@article {43173,
	title = {Interpolation-Based Inference of Vessel Trajectory Waypoints from Sparse AIS Data in Maritime},
	journal = {Journal of Marine Science and Engineering},
	volume = {11},
	number = {615},
	year = {2023},
	publisher = {Ocean Engineering},
	author = {Zaman, Bakht and Marijan, Dusica and Kholodna, Tetyana}
}
@article {43331,
	title = {Reactive buffering window trajectory segmentation: RBW-TS},
	journal = {Journal of Big Data},
	volume = {10},
	number = {123},
	year = {2023},
	month = {07/2023},
	publisher = { Springer},
	doi = {https://doi.org/10.1186/s40537-023-00799-0},
	author = {Zaman, Bakht and Altan, Dogan and Marijan, Dusica and Kholodna, Tetyana}
}
@article {43087,
	title = {SafeWay: Improving the Safety of Autonomous Waypoint Detection in Maritime using Transformer and Interpolation},
	journal = {Maritime Transport Research},
	volume = {4},
	number = {100086},
	year = {2023},
	note = {},
	month = {03/2023},
	publisher = { Elsevier},
	abstract = {},
	issn = {2666-822X},
	doi = {https://doi.org/10.1016/j.martra.2023.100086},
	author = {Altan, Dogan and Marijan, Dusica and Kholodna, Tetyana}
}
@inproceedings {42454,
	title = {Discovering Gateway Ports in Maritime using Temporal Graph Neural Network Port Classification},
	journal = {35th Canadian Conference on Artificial Intelligence},
	year = {2022},
	note = {},
	publisher = {Canadian Artificial Intelligence Association},
	abstract = {Vessel navigation is influenced by various factors, such as dynamic environmental factors that change over time or static features such as vessel type or depth of the ocean. These dynamic and static navigational factors impose limitations on vessels, such as long waiting times in regions outside the actual ports, and we call these waiting regions gateway ports. Identifying gateway ports and their associated features such as congestion and available utilities can enhance vessel navigation by planning on fuel optimization or saving time in cargo operation. In this paper, we propose a novel temporal graph neural network (TGNN) based port classification method to enable vessels to discover gateway ports efficiently, thus optimizing their operations. The proposed method processes vessel trajectory data to build dynamic graphs capturing spatio-temporal dependencies between a set of static and dynamic navigational features in the data, and it is evaluated in terms of port classification accuracy on a real-world data set collected from ten vessels operating in Halifax, NS, Canada. The experimental results indicate that our TGNN-based port classification method provides an f-score of 95\% in classifying ports.},
	keywords = {actual ports, AIS data, gateway ports, maritime situational awareness, maritime traffic, port area, port classification, port congestion, spatio-temporal, temporal graph neural networks, vessel trajectories},
	doi = {https://doi.org/10.21428/594757db.a76bcb9d},
	author = {Altan, Dogan and Etemad, Mohammad and Marijan, Dusica and Kholodna, Tetyana}
}
