Traffic flow prediction method, prediction model generation method and device

Abstract

The invention discloses a traffic flow prediction method, a traffic flow prediction model generation method and a traffic flow prediction device. The prediction method includes the following steps that: historical traffic flow data of a road section to be predicted in a period before a current time point are obtained; a traffic flow prediction model corresponding to the road section to be tested is obtained from preset corresponding relations between roads and traffic flow prediction models; and the historical traffic flow data in the period before the current time point are inputted to the traffic flow prediction model corresponding to the road section to be tested, so that traffic flow data in a period after current time point can be obtained. Since traffic flow data have high nonlinearity and uncertainty, and a neural network model has a high nonlinear prediction ability, and therefore, the neural network model can be trained according to the historical traffic flow data of the road, and the traffic flow prediction model obtained through the training can accurately predict the traffic flow data in the period after the current time point according to the traffic flow data in the period before the current time point.

Description

Technical field [0001] The invention relates to the field of real-time traffic, in particular to a traffic flow prediction method, a prediction model generation method and a device. Background technique [0002] With the increasing popularity of intelligent transportation systems, the application of real-time traffic flow in intelligent transportation systems has become more extensive and in-depth. At present, the way to publish real-time traffic flow data is to publish road real-time traffic data (such as road speed, etc.) at regular intervals (such as 5s, 10s, 30s, 1 minute, or 2 minutes, etc.), which is released in the prior art The real-time traffic data is the real-time traffic situation of the road at the current moment, but the traffic flow data for a certain period in the future cannot be released. However, in real life, more and more users expect to know the traffic data of certain roads in advance in order to arrange their itineraries reasonably in advance. Therefore, ...

AI Technical Summary

Problems solved by technology

At present, the way of releasing real-time traffic flow data is to publish the real-time traffic data (such as the driving speed of the road, etc.) The real-time traffic data is the real-time traffic situation of the road at the current moment, but it cannot release the traffic flow data for a certain period of time in the future

However, in real life, more and more users expect to know the traffic data of certain roads in advance in order to arrange their trip reasonably in advance. Therefore, the existing methods of publishing real-time traffic flow data cannot meet the needs of users.

[0003] Due to the strong nonlinearity and uncertainty of traffic flow data, it is difficult for humans to speculate which factors will affect the change of traffic flow in the next period. Therefore, there is no public and effective technical solution to accurately predict traffic flow data.

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