Umar Zaman1, Junaid Khan2, Eunkyu Lee1,3, Sajjad Hussain4, Awatef Salim Balobaid5, Rua Yahya Aburasain5, Kyungsup Kim1,2,*
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1789-1808, 2024, DOI:10.32604/cmc.2024.056222
- 15 October 2024
Abstract Maritime transportation, a cornerstone of global trade, faces increasing safety challenges due to growing sea traffic volumes. This study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System (AIS) data and advanced deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (DBLSTM), Simple Recurrent Neural Network (SimpleRNN), and Kalman Filtering. The research implemented rigorous AIS data preprocessing, encompassing record deduplication, noise elimination, stationary simplification, and removal of insignificant trajectories. Models were trained using key navigational parameters: latitude, longitude, speed, and heading. Spatiotemporal aware processing through trajectory segmentation… More >
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