一种加入时间维的船舶轨迹高效离线压缩算法研究

全球船舶轨迹大数据加工的一个重要步骤是对船舶轨迹进行压缩。以经典船舶轨迹离线压缩算法思路为基础,探讨了传统算法在压缩效果和压缩效率方面存在的问题。例如,经典Douglas-Peucker压缩算法没有考虑时间维,导致压缩后的数据对船舶的速度与航行状态信息的保留效果不佳。但是,动态Douglas-Peucker算法虽然考虑了时间维,却因此增加了计算量,导致压缩效率不高。在上述算法的基础上,将考虑时间维的轨迹压缩算法对应到向量空间中,利用向量的内积、外积的几何性质,对算法压缩效率和效果进行了改善,并提出了快速Douglas-Peucker算法。经过实验验证,该算法较经典算法效率提高了约30%以上,压缩效果比已有算法更优。

Offline Efficient Compression Algorithm for AIS Data Retains Time Elapsing Dimension

Ship track compressing is an important step in global ship trace big data processing.This paper firstly expounded some existing offline compression algorithms for ship trajectory and also exhibited their problems.For example,classic Douglas-Peucker compression algorithm just compresses the ship trajectory in two dimensions,losing time element,which makes that the compression algorithm cannot keep the information of speed and status of ships.On the other hand,although dynamic Douglas-Peucker compression algorithm needs to divide ship trajectory into different groups before compression,it will yet result in some efficiency problems.The compression algorithms which takes into account time dimensions was token to spatial vector.In order to improve the effect and efficiency,we used inner product and outer product.Through experiment,we found that the efficiency of the algorithm had a 30% increase and a better effect in most cases.The innovation is putting the inner product and outer product into compression algorithms.