Trajectory Time Series Compression Algorithm Based on Unsupervised Segmentation

Aiming at the problem of ignoring the importance of starting point features of trajecory segmentation in existing trajectory compression algorithms, a study was conducted on the preprocessing process of trajectory time series. Firstly, an algorithm improvement was proposed based on the segmentation algorithm GRASP-UTS(Greedy Randomized Adaptive Search Procedure for Unsupervised Trajectory Segmentation). On the basis of considering trajectory coverage, this algorithm designs an adaptive parameter adjustment to segment long-term trajectory data reasonably and the identification of an optimal starting point for segmentation. Then the compression efficiency of typical offline and online algorithms, such as the Douglas-Peucker algorithm, the Sliding Window algorithm and its enhancements, was compared before and after segmentation. The experimental findings highlight that the Adaptive Parameters GRASP-UTS segmentation approach leads to higher fitting precision in trajectory time series compression and improved algorithm efficiency post-segmentation. Additionally, the compression performance of the Improved Sliding Window algorithm post-segmentation showcases its suitability for trajectories of varying scales, providing reasonable compression accuracy.