 TRED, Traffic Root Extraction and Anomaly Detection is a machine learning algorithm designed to extract and analyze maritime traffic patterns from raw AIS data. It is able to process varying levels of intermittency, sensor coverage, and data sources, as well as account for time lags between successive observations. By using this algorithm, it is possible to identify and classify ships based on their behavior, such as port visits or route changes. Additionally, TRED can also detect anomalous behavior, such as ships that have deviated from their normal course. This article was authored by Karna Bryan, Juliana Pilata, and Michelle Vesp.