Adaptive Douglas-Peucker Algorithm With Automatic Thresholding for AIS-Based Vessel Trajectory Compression

• Published in: IEEE access Vol. 7; pp. 150677 - 150692
• Main Authors: Liu, Jingxian, Li, Huanhuan, Yang, Zaili, Wu, Kefeng, Liu, Yi, Liu, Ryan Wen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Algorithms; Artificial intelligence; Classification; Classification algorithms; Clustering; Clustering algorithms; Data mining; Douglas-Peucker algorithm; Euclidean distance; Inland waterways; maritime safety; Radar equipment; Safety; Satellite constellations; Segmentation; Traffic safety; Trajectory; trajectory classification; trajectory clustering; trajectory compression; Vessel traffic services; Vessels
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2947111

Automatic identification system (AIS) is an important part of perfecting terrestrial networks, radar systems and satellite constellations. It has been widely used in vessel traffic service system to improve navigational safety. Following the explosion in vessel AIS data, the issues of data storing, processing, and analysis arise as emerging research topics in recent years. Vessel trajectory compression is used to eliminate the redundant information, preserve the key features, and simplify information for further data mining, thus correspondingly improving data quality and guaranteeing accurate measurement for ensuring navigational safety. It is well known that trajectory compression quality significantly depends on the threshold selection. We propose an Adaptive Douglas-Peucker (ADP) algorithm with automatic thresholding for AIS-based vessel trajectory compression. In particular, the optimal threshold is adaptively calculated using a novel automatic threshold selection method for each trajectory, as an improvement and complement of original Douglas-Peucker (DP) algorithm. It is developed based on the channel and trajectory characteristics, segmentation framework, and mean distance. The proposed method is able to simplify vessel trajectory data and extract useful information effectively. The time series trajectory classification and clustering are discussed and analysed based on ADP algorithm in this paper. To verify the reasonability and effectiveness of the proposed method, experiments are conducted on two different trajectory data sets in inland waterway of Yangtze River for trajectory classification based on the nearest neighbor classifier, and for trajectory clustering based on the spectral clustering. Comprehensive results demonstrate that the proposed algorithm can reduce the computational cost while ensuring the clustering and classification accuracy.