In the context of the rapid development of deep learning theory, predicting future motion states based on time series sequence data of ship trajectories can significantly improve the safety of the traffic environment. Considering the spatiotemporal correlation of AIS data, a trajectory time window panning and smoothing filtering method is proposed for the abnormal values existing in the trajectory data. The application of this method can effectively deal with the jump values and outliers in the trajectory data, make the trajectory smooth and continuous, and ensure the temporal order and integrity of the trajectory data. In this paper, for the features of spatiotemporal data of trajectories, the LSTM structure is integrated on the basis of the deep learning Transformer algorithm framework, abbreviated as TRFM-LS. The LSTM module can learn the temporal features of spatiotemporal data in the process of computing the target sequence, while the self-attention mechanism in Transformer can solve the drawback of applying LSTM to capture the sequence information weakly at a distance. The advantage of complementarity of the fusion model in the training process of trajectory sequences with respect to the long-range dependence of temporal and spatial features is realized. Finally, in the comparative analysis section of the error metrics, by comparing with current state-of-the-art methods, the algorithm in this paper is shown to have higher accuracy in predicting time series trajectory data. The research in this paper provides an early warning information reference for autonomous navigation and autonomous collision avoidance of ships in practice.

The continuous growth of global trade and maritime transport has significantly heightened the challenges of managing ship traffic in port waters, particularly within fairways. Effective traffic management in these channels is crucial not only for ensuring navigational safety but also for optimizing port efficiency. A deep understanding of ship behavior within fairways is essential for effective traffic management. This paper applies machine learning techniques, including Decision Tree, Random Forest, and Gradient Boosting Regression, to model and analyze the behavior of various types of ships at specific moments within fairways. The study focuses on predicting four key behavioral parameters: latitude, longitude, speed, and heading. The experimental results reveal that the Random Forest model achieves adjusted $R^2$ scores of 0.9999 for both longitude and latitude, 0.9957 for speed, and 0.9727 for heading. All three models perform well in accurately predicting ship positions at different times, with the Random Forest model particularly excelling in speed and heading predictions. It effectively captures the behavior of ships within fairways and provides accurate predictions for different types and sizes of vessels, especially in terms of speed and heading variations as they approach or leave berths. This model offers valuable support for predicting ship behavior, enhancing ship traffic management, optimizing port scheduling, and detecting anomalies.

Ship trajectory data can be used in most marine-related research, and most ship trajectory data come from AIS. The large number of ships and the short reporting period of AIS have resulted in a huge amount of ship trajectory data, which has caused a certain amount of pressure on the relevant research. This paper proposes a direction-preserved vessel trajectory compression method based on Open Window, which can effectively retain the direction change feature points while ensuring the position error. In addition, the method can work in both offline and online modes. Also, the velocity threshold and low-speed redundancy points problems are considered. In order to verify the effect of the proposed method and the adaptability of the method to different feature data, a parallel experiment was performed on port water and coastal water datasets. The results show that our method can compress vessel trajectories while retaining the feature points. Meanwhile, by comparing it with the compression method that is based on distance thresholds, the vessel trajectory compress time is drastically reduced by up to 87.3% in the port water data. The research in this paper provides a new method through which to compress vessel trajectories for research in marine-related fields on vessel trajectory.

This study investigates the spatiotemporal density aggregation and pattern distribution of vessel traffic amidst bustling maritime logistics scenarios. Firstly, a relatively new spatiotemporal segmentation and reconstruction method is proposed for ship AIS trajectories to address trajectory disruptions caused by berthing, anchorage, and other factors. Subsequently, a trajectory filtering algorithm utilizing time window panning is introduced to mitigate position jumps and deviation errors in trajectory points, ensuring that the dynamic trajectory adheres to the spatiotemporal correlations of ship motion. Secondly, to establish a geographical spatial mapping of dynamic trajectories, spatial gridding is applied to maritime traffic areas. By associating the geographical space of traffic activities with the temporal attributes of dynamic trajectories, a dynamic trajectory temporal density model is constructed. Finally, a case study is conducted to evaluate the effectiveness and applicability of the proposed method in identifying spatiotemporal patterns of maritime traffic and spatiotemporal density aggregation states. The results show that the proposed method can identify dynamic trajectory traffic patterns after the application of compression algorithms, providing a novel approach to studying the spatiotemporal aggregation of maritime traffic in the era of big data.

Due to the development of ship intelligence, the demand for precise controllers is increasing day by day. In traditional design, the trial-and-error method often leads to the use of inaccurate controller parameters, causing oscillations when environmental disturbances are present. To address these issues, this study introduces deep learning to optimize the controller parameters, particularly those used for sliding mode controllers in underactuated ships. We adopt the Deep Deterministic Policy Gradient (DDPG) algorithm based on the Actor Critic framework. By freely setting the reward function to match the control objective, the trained model can output more accurate controller parameters, with the resulting change rates for the robustness-related parameters being 38%, 27.5%, 5%, and 200%, respectively. The results of the simulation comparison experiment show that the parameter-optimized sliding mode controller improves the path-tracking accuracy and rudder angle stability by about 20%, which is significantly better than that of the controller parameterized by the traditional trial-and-error method. It also more effectively meets the control requirements of intelligent ships.

To address the challenge of recognizing ship states accurately amidst the complexities of marine environments, this study proposes a novel ship state recognition approach leveraging a graph convolutional neural network (GCNN). Initially, the method extracts canonical and efficient ship motion trajectories from AIS data. Subsequently, a state recognition network tailored for ship motion trajectories is devised and implemented employing graph convolution. Notably, the accuracy of this model is enhanced through the introduction of novel weights and optimization of the Adj parameter. Experimental evaluations conducted on a ship state dataset demonstrate significant performance improvements. Specifically, the proposed recognition network achieves a recognition accuracy of 98.3% for regulated ship trajectories, marking an impressive 8.4% enhancement over traditional convolutional neural networks. This advancement holds promise for enhancing ship state recognition accuracy across diverse maritime applications including maritime supervision, navigation safety, and ship management.