Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.

The Internet of Ships (IoS), by integrating advanced technologies, such as the Internet of Things, cloud computing, and artificial intelligence, aims to interconnect and communicate various physical devices related to maritime transportation, including ships, ports, traffic infrastructure, and warehouses. This integration is designed to optimize transportation decision making, reduce costs, enhance efficiency, improve safety, and promote environmental sustainability. Traditional IoS adopts a cloud computing-based data processing and service model, which, due to its centralized and remotely deployed nature, often places computing nodes far from the data collection and service demand points. This setup struggles to meet the high real-time and low-latency requirements of intelligent ships, traffic organization, remote control, and other applications. Edge computing, by decentralizing computing, storage, and network resources to the edge of the IoS, enables more responsive handling of device requests. It addresses critical requirements, such as intelligent access, real-time communication, and privacy protection in the IoS environment, facilitating intelligent, green communication, efficient data processing, and timely service responses. In this article, the current state of IoS and the relevant concepts of edge computing are introduced. The edge computing enabling IoS (EC-IoS) architecture and the core technologies driving EC-IoS development are systematically discussed. Emerging applications and the case studies of EC-IoS, including intelligent ships at different autonomy levels, intelligent transportation, smart ports, and warehouses, are summarized. Finally, challenges and future opportunities in open computing environments, maritime data management, system security, and resource management are outlined, providing a reference for optimizing maritime management and autonomous navigation.

A framework for risk assessment due to inland waterway transportation of dangerous goods is designed based on all possible event types that may be caused by the inland transportation of dangerous goods. The objective of this study is to design a framework for calculating the risks associated with changes in the transportation of dangerous goods along inland waterways. The framework is based on the traditional definition of risk and is designed for sensitive riverside environmental conditions in inland waterways. From the perspective of transportation management, this paper introduced the concept of transportability of dangerous goods and constructed a transportability assessment framework, which consists of a multi-index evaluation system and a single metric model. The result of the assessment is as an auxiliary basis to determine the transportation permit and control intensity of dangerous goods in an inland waterway specific voyage. The methodology is illustrated using a case study of transporting fireworks in the Yangtze River.