Individual activity steady state training method under large data environment

Abstract

The invention aims at using a space activity range data set of a mobile terminal individual time sequence to excavate steady state and unsteady state behaviors of the individual and carrying out space-time range statistics on the behaviors so as to really and objectively reflect population residence distribution and flow conditions in a city. In order to reach the above purpose, a technical scheme of the invention provides an individual activity steady state training method under a large data environment. The method has the advantages that large data resources of an existing mobile terminal are fully used; through using a lot of existing anonymous encryption mobile terminal individual positioning data of an operator, distribution of steady state behaviors of the city population can be automatically acquired in low-cost and high-frequency mode and high timeliness is possessed.

Description

technical field [0001] The present invention relates to an individual activity steady-state training method based on massive anonymous encrypted mobile terminal individuals (such as mobile phones, tablets, Internet access cards and other independent mobile terminals, hereinafter referred to as individuals) time-series positioning data in a big data environment. Clustering the time and space dimensions, looking for the behavior of individuals staying in a small area for a long time (that is, steady state), and statistical training on the spatial distribution, time distribution, and duration distribution of such behaviors, can be used to obtain the time and space of urban population Distribution and flow, providing services for urban operation management, traffic policy evaluation, traffic planning, etc., belonging to the technical field of urban planning and urban operation management. Background technique [0002] A city is a gathering place of population and resources. Urb...

AI Technical Summary

Problems solved by technology

However, due to electronic technology itself, each spatial coordinate point will have a corresponding systematic deviation, such as GPS, Galileo, and Beidou system positioning deviations are in the range of a few meters to more than ten meters, Wi-Fi positioning deviations are in the range of 10-100 meters, and mobile phone base stations The positioning deviation is 50-200 meters, and it may reach 1000 meters in remote mountainous areas

At the same time, due to the service space scope and operation management pressure of each operator, it is not possible to collect data of all users in all spaces and at all times. Therefore, the data sets will have varying degrees of lack of time range or space range.

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