Advanced deep learning approaches for maritime trajectory prediction leveraging automatic identification system data

Hajiaghaei Keshteli, MostafaFamilsamavati, Sajad2025-05-092023Familsamavati, S. (2024). Advanced deep learning approaches for maritime trajectory prediction leveraging automatic identification system data. [Tesis maestría]. Instituto Tecnológico y de Estudios Superiores de Monterrey. Recuperado de: https://hdl.handle.net/11285/703639https://hdl.handle.net/11285/703639https://orcid.org/0000-0002-9988-2626This study investigates the efficacy of advanced DL models, specifically Bi-GRU, LSTM, and Bi-LSTM, for predicting maritime vessel trajectories using AIS data. The study focuses on doing comparative analysis of prediction accuracy in high-traffic maritime environments, particularly the Port of Manzanillo. Comprehensive AIS data preprocessing, feature engineering, and normalization were conducted to prepare the data for model training. The Bi-GRU model emerged as the most effective, demonstrating superior performance with the lowest test loss, MAE, and MSE, highlighting its capability in capturing sequential dependencies in vessel trajectories. The research contributes significantly to maritime traffic management by offering a predictive framework that enhances safety and efficiency in dynamic maritime operations. Future research directions include integrating additional data sources and extending model applications across various maritime regions.TextoengopenAccesshttp://creativecommons.org/licenses/by/4.0INGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA NAVAL::BARCOSNaval ScienceAdvanced deep learning approaches for maritime trajectory prediction leveraging automatic identification system dataTesis de Maestría / master ThesisPeriodo predeterminado para revisión de contenido susceptible de protección, patente o comercializaciónhttps://orcid.org/0000-0003-4769-6554Automatic Identification SystemLSTMGRUDeep learningTrajectory prediction1246845