AISClean: AIS data-driven vessel trajectory reconstruction under uncertain conditions
Maohan Liang, Jianlong Su, Ryan Wen Liu, Jasmine Siu Lee Lam
Abstract
In maritime transportation, intelligent vessel surveillance has become increasingly prevalent and widespread by collecting and analyzing high massive spatial data from automatic identification system (AIS). The state-of-the-art AIS devices contain various functionalities, such as position transmission, tracking navigation, etc. Widely equipped shipboard AIS devices provide a large amount of real-time and historical vessel trajectory data for maritime management. However, the original AIS data often suffers from unwanted noise (i.e., poorly tracked timestamped points for vessel trajectories) and missing (i.e., no data is received or transmitted for a long term) data during signal acquisition, transmission, and analog-to-digital conversion. This degradation in data quality poses significant risks, including potential miscalculations in vessel collision avoidance systems, inaccuracies in emission calculations, and challenges in port management. In this work, a data-driven vessel trajectory reconstruction framework considering historical features is proposed to enhance the reliability of vessel trajectory. Specifically, a series of statistical methods are proposed to identify noisy data and missing data. Then, a model combining Geohash and dynamic time warping algorithms is developed to restore the trajectories degraded by random noise and missing data in vessel trajectories. Comparative experiments with baseline methods on multiple datasets verify the effectiveness of the proposed data-driven model.