基于多重运动特征的轨迹相似性度量模型

针对目前只能对单一运动特征（速度、方向等）进行轨迹相似性分析的不足，提出了基于多重运动特征的轨迹相似性度量，该度量对于分析和理解移动对象的运动行为和规律具有重要意义。将其应用于基于多重运动特征的运动序列模式发现。该相似性度量借鉴数据立方体的思想，将多重运动特征时间序列进行量化和符号化表示，在多重运动特征值域空间中计算两字符间的距离作为字符间替换代价，最终以加权编辑距离作为相似性度量。将该相似性度量与谱聚类方法相结合进行运动序列模式发现。实验以飓风数据为例，通过气象文献中飓风的发生与运动规律验证了模型的有效性。

Trajectory Similarity Measure Based on Multiple Movement Features

For the shortcoming that existing methods can only measure the trajectory similarity of single movement feature (e.g. velocity, acceleration), the trajectory similarity measure based on multiple movement features is proposed. The measure is significant for analyzing and understanding the movement behaviors and mechanisms of moving objects. The measure borrows the idea of data cube, quantizes and symbolizes the multiple movement feature time series. In multiple movement feature domain space, the Euclidean distances between characters are computed as the substitution costs of weighted edit distance which is computed as the similarity measure. The measure is integrated with the spectral clustering method for movement sequential pattern discovery. Using the hurricane dataset, the known hurricane originating and movement laws in meteorological literatures verify the effectiveness of the measure.