Multi-user model moving track prediction method based on sequential pattern mining

A technology of sequential pattern mining and moving trajectory, applied in prediction, database model, data processing application, etc., can solve the problems of less personal historical data, low sampling rate of mobile phone signaling trajectory, and inability to predict well, and improve accuracy. sexual effect

Inactive Publication Date: 2017-08-04
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

The disadvantage of this method is that the prediction is based on the longitudinal historical data of a single user, and it cannot be predicted well in the case of less historical data.
[0009] Because people's mobile behavior has certain rules, that is, people tend to spend most of their time traveling to and from a few places with high frequency, so most domestic and foreign rese...

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  • Multi-user model moving track prediction method based on sequential pattern mining
  • Multi-user model moving track prediction method based on sequential pattern mining
  • Multi-user model moving track prediction method based on sequential pattern mining

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[0060] specific implementation plan

[0061] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0062] The relevant definitions involved in the multi-user model mobile trajectory prediction method based on sequential pattern mining in the present invention are as follows:

[0063] Definition 1, a trajectory or a space-time sequence is a data sequence composed of multiple triples:

[0064] Traj=0 ,y 0 ,t 0 >,....,n ,y n ,t n >

[0065] where (x i ,y i ) is R 2 A two-dimensional point in , whose values ​​represent latitude and longitude, respectively. t i is a timestamp, indicating that the corresponding (x i ,y i ) moment, and there is That is, the sequence obtained by sorting the anchor points in chronological order is called a trajectory.

[0066] Definition 2, stay point refers to a set of cluster-like anchor points that appear in the user’s trajectory beyond a given stay time. This...

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Abstract

The invention discloses a multi-user model moving track prediction method based on sequential pattern mining. Transverse track data positioned through mobile phone signaling is utilized to establish multiple user models to predict. According to the multi-user model moving track prediction method based on sequential pattern mining, a stay point mining algorithm with outlier tolerance is improved and provided, and stay points in the track are effectively mined; an inter-user similarity index based on a user stay area is provided, based on the index, transverse user track data is divided, and different user prediction models are established; based on a PrefixSapn algorithm, a moving track prediction method based on a sequential mining mode is provided, and the prediction model can be updated through newly generated real-time tracks.

Description

technical field [0001] The invention belongs to the technical field of data mining, and relates to a method for preprocessing mobile phone signaling traces based on clustering and outlier analysis. Background technique [0002] The user's current location is a key user context attribute, which can be associated with many applications to provide better location-based services (LBS Location Based Service). convenience. In the field of transportation, the real-time location information of vehicles has also become the basis for the implementation of congestion analysis and road flow analysis. Obviously, if the user's current location can be effectively obtained by some means, and its future location can be effectively predicted, its potential value is immeasurable. [0003] There have been a considerable amount of research on current trajectory prediction. From the way of data utilization, it is mainly divided into two categories, one is the vertical prediction method, and th...

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Application Information

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IPC IPC(8): G06F17/30G06Q10/04
CPCG06F16/2255G06F16/24564G06F16/2465G06F16/285G06Q10/04
Inventor 钱琨肖冰言陈庆春唐小虎
Owner SOUTHWEST JIAOTONG UNIV
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