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Prediction oof Vessel Trajectories From AIS Data Via Sequence-To-Sequence Recurrent Neural Networks
پیش بینی مسیرهای کشتی از داده های AIS از طریق شبکه های عصبی تکرار شونده به ترتیب-2020 In this paper, we address the problem of predicting vessel
trajectories based on Automatic Identification System (AIS)
data. The goal is to learn the predictive distribution of maritime
traffic patterns using historical data during the training
phase, in order to be able to forecast future target trajectory
samples online on the basis of both the extracted knowledge
and the available observation sequence. We explore neural
sequence-to-sequence models based on the Long Short-Term
Memory (LSTM) encoder-decoder architecture to effectively
capture long-term temporal dependencies of sequential AIS
data and increase the overall predictive power. The experimental
evaluation on a real-world AIS dataset demonstrates
the effectiveness of sequence-to-sequence recurrent neural
networks (RNNs) for vessel trajectory prediction and shows
their potential benefits compared to model-based methods. Index Terms: Vessel trajectory prediction | recurrent neural networks | sequence-to-sequence models | LSTM | AIS |
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