Mobile phone positioning data based resident activity pattern mining method and device

Abstract

The invention relates to the technical field of resident activity pattern mining, and particularly relates to a mobile phone positioning data based resident activity pattern mining method and device. The mobile phone positioning data based resident activity pattern mining method comprises the steps of a, setting a distance threshold and a time threshold, and performing activity stay area recognition on mobile phone positioning data; b, extracting the resident activity transition probability, the activity probability in different time periods and the activity stay area point of interest distribution proportion, and building a Markov chain based weight probability activity purpose inference model; and c, judging the activity purpose of the activity stay area in which a use is located through the activity purpose inference model, and extracting the resident activity pattern according to the activity purpose. The method provided by the invention recognizes the activity stay area more efficiently and more reliably through regularly sampling the mobile phone positioning data in a large scale, overcomes conditions such as great time interval and positioning error in a data source, has better pertinence and practicability, and is simpler and more efficient.

Description

technical field [0001] The invention relates to the technical field of mining activity patterns of residents, in particular to a method and device for mining activity patterns of residents based on mobile phone positioning data. Background technique [0002] The sustainable development of the city needs to master the pulse of the city and complete the urban planning by understanding the behavior patterns of urban residents. However, the current extraction of resident activity patterns still relies on regular sampling surveys, which have a small sample size and long survey intervals, which often cannot reflect the ever-changing characteristics of residents in first-tier cities in China in a timely, accurate and effective manner. [0003] With the rise of the big data boom and the increasing development of positioning technology in recent years, more and more studies have used data sources such as mobile phone positioning data or satellite positioning (GPS) data to mine reside...

AI Technical Summary

Problems solved by technology

[0005] To sum up, the disadvantages of the existing mining methods of residents’ activities are: most of the existing technologies are based on the similarity of residents’ travel patterns for association mining or mining by constructing pattern tree classification, and the technical implementation scheme is relatively simple but requires more data. High, on the one hand, it requires that some location data need to carry prior knowledge such as the purpose of the activity, on the other hand, it requires the continuity and short interval of location data records; High-accuracy satellite positioning (GPS) data for event purposes

However, as far as urban planning is concerned, the use of a small amount of satellite positioning data is not macroscopically representative, and most of the big data that can be obtained and used in real life is base station positioning (GSM) data, and there is no prior activity purpose , if the research based on this type of data adopts the original research method, it will not only fail to achieve a high accuracy rate, but also cannot obtain a good algorithm efficiency due to the exponential growth of the pattern

The machine learning method based on conditional probability can obtain acceptable mining accuracy, but on the one hand, due to the lengthy learning process, on the other hand, the selection of parameters such as learning rate and network initial weight will subjectively affect the learning effect, making it difficult Responding to the Space Requirements of Ubiquitous Computing under the Background of Big Data

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