A Trajectory Compression Method Based on Sliding Window

Abstract

The invention discloses a trajectory compression method based on a sliding window. Firstly, the current trajectory point to be compressed in the sliding window and the vertical distance of a straight line from a reference trajectory point with a maximum offset distance to its initial trajectory point and an end trajectory point are respectively calculated; Then make a judgment: if any of these two distances is greater than the distance threshold for trajectory compression, add the current trajectory point to be compressed to the compressed trajectory set, and use the current trajectory point to be compressed as the start of the sliding window track point to set a new sliding window; otherwise, according to the size of the two distances, update the maximum offset distance reference track point, and move the current track point to be compressed and the end track point of the sliding window back by one position at the same time. The above processing is repeated until the compression of all track points is completed. The method can effectively speed up the track compression processing speed, improve the operation efficiency, and reduce the complexity of the algorithm.

Description

technical field [0001] The invention relates to a trajectory compression method based on a sliding window, which belongs to the application field of geographic information systems (GIS). Background technique [0002] In recent years, with the widespread use of civilian GPS and other positioning devices on mobile terminals and the development and popularization of Location-Based Service (Location-Based Service, LBS) and Mobile Social Network (Mobile Social Network), users can easily obtain personal location Information, researchers can also easily obtain trajectory paths through location-aware devices. Therefore, more and more trajectory data of moving objects are collected and stored in the moving object database. As the historical activity data of moving objects, trajectory data can reflect the internal and external characteristics of moving objects such as attributes, states, and behaviors to a certain extent. However, these huge data volumes cause great difficulties for ...

AI Technical Summary

Problems solved by technology

[0008] However, when the sliding window algorithm and the ending track point of the Normal Opening Window change, it is necessary to calculate the vertical distance from all track points between the starting track point and the ending track point of the sliding window to the straight line between the starting track point and the ending track point. Many scholars believe that this will affect the computational efficiency of the algorithm, and the processing time will be longer

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