On-line position prediction method based on mass of space trajectory excavation

Abstract

The invention relates to an on-line position prediction method based on mass of space trajectory excavation and belongs to the field of space trajectory excavation. The method comprises the steps of firstly, excavating frequent sequential patterns from the mass of space trajectory excavation, secondly, using the sequence patterns to establish a model based on a prefix tree structure, thirdly, establishing a model based on distance and popularity to solve the zero frequency problem, and finally, using the established predication models to predict the next position of a moving target according to the current trajectory information of the moving target. The accuracy of the prediction method is improved greatly compared with the existing methods, the calculation complexity is low, real-time prediction can be carried out on the position of the moving target, only the position information of the moving target is needed, and therefore the on-line position prediction method based on mass of space trajectory excavation can be widely applied to the fields such as intelligent traffic and services based on geographic positions.

Description

technical field [0001] The invention relates to a position prediction method of a moving target, in particular to an online position prediction method based on massive space trajectory mining. Background technique [0002] In recent years, with the development of the mobile Internet and the popularization of mobile smart terminals, positioning technology has been widely used. For example, as the infrastructure of intelligent transportation systems, taxis and buses in many cities are equipped with GPS devices; in mobile Internet applications such as Weibo, WeChat, and Dianping, the user's current location is often recorded as the most basic information and share. These massive spatial trajectory information generated by moving objects such as vehicles and users make it possible to analyze the behavior patterns of moving objects. Among them, predicting the position of moving objects is an important research task, which has important theoretical significance and practical app...

AI Technical Summary

Problems solved by technology

[0005] The model-driven method is more interpretable because it models the internal factors of the user's movement, but this method requires more historical trajectory information of the current user to model, and the computational complexity is high , so there are certain limitations

The data-driven method does not make assumptions about the internal factors of user movement, so the interpretability is weak, and the accuracy of the prediction is affected by the size of the training data

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