Method and system for extracting group behavior characteristics of mobile phone track data clustering

A technology of trajectory data and feature extraction, applied in electrical digital data processing, special data processing applications, computer components and other directions, can solve the problems of difficulty, limitation and lack of group behavior patterns, and achieve the effect of efficient processing

Inactive Publication Date: 2018-10-09
深圳市数字城市工程研究中心
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Problems solved by technology

[0003] However, the mobile phone trajectory data in the prior art is limited by spatial positioning methods such as GPS and WiFi. The spatial positioning accuracy generally ranges from tens of meters to several kilometers, and the time sa

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  • Method and system for extracting group behavior characteristics of mobile phone track data clustering
  • Method and system for extracting group behavior characteristics of mobile phone track data clustering

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[0051]In order to make the object, technical solution and advantages of the present invention more clear and definite, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0052] In the method provided by the present invention, according to the time periodicity and the positional periodicity of the mobile phone track, the mobile phone track data model is set as: ( id , x i , y i , t i ), ( id , x i+1 , y i+1 , t i+1 ), ( id , x i+2 , y i+2 , t i+2 ),…, ( id , x j , y j , t j ); the mobile phone track activity data model is: ( id , a, x i , y i , t i, t j ).

[0053] The first embodiment provided by the present invention is a group behavior feature extraction method for mobile phone track data clustering, which inclu...

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Abstract

The invention provides a method and system for extracting group behavior characteristics of mobile phone track data clustering. Time sequence tracks of individuals are obtained by acquiring mobile phone track data, the spatial repeatability and time periodicity of the time sequence track of each individual are counted, and the to-be-processed track data is generated; individual active points are extracted from the to-be-processed track data, candidate active point track data corresponding to each individual is obtained, simplification and completion processing are carried out on the candidateactive point track data, and active point track data of each individual is obtained; the similarity between the active point track data of the individuals is calculated; a plurality of active point tracks belonging to different track types are obtained according to the similarity, and the group behavior characteristics are extracted. According to the method and the system, high-efficiency processing of the mobile phone track data and intelligent extraction of a space-time multi-resolution behavior pattern are achieved, and large-scale human activity analysis is supported.

Description

technical field [0001] The present invention relates to the technical field of track data analysis and processing, in particular to a method and system for extracting group behavior characteristics of mobile phone track data clustering. Background technique [0002] Mobile phone trajectory data records the time and location of individuals, contains a large amount of individual activities and travel information, and shows considerable potential in the fields of online car-hailing scheduling, intelligent transportation, urban planning, and mobile location services. Mining patterns in large-scale mobile phone trajectory data can discover the rules of individual activities, describe the refined interaction process between residents and buildings, POIs, etc., and provide basic human activity data support for data-driven urban planning and urban intelligent operation. [0003] However, the mobile phone trajectory data in the prior art is limited by spatial positioning methods such...

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Application Information

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IPC IPC(8): G06F17/30G06K9/62H04W4/029
CPCH04W4/029G06F18/231
Inventor 涂伟贺彪王伟玺陈学业栾兆亮
Owner 深圳市数字城市工程研究中心
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