Improved mobile phone signaling data trip identification method based on dynamic space threshold

Abstract

The invention provides an improved mobile phone signaling data trip identification method based on a dynamic space threshold, which is used for identifying travel conditions of each user according tomobile phone signaling data acquired by each mobile phone base station so as to obtain an individual travel chain of the user, and is characterized by comprising the following steps: S1, acquiring themobile phone signaling data; s2, cleaning the mobile phone signaling data; s3, judging a dynamic threshold D corresponding to each mobile phone base station according to the position of the base station through a preset dynamic threshold calculation method; s4, performing classifying to obtain a to-be-analyzed data set of the user; s5, sequentially selecting a group of to-be-analyzed data sets ofthe user; s6, reading to-be-analyzed data in the current user data set in sequence, judging the stay point of the current user according to the corresponding dynamic threshold value D, S7, generatingand storing an individual travel chain, and S8, repeating the step S5 to the step S7 until all the to-be-analyzed data of the user are processed.

Description

technical field [0001] The invention belongs to the field of urban planning, and relates to a mobile phone signaling data-based travel identification method, in particular to a mobile phone signaling data travel identification method based on an improved dynamic space threshold. Background technique [0002] In recent years, mobile phone signaling data has been widely used in various fields of urban research and urban planning, ranging from urban space and spatial structure, urban traffic planning, urban population and employment distribution, urban actual population, to individual behavior Pattern mining. When analyzing mobile phone signaling data, it is usually necessary to obtain the individual travel chains of each mobile phone user based on the mobile phone signaling data, so as to better analyze the behavior patterns of users in the city based on the individual travel chains. [0003] In the prior art, the time-space clustering method is generally used to identify ind...

AI Technical Summary

Problems solved by technology

Although the above two methods can avoid the problem of uneven recognition accuracy to a certain extent, there will still be problems of "over-recognition" and "under-recognition" within the partition

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