Abstract
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.
Highlights
For a ship to avoid collision, it must predict the behaviors of other ships in order to estimate the collision risk
We inferred the following from our experiment: cause the rebound phenomenon to occur, it can quickly converge and stabilize in a short time
An recurrent neural network (RNN) can remember the common features of the automatic identification system (AIS) big data and forget personality
Summary
For a ship to avoid collision, it must predict the behaviors of other ships in order to estimate the collision risk. Trajectory data are input to a fixed formula or trained model. Xu et al [2] trained a three-layer back-propagation (BP) neural network to accept the ship’s direction and speed as inputs and yield differences in the latitude and longitude as output. Zhen et al [5] constructed a three-layer BP neural network in which AIS data from the previous three points were used as the input and the fourth point was given as the output to predict the navigation behavior of the ship. Zhao et al [6] used an improved Kalman filter algorithm to predict ship trajectories. The. algorithm was proposed by Sakfilters [28] to establish selective memory, and solves the problem of RNN for long-time data. Human operator’s better understanding of complex conditions at sea and enhance decision-making to
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