Method for predicting time sequence of number of people served by base stations based on space-time transfer probabilities of mobile phones

Abstract

The invention discloses a method for predicting a time sequence of number of people served by base stations based on space-time transfer probabilities of mobile phones. The method comprises the following steps: calculating the total number of people within base station service areas of the mobile phones within an equal time period by using space-time orbit data of the mobile phones; dividing people moving orbits by using the space-time orbit data of the mobile phones, and calculating the number of people coming back and forth between the base stations within adjacent time periods in a research area; based on related theory of Bayesian and Markov chains, calculating the transfer probabilities of mobile phone users within target base stations to occur in the base stations at a current moment according to historical data; calculating the transfer probabilities of the mobile phone users within the target base stations to occur in the base stations within different time periods to construct a complete space-time transfer probability matrix in the research area; and predicting the number of people served within the base station ranges of the mobile phones in the research area with the relatively stable total number of people by using the complete space-time transfer probability matrix. The method disclosed by the invention has the advantages of low data acquisition cost, simple model structure and high prediction efficiency.

Description

technical field [0001] The invention belongs to the technical field of population prediction, and relates to a method for predicting the number of dynamic population in a region, in particular to a method for predicting the number of people served by a base station based on the time-space transition probability of mobile phone locations. [0002] technical background [0003] Comparing the two traditional methods of population forecasting in large-scale open areas are based on the evolution of time series correlation theory. The first method is the moving average method, and the other is the ARIMA method. The former is based on the time series, moving item by item, and calculating the sequential time average containing a certain number of items in turn, so as to make predictions, but the moving average method does not consider the influence of spatial factors on the movement behavior of the crowd, and emergencies will also affect the population. The prediction results have a ...

AI Technical Summary

Problems solved by technology

The former is based on the time series, item by item, and sequentially calculates the time series average number containing a certain number of items, so as to make predictions. However, the moving average method does not consider the influence of spatial factors on the movement behavior of the crowd. In addition, emergencies will also affect The prediction results have a relatively large impact; the second method is based on the ARIMA model to predict the population, but one of the premise assumptions in this method is that the total population within the research area remains stable, while in fact the population mobility within the city Larger, it is impossible to guarantee that the number of people in the study area is in a relatively stable state, so this method is also difficult to accurately predict the number of people in different time periods in the area

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