthermore, we compare the four-hot format suggested by [2] with the five-hot representation proposed by [3] to see which one represented AIS data better. As a consequence, we discuss methodologies for real-time abnormal behavior identification with a variational autoencoder (VAE)-based monitoring statistics and investigate the benefits and drawbacks of various monitoring statistics. Finally, we conduct comparison research with various VAE-based monitoring statistics and trajectory assessments using simulated data from possible anomalous circumstances and real-world data.ope