Wide-Area Ship Movement Prediction Using Random Forests

: Vähämäki,Tanja; Farahnakian, Farshad; Nevalainen, Paavo; Heikkonen, Jukka

: Razminia, Abolhassan; Nguyen, Dinh Hoa

: International Symposium on Intelligent Technology for Future Transportation

Publisher: Springer Nature Switzerland

: 2025

: Communications in Computer and Information Science

: Intelligent Technology for Future Transportation: First International Symposium, ITFT 2024, Helsinki, Finland, October 19–21, 2024, Proceedings

: Communications in Computer and Information Science

: 2378

: 220

: 245

: 978-3-031-84147-7

: 978-3-031-84148-4

: 1865-0929

: 1865-0937

DOI: https://doi.org/10.1007/978-3-031-84148-4_18(external)

: https://doi.org/10.1007/978-3-031-84148-4_18(external)

Maritime situational awareness requires real-time traffic prediction over a large area based on the Automatic Identification System (AIS). The second requirement is allowing input from all the traffic. We propose Random Forests (RF) for ship movement prediction and demonstrate how it can be adapted to varying zone shapes and anomaly detection tasks. We also apply it to the clustering of vessels to regularly and irregularly moving ships. Our research area is the Baltic Sea and the recording period of data is 26 July 2022... 12 August 2022. Results from the class of regularly behaving ships (499 ships out of 634) show 0.2... 2.1 km mean absolute error (MAE) over 15 min... 2 h which reaches the same accuracy as many published cases with more expensive computational models. The prediction for all supported time intervals can be updated every 10 min, which makes the implementation practical for large-scale situational awareness systems.