Density clustering-based self-adaptive trajectory prediction method

Abstract

The invention discloses a density clustering-based self-adaptive trajectory prediction method which comprises a trajectory modeling stage and a trajectory updating stage, wherein in the trajectory modeling stage, rasterizing treatment is carried out on a newly generated movement report, so that moving points can be obtained and are divided into six moving point subsets; the six moving point subsets are clustered by adopting a limited area data sampling-based density clustering algorithm, so that a new trajectory cluster can be formed; the new trajectory cluster and an old trajectory cluster in the same period of time are merged with each other according to the similarity of the trajectory points, and the trajectory points of the merged trajectory cluster and the area of influence are updated; the trajectory points are combined according to the time sequence, so that a complete user movement trajectory can be obtained; in the trajectory updating stage, the user movement trajectory generated in the trajectory modeling stage is corrected. The density clustering-based self-adaptive trajectory prediction method is used for user movement trajectory prediction in the mobile communication scene; furthermore, when the new user movement trajectory is generated, the whole trajectory data is not needed to be modeled again.

Description

technical field [0001] The invention belongs to the technical field of data mining of computer science and technology, and relates to an adaptive trajectory prediction method based on density clustering. Background technique [0002] With the widespread popularity of mobile portable devices, the rapid development of wireless communication technology and global positioning technology, people have been able to obtain a large amount of real-time location data of users, such as the current geographic location and driving direction of the car can be obtained in real time by using the car GPS navigation system and other information; for users carrying mobile devices, the user's activity area can be roughly estimated by means of base station positioning. "Concatenating" the obtained location information at continuous time points forms a user's movement track over a period of time. A large amount of user location data and movement trajectories contain rich spatial structure informa...

AI Technical Summary

Problems solved by technology

[0004] Most of the above methods rely on complete, continuous, and static user trajectory data, while mobile communication data has the characteristics of huge data volume, discrete information distribution, and complex data format. The existing research methods for continuous trajectory data are difficult to directly apply to mobile In communication scenarios, and when new user movement trajectory data arrives, all trajectory data needs to be remodeled, resulting in low algorithm efficiency

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