Abstract
With the trend of internationalization, maritime traffic density has gradually increased. Since 2002, the International Maritime Organization (IMO) has required various types of vessels to be equipped with the Automatic Identification System (AIS). Through AIS static and dynamic data, more complete navigational information of vessels can be obtained. As the Port of Kaohsiung is currently transitioning into a smart port, this study focuses on inbound and outbound vessels of the Second Port of Kaohsiung. It considers both the safety monitoring of the smart port and environmental security, integrating a big data database to provide early warnings for abnormal navigation conditions. This study builds an integrated database based on vessel AIS data, conducts AIS big data analysis to extract useful information, and establishes a random forest model to predict whether a vessel’s course and speed during port navigation deviate from normal patterns, thereby achieving the goal of early warning. This study also helps reduce the risk of collisions caused by abnormal vessel operations and thus prevents marine pollution in the port area due to oil spills or hazardous substance leakage. Through real-time monitoring and early warning of navigation behavior, it not only enhances navigation safety but also serves as the first line of defense against marine pollution, contributing significantly to the protection of the port’s ecological environment and the promotion of sustainable development.