Predicting vessel speed in the Arctic without knowing ice conditions using AIS data and decision trees

• Published in: Maritime Transport Research
• Main Authors: Rao, Prithvi S., Kim, Ekaterina, Smestad, Bjørnar Brende, Asbjørnslett, Bjørn Egil, Bhattacharyya, Anirban
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier Science 2021
• Subjects: Maskinlæring; Machine learning; Skipsfart; Shipping; Arktis; Arctic; VDP::Kunnskapsbaserte systemer: 425; VDP::Knowledge-based systems: 425; Barents Sea; Kara Sea; Sabetta; Sog; Arktis*; ice covered waters
• Online Access: https://hdl.handle.net/11250/3029508; https://doi.org/10.1016/j.martra.2021.100024

The vessel speed is one of the important parameters that govern safety, emergency, and transport planning in the Arctic. While previous studies have traditionally relied on physics-based simulations to predict vessel's speed in ice-covered waters, most have not fully explored data-driven approa...