A traffic flow forecasting method, forecasting model generation method and device

Abstract

Disclosed are a traffic flow prediction method, and a prediction model generation method and device.The prediction method comprises: for a road to be predicted, acquiring historical traffic flow data of the road to be predicted within a time period prior to the current time (301); from a correlation between a pre-existing road and a traffic flow prediction model, acquiring the traffic flow prediction model corresponding to the road to be predicted (302); and inputting the historical traffic flow data within the time period prior to the current time into the traffic flow prediction model corresponding to the road to be predicted, to obtain traffic flow data within a time period after the current time (303). Since traffic flow data has high nonlinearity and uncertainty and a neural network model has a relatively high nonlinear prediction capability, the neural network model is trained according to historical traffic flow data of a road, so that a traffic flow prediction model obtained by training can relatively accurately predict the traffic flow data of the road within the time period after the current time according to the traffic flow data of the road within the time period prior to the current time.

Description

technical field [0001] The invention relates to the field of real-time traffic, in particular to a traffic flow forecasting method, a forecasting 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 is becoming more and more extensive and in-depth. 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 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 ne...

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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