Sudden changes in a vessel's path are common, complicating efforts to predict future behavior. In this work, we leverage existing traffic to develop an algorithm that makes informed trajectory predictions. Our method begins with analyzing historical maritime traffic using AIS data from vessels in southern New England. This analysis culminates in creating representative trajectories of this data that are validated against nautical charts. The algorithm matches a given path to these trajectories and uses them to model their behavior. This developing effort aims to reduce the cognitive load for mariners and support autonomous navigation efforts by predicting the probable movement of nearby vessels and their future waypoints.