基于划分的自适应轨迹拐点提取压缩算法

海量的轨迹数据为管理分析和数据挖掘工作带来了巨大的挑战, 轨迹压缩技术成为解决这一问题的一种有效方案. 针对目前多数轨迹压缩算法需要人为干预设定阈值的问题, 融合特征聚类与轨迹划分的思想提出了一种自适应的轨迹拐点提取压缩算法. 算法从轨迹的全局方向特征与局部方向特征出发考虑, 依次进行了轨迹粗划分、子轨迹合并以及轨迹细划分的工作. 实验结果显示, 随着轨迹规模的增大, 与其他算法相比, 该算法基本能够在保持更高压缩率的同时产生更低的方向误差. 提出的算法具有自适应和高精度拐点识别的优势, 在其他轨迹压缩场景之下仍有着较高的参考价值.

Adaptive Trajectory Inflexion Extraction and Compression Algorithm Based on Partition

Massive trajectory data pose challenges to management analysis and data mining, and trajectory compression technology has become an effective solution to this problem. Aiming at the problem that most current trajectory compression algorithms need human intervention to set thresholds, this study proposes an adaptive trajectory inflection point extraction and compression algorithm which combines the idea of feature clustering and trajectory partition. Based on the global and local direction characteristics of the trajectory, the algorithm carries out the rough trajectory division, sub-trajectory merging, and fine trajectory division. The experimental results show that with the increasing trajectory size, the proposed algorithm can produce lower direction error and maintain a higher compression rate than other algorithms. The algorithm features adaptive and high-precision inflection point recognition and still has a high reference value under other trajectory compression scenarios.