Regional radiative traffic flow prediction method

Abstract

The invention discloses a regional radiative traffic flow prediction method, and the method is characterized in that the method comprises the following steps: S1, collecting the movement track data information of residents in daily life, and converting the track data into a traffic in-out flow graph taking a sub-region as a minimum unit; S2, acquiring traffic flow characteristics of different regions of a city and spatial correlation of flow among different regions of the city; S3, constructing a regional radiative traffic flow prediction model, and adopting a specific full convolutional network to realize gradual expansion prediction by superposing feature extraction-prediction sub-modules by taking a central region as a starting point; and S4, reversely adjusting the full convolutional neural network through the error between the predicted flow diagram and the actual flow diagram, and fitting a regional radiative traffic flow prediction model close to a real condition. According to the method, the problem that prediction is continuously expanded to the peripheral area based on the flow of the central area is solved, the prediction time period is shortened, and real-time prediction of the traffic flow is realized.

Description

technical field [0001] The invention relates to the field of traffic flow forecasting, in particular to a regional radial traffic flow forecasting method. Background technique [0002] With the development of the economy, the number of private cars continues to increase, and the method of strengthening road infrastructure construction and increasing road traffic capacity has been unable to meet the growing traffic demand. Traffic congestion, traffic safety hazards and lack of traffic information not only bring inconvenience to travelers, but also bring resistance to urban development. The real-time grasp and accurate prediction of regional traffic flow become an important basis for solving traffic problems by using intelligent transportation technology (ITS). [0003] Most of the methods for traffic forecasting are based on time series. This type of method requires that traffic must have a great relationship with time, and the time series must be largely seasonal. The time...

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Problems solved by technology

[0005] The object of the present invention is to provide a regional radial traffic flow forecasting method for the above-mentioned existing problems, which solves the problem of continuous expansion forecast based on the flow rate in the central area to the surrounding area, and shortens the time period of forecasting

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