摘要
船舶轨迹数据是目前船舶轨迹数据挖掘研究的重要数据源。然而，船舶AIS数据采样间隔短，数据冗余量大，影响了AIS数据的有效利用。为了有效去除AIS数据中的冗余信息，提高AIS数据的使用效率，提出了一种考虑关键区域识别的船舶轨迹数据压缩算法(VATDC_CCRI)。VATDC_CRI算法通过分析节点变化率的分布来识别船舶轨迹的关键区域。采用douglas–peucker(DP)算法对这些关键区域的数据进行压缩，减少了压缩后弹道的失真。此外，该算法利用滑动窗口方法处理初始轨迹，以提高压缩血管轨迹的质量，并尽可能多地保留原始轨迹的时空特征。该算法将船舶轨迹关键区域的特征节点与滑动窗口算法的结果相结合,有效地压缩了船舶轨迹。在单个和多个轨迹上的实验表明,与其他经典的船舶轨迹压缩算法相比,vatdc_ccri算法在保持船舶轨迹形状的同时,具有更高的压缩率和更快的处理速度。
Abstract
Vessel trajectory data are currently the most important data source for vessel trajectory data mining research. However, vessel AIS data have a short sampling time interval and a large amount of data redundancy, which hampers the efficient utilization of AIS data. In order to effectively remove redundant information from AIS data and improve its usage efficiency, a compression algorithm for vessel trajectory data compression algorithm considering critical region identification (VATDC_CCRI) is proposed. The VATDC_CCRI algorithm identifies the critical regions of a vessel’s trajectory by analyzing the distribution of node variation rates. It employs the Douglas–Peucker (DP) algorithm to compress the data in these critical regions, reducing the distortion of the trajectory after compression. Additionally, the algorithm utilizes a sliding window approach to process the initial trajectory to improve the quality of the compressed vessel trajectories and retain as many spatiotemporal characteristics of the original trajectories as possible. It combines the feature nodes from the crucial regions in the vessel’s trajectory with the results obtained from the sliding window algorithm, effectively compressing the vessel’s trajectory. Experiments conducted on individual and multiple trajectories demonstrate that the VATDC_CCRI algorithm achieves higher compression rates and exhibits faster processing speeds compared to other classical vessel trajectory compression algorithms while preserving the shape of the vessel’s trajectory significantly.
NETL
NSTL
Wiley-Hindawi