To address the issue of data integrity and reliability caused by sparse vessel trajectory data, this paper proposes a multi-step restoration method for sparse vessel trajectory based on feature correlation. First, we preserved the overall trend of the trajectory by detecting and marking the sparse and abnormal vessel trajectories points and using the cubic spline interpolation method for preliminary restoration. Then, we established a composite indicator of feature correlation for selecting highly correlated trajectory features as inputs to the model, reducing data redundancy while improving the key correlation between trajectory features. Finally, we developed a vessel trajectory restoration model based on the Seq2Seq network for secondary restoration of the trajectory to ensure the accurate restoration of the vessel trajectory. Through comparison and ablation experiments, we demonstrate that the method can efficiently extract highly correlated features from vessel trajectories and combines the advantages of the interpolation method and neural network model to effectively improve the accuracy of trajectory restoration and ensure the integrity and accuracy of trajectory data. The research results could provide crucial technical support for the subsequent mining of vessel behavior patterns and assisted decision-making, which holds significant application prospects and potential value.

Sea transportation plays a vital role in global trade, and studying the impact of emergencies on global sea transportation is essential to ensure the stability of trade. At present, the conflict between Palestine and Israel has attracted extensive attention worldwide. However, there is a lack of specific research on the impact of conflict on shipping, particularly on global shipping costs. By using the global vessel trajectory data of tankers from the Automatic Identification System (AIS) and taking the global sea transportation of large tankers as an example, this paper quantifies and visualizes the changes in global sea transportation before and after conflicts from a data-driven perspective. Firstly, the complete vessel trajectory, as well as the port of departure and the port of destination are extracted. Then, from the perspective of shipping cost and vessel traffic flow, we evaluate the vessel traffic flow changes caused by the conflict by using the route distance to replace the shipping costs and quantify the cost increase for the relevant countries caused by the vessel detour based on the shipping cost increment index. The research results show that after the outbreak of the conflict, the number of vessels passing through the Red Sea area has decreased significantly. About 3.1% of global vessels were affected, with global sea transportation costs of large tankers increasing by about 0.0825%. This study takes the Israeli–Palestinian conflict as an example and analyzes the impact of emergencies on the global sea transportation situation of tankers based on AIS data. The research results reveal the characteristics of international shipping to a certain extent and provide guidance for global sea transportation route planning.

In the field of research into vessel type recognition utilizing trajectory data, researchers have primarily concentrated on developing models based on trajectory sequences to extract the relevant information. However, this approach often overlooks the crucial significance of the spatial dependency relationships among trajectory points, posing challenges for comprehensively capturing the intricate features of vessel travel patterns. To address this limitation, our study introduces a novel multi-graph fusion representation method that integrates both trajectory sequences and dependency relationships to optimize the task of vessel type recognition. The proposed method initially extracts the spatiotemporal features and behavioral semantic features from vessel trajectories. By utilizing these behavioral semantic features, the key nodes within the trajectory that exhibit dependencies are identified. Subsequently, graph structures are constructed to represent the intricate dependencies between these nodes and the sequences of trajectory points. These graph structures are then processed through graph convolutional networks (GCNs), which integrate various sources of information within the graphs to obtain behavioral representations of vessel trajectories. Finally, these representations are applied to the task of vessel type recognition for experimental validation. The experimental results indicate that this method significantly enhances vessel type recognition performance when compared to other baseline methods. Additionally, ablation experiments have been conducted to validate the effectiveness of each component of the method. This innovative approach not only delves deeply into the behavioral representations of vessel trajectories but also contributes to advancements in intelligent water traffic control.