Position prediction method by fusion of movement law of individual and neighbors

Abstract

The invention discloses a position prediction method by fusion of a movement law of an individual and neighbors. The position prediction method comprises the steps of extracting time position information of an important (key) location in moving data of a user firstly to obtain a user track data; then performing mapping to find out K users which are located around the user and have similar time and space distribution; deeply finding a position movement mode which is in the shape of <A-B-C, T> from the historical user track data; and searching K neighbors having the same position movement mode, and taking the similarity of the predicted user and the adjacent neighbors as the weight, and performing fusion on the predicted user (individual). The similarity of the K neighbor users and the influence on the predicted user by taking the similarity as the weight are taken into consideration; meanwhile, the associated movement law, namely the position movement mode, is searched to perform the position prediction; and therefore, compared with the conventional position prediction method, the position prediction method with such technological thought is more robust, higher in noise resistance, higher in accuracy, and can better satisfy the sociology law.

Description

Technical field [0001] The invention belongs to the technical field of position prediction based on user trajectory data, and more specifically, relates to a position prediction method that integrates the movement laws of individuals and neighbors. Background technique [0002] With the development of satellites, wireless networks, and positioning equipment, a large number of user trajectory data are showing a rapid growth trend, such as vehicle trajectory data and personnel movement trajectory data. Discovering hidden knowledge through user trajectory data mining, studying human behavior patterns and making predictions, can help governments and users make better decisions, and can even solve major social issues such as urban traffic, urban environment, and emergencies. Effective means. For example, in terms of traffic, by analyzing the trajectory data of vehicles, you can know the congestion situation of the road, so that the congestion situation and the best navigation can be ...

AI Technical Summary

Problems solved by technology

Based on the personal historical user trajectory data, the focus is on the periodic pattern or geographical distribution pattern in the personal historical user trajectory data, such as mining personal movement rules, etc., but this type of method not only ignores the fact that the user is an individual with social attributes. important features, and there are deficiencies in the discovery of user personal mobility patterns, tight coupling of time and space, etc.

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