The real-time prediction of ship behavior plays an important role in navigation and intelligent collision avoidance systems. This study developed an online real-time ship behavior prediction model by constructing a bidirectional long short-term memory recurrent neural network (BI-LSTM-RNN) that is suitable for automatic identification system (AIS) date and time sequential characteristics, and for online parameter adjustment. The bidirectional structure enhanced the relevance between historical and future data, thus improving the prediction accuracy. Through the “forget gate” of the long short-term memory (LSTM) unit, the common behavioral patterns were remembered and unique behaviors were forgotten, improving the universality of the model. The BI-LSTM-RNN was trained using 2015 AIS data from Tianjin Port waters. The results indicate that the BI-LSTM-RNN effectively predicted the navigational behaviors of ships. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could potentially be applied as the predictive foundation for various intelligent systems, including intelligent collision avoidance, vessel route planning, operational efficiency estimation, and anomaly detection systems.

According to the statistics of water transportation accidents, collision accidents are on the rise as the shipping industry has expanded by leaps and bounds, and the water transportation environment has become more complex, which can result in grave consequences, such as casualties, environmental destruction, and even massive financial losses. In view of this situation, high-precision and real-time ship trajectory prediction based on AIS data can serve as a crucial foundation for vessel traffic services and ship navigation to prevent collision accidents. Thus, this paper proposes a high-precision ship track prediction model based on a combination of a multi-head attention mechanism and bidirectional gate recurrent unit (MHA-BiGRU) to fully exploit the valuable information contained in massive AIS data and address the insufficiencies in existing trajectory prediction methods. The primary advantages of this model are that it allows for the retention of long-term ship track sequence information, filters and modifies ship track historical data for enhanced time series prediction, and models the potential association between historical and future ship trajectory status information with the current state via the bidirectional gate recurrent unit. Significantly, the introduction of a multi-head attention mechanism calculates the correlation between the characteristics of AIS data, actively learns cross-time synchronization between the hidden layers of ship track sequences, and assigns different weights to the result based on the input criterion, thereby enhancing the accuracy of forecasts. The comparative experimental results also verify that MHA-BiGRU outperforms the other ship track prediction models, demonstrating that it possesses the characteristics of ease of implementation, high precision, and high reliability.

Large volumes of automatic identification system (AIS) data provide new ideas and methods for ship data mining and navigation behavior pattern analysis. However, large volumes of big data have low unit values, resulting in the need for large-scale computing, storage, and display. Learning efficiency is low and learning direction is blind and untargeted. Therefore, key feature point (KFP) extraction from the ship trajectory plays an important role in fields such as ship navigation behavior analysis and big data mining. In this paper, we propose a ship spatiotemporal KFP online extraction algorithm that is applied to AIS trajectory data. The sliding window algorithm is modified for application to ship navigation angle deviation, position deviation, and the spatiotemporal characteristics of AIS data. Next, in order to facilitate the subsequent use of the algorithm, a recommended threshold range for the corresponding two parameters is discussed. Finally, the performance of the proposed method is compared with that of the Douglas–Peucker (DP) algorithm to assess its feature extraction accuracy and operational efficiency. The results show that the proposed improved sliding window algorithm can be applied to rapidly and easily extract the KFPs from AIS trajectory data. This ability provides significant benefits for ship traffic flow and navigational behavior learning.