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.