PSOTSC: A Global-Oriented Trajectory Segmentation and Compression Algorithm Based on Swarm Intelligence

Zhihong Ouyang,Lei Xue,Da Li,Feng Ding

doi:10.3390/ijgi10120817

Abstract

Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. High quality segmentation and compression need to accurately select and store as few points as possible that can reflect the characteristics of the original trajectory, while the existing methods still have room for improvement in segmentation accuracy, reduction of compression rate and simplification of algorithm parameter setting. A trajectory segmentation and compression algorithm based on particle swarm optimization is proposed. First, the trajectory segmentation problem is transformed into a global intelligent optimization problem of segmented feature points, which makes the selection of segmented points more accurate; then, a particle update strategy combining neighborhood adjustment and random jump is established to improve the efficiency of segmentation and compression. Through experiments on a real data set and a maneuvering target simulation trajectory set, the results show that compared with the existing typical methods, this method has advantages in segmentation accuracy and compression rate.

Highlights

Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining
Compared with DP, trajectory clustering (TRACLUS) and TCSS typical segmentation and compressi algorithms, the advantage of the PSOTSC algorithm is verified on real trajectory data algorithms, the advantage of the algorithm is verified on real set and maneuvering target simulation data set
Compression ratio. the particle update strategy based on neighborhood adjustment and random jump (NARJ) is established, which can effectively improve the search efficiency of trajectory segmentation solution

Summary

Introduction

Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. The second is a trajectory data compression based on road network structure [18,19,20,21,22]; the trajectory points are mapped onto the road network, and the original trajectory is represented by a grid structure to reduce the amount of data. This kind of method is not suitable. This kind of method often ignores the movement characteristics of the target, and it is difficult to obtain the semantic features of the trajectory

Results

Discussion

Conclusion