Adaptive Douglas-Peucker Algorithm With Automatic Thresholding for AIS-Based Vessel Trajectory Compression
Adaptive Douglas-Peucker Algorithm With Automatic Thresholding for AIS-Based Vessel Trajectory Compression
复制标题
基于 AIS 的血管轨迹压缩的具有自动阈值的自适应 Douglas-Peucker 算法
DOI：
10.1109/access.2019.2947111
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Liu, Ryan Wen
中科院分区：
文献类型：
--
作者：
Liu, Jingxian;Li, Huanhuan;Liu, Ryan Wen
自动识别系统（AIS）是完善地面网络、雷达系统和卫星星座的重要组成部分。它已被广泛应用于船舶交通服务系统，以提高航行安全。随着船舶AIS数据的爆炸式增长，数据存储、处理和分析问题成为近年来新兴的研究课题。船舶航迹压缩技术可以消除冗余信息，保留关键特征，简化信息，便于进一步数据挖掘，从而相应提高数据质量，保证测量准确，保障航行安全。众所周知，轨迹压缩质量显著地取决于阈值选择。我们提出了一种自适应道格拉斯-Peucker（ADP）算法与自动阈值为基础的AIS血管轨迹压缩。特别地，最佳阈值的自适应计算使用一种新的自动阈值选择方法为每个轨迹，作为原始的道格拉斯-Peucker（DP）算法的改进和补充。它是基于通道和轨迹特征、分割框架和平均距离开发的。该方法能够有效地简化船舶轨迹数据，提取有用信息。本文基于ADP算法对时间序列的轨迹分类和聚类进行了讨论和分析。为了验证该方法的合理性和有效性，在长江内河两组不同的航迹数据集上进行了基于最近邻分类器的航迹分类和基于谱聚类的航迹聚类实验。综合实验结果表明，该算法在保证聚类和分类精度的同时，降低了计算代价。
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.