Qing-He Zhao

Vessel ETA Prediction Integrating BiLSTM with Attention Mechanism Using AIS Data

A deep learning method that combines unidirectional (UniLSTM) and bidirectional long short-term memory (BiLSTM) networks with an attention mechanism to predict ship estimated time of arrival (ETA) shows that BiLSTM performs better than the UniLSTM, and the attention mechanism further improves prediction accuracy.