AIS-Based Intelligent Vessel Trajectory Prediction Using Bi-LSTM

Abstract

Accurate vessel trajectory prediction is essential for maritime traffic control and management. In addition to collision avoidance, accurate vessel trajectory prediction can help in planning navigation routes, shortening the sailing distance, and increasing navigation efficiency. Vessel trajectory prediction with automatic identification system (AIS) data has thus attracted considerable attention in the maritime industry. Original AIS data may contain noise, which limits their application in real-world maritime traffic management. To overcome this problem, this study proposes a vessel trajectory prediction method that combines data denoising and a deep learning prediction model. In this method, data denoising is realized in three steps: trajectory separation, data denoising, and standardization. First, outliers from the original AIS data samples are removed, after which the moving average model is employed to further clean up the data; finally the denoised data are standardized into uniformly distributed time-series data. Bidirectional long short-term memory (Bi-LSTM) is then applied for vessel trajectory prediction. The performance of the proposed prediction model was verified using data on the trajectories of ten vessels and comparing the results obtained with those obtained using other prediction models (exponential smoothing, autoregressive integrated moving average, support vector regression, recurrent neural network, and LSTM models); the trajectory data were downloaded from a public AIS database. The experimental results revealed that model prediction accuracy increased after the data denoising process. Specifically, the Bi-LSTM model had the lowest mean absolute error, mean absolute percentage error, and root-mean-square error, demonstrating that the proposed method is highly efficient for trajectory prediction and can help vessel traffic controllers predict accurate vessel tracks; this would enable them to take early preventive measures to avoid collisions and thus improve the efficiency and safety of maritime traffic.

Deep learning based ship trajectory prediction with AIS

“…They optimized the internal parameters of the model with the Adaptive Chaos Differential Evolution (ACDE) algorithm, thereby enhancing both the convergence speed and prediction accuracy. Yang et al employed Bidirectional Long Short-Term Memory (Bi-LSTM) for ship trajectory prediction, demonstrating superior performance compared to traditional LSTM models [7]. However, their direct processing of the entire trajectory dataset overlooked local features and dynamic changes within the data, resulting in lower accuracy and efficiency in the prediction results.Murray et al [8][9] conducted pattern classification on the selected ships and utilized an enhanced subset of data corresponding to the future behavior of the selected ships to improve trajectory prediction performance.…”

A novel approach based on LSTM and self-attention mechanism for vessel trajectory prediction

“…Tang et al 5 adopt an LSTM model with four hidden layers to model the vessel trajectory. Wang et al 6 selected bidirectional GRU (Bi-GRU), while Yang et al 7 selects a similar approach by using bidirectional LSTM (Bi-LSTM) to model the trajectory sequence. However, models like this only enable single-step prediction, and multi-step prediction can lead to error accumulation and a significant reduction in prediction accuracy.…”

Research into Ship Trajectory Prediction Based on An Improved LSTM Network

“…This method integrates the data on the nearest time of the ship and proposes an attention mechanism to improve feature-extraction efficiency, thereby realizing the fusion data trajectory prediction. Yang et al [9] proposed a Bi-LSTM for ais-based intelligent vessel trajectory prediction. In this method, AIS data are cleaned using a moving average model and standardized to form uniformly distributed time series data.…”