Abstract
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.
Highlights
The maritime data from multi-sources has rich meaning in the big data era, especially the meaning of ship behaviors [1]
Where some studies obtain the regional distribution of ship behavior based on statistical models [4], some focus on identifying abnormal behavior [14,15,16], and some obtain simple behaviors based on one type of data [5]
Baglioni et al [26] presented an approach to provide the interpretation of movement behavior. This approach provides a model for the conceptual representation and deductive reasoning of trajectory patterns obtained from mining raw trajectories
Summary
The maritime data from multi-sources has rich meaning in the big data era, especially the meaning of ship behaviors [1]. Most of the existing studies focus on the data analysis in the ships behavior research [4,5], but there are some problems—the semantic meanings of behaviors and their relationships are not explicit; the data from different sources or dimensions cannot be connected; and the traffic rules are difficult to consider [6]. This has led to the development of semantic ship behaviors based on the semantic trajectory [7].
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