A spatio-temporal trajectory compression method based on trajectory dictionary

Abstract

The invention discloses a spatio-temporal trajectory compression method based on a trajectory dictionary, comprising two parts of establishing a trajectory dictionary set and performing trajectory compression based on the trajectory dictionary set. In this manner, for spatial trajectory data and temporal trajectory data, three methods of trajectory establishment and two algorithms of trajectory compression are proposed, which guarantee the compression rate, guarantee the high quality of trajectory compression, and have the advantages of high reliability and high accuracy. At the same time, themethod has a wide market prospect in database, data analysis, data mining, trajectory data query and analysis, and the application and popularization of trajectory data mining.

Description

technical field [0001] The invention relates to the fields of database design, spatiotemporal data management, data analysis, data mining, trajectory data analysis, and trajectory data mining, and in particular relates to a spatiotemporal trajectory compression method based on a trajectory dictionary set. Background technique [0002] Due to the widespread popularization of GPS mobile positioning equipment and wireless communication technologies (such as Wi-Fi, 3G, etc.), the amount of trajectory data of moving objects has increased exponentially. These trajectory data contain rich information, reflecting the characteristics of crowd movement and The characteristics of traffic road conditions are of great significance to rationally plan the layout of urban facilities, improve traffic conditions, and plan people's itineraries. But at the same time, massive trajectory data brings huge challenges to data storage, transmission, query and data analysis: 1) The data scale expands,...

AI Technical Summary

Problems solved by technology

Under the limitation of condition 1), PRESS cannot effectively compress moving objects in free space (such as animals, flying objects, etc.) or trajectories in specific spaces (such as hand-drawn trajectory data); for condition 2), in some areas, especially In developing countries (such as some big cities in China), the road network structure will change frequently, and the PRESS algorithm has to recalculate all the shortest paths as the road network changes, which consumes a lot of time. At the same time, The PRESS algorithm needs to record all the shortest paths of each road network structure, requiring a large amount of storage space

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