Personnel flow prediction method and device based on signaling data, equipment and medium

Abstract

The invention discloses a personnel flow prediction method and device based on signaling data, equipment and a medium. The method comprises the following steps: acquiring long-time-sequence signaling data and geographic information data in the same historical period; dividing a plurality of time periods according to the long time sequence signaling data and the geographic information data, establishing traffic prediction models of different time periods, and solving the traffic prediction models to obtain an initial traffic prediction result without special event influence; when the influence of the special event exists, calculating an initial attenuation coefficient and a destination attenuation coefficient, and correcting the initial traffic prediction result according to the coefficients to obtain a first traffic prediction result under the influence of the special event; and dynamically adjusting the initial attenuation coefficient and the destination attenuation coefficient along with data updating, and adjusting the first traffic prediction result according to the coefficients to obtain a second traffic prediction result under the influence of the special event. According to the method, mobile phone signaling data with a large sample size and a wide coverage range can be utilized to predict the number and spatial-temporal characteristics of foreign worker flow during major festivals and holidays.

Description

technical field [0001] The present invention relates to the field of big data technology, in particular to a method, device, equipment and medium for predicting personnel flow based on signaling data. Background technique [0002] The large-scale flow of migrant workers is of great significance to the prevention and control of infectious diseases, transportation management, labor security, housing and public service allocation, economic consumption vitality, social security governance, urbanization pattern, resource consumption, and environmental protection. , is the focus of policy formulation on population flow and migration, regional and urban governance, and spatial planning around the world. Therefore, how to use big data to simulate and predict the flow of migrant workers with high precision is a prerequisite for formulating relevant policies scientifically and rationally. [0003] In the existing technology, there is a personnel flow analysis method based on geograph...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, there is a personnel flow analysis method based on geographic location service technology. First, this technology only uses big data to analyze the current situation of personnel flow. The application of big data mainly stays at the display level and fails to extract the information of personnel flow. The general law of the spatio-temporal change process does not realize the function of predicting the future flow of people; second, geographic location services require end users to actively initiate service requests before they can obtain relevant spatio-temporal data. There are a large number of platforms that provide this service, so the degree of data coverage It needs to be improved, and a large number of spatial flow behaviors have not been effectively recorded; third, the data obtained by this technology does not have user ID information, so it cannot identify the user's complete travel, but only counts the flow of people between cities

[0004] There is also a major holiday OD traffic forecast method based on linear and nonlinear regression. Although this method is simple, effective and reasonable, the determination of explanatory variables in the multivariate model depends on empirical judgment and subjective selection, and the selection of explanatory variables will be difficult. have some influence on the predictive effect

In addition, the flow data of expressway exits used in this method can only reflect the OD flow of a single traffic mode on the expressway during major holidays, but cannot reflect the OD volume of personnel flow during holidays that integrate multiple modes of traffic.

Moreover, this prediction method is mostly applicable to a small prediction area. For a larger range, such as the traffic OD volume prediction of the whole country, the calculation process of this method will be more complicated.

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