Short-term traffic flow prediction method and system based on neural network, and storage medium

Abstract

The invention discloses a short-term traffic flow prediction method and system based on a neural network and a storage medium, and the method comprises the following steps: constructing model input data according to obtained original data, wherein the original data comprises traffic data and road data; training a first prediction model through the model input data; replacing a full connection layer in the trained first prediction model with a support vector regression model to obtain a second prediction model; training the second prediction model through the input data; and carrying out real-time traffic flow prediction through the trained second prediction model. According to the invention, real-time traffic flow prediction is carried out through the trained second prediction model, so that the prediction precision of the traffic flow is effectively improved. The method can be widely applied to the technical field of traffic flow prediction.

Description

technical field [0001] The invention relates to the technical field of traffic flow prediction, in particular to a neural network-based short-term traffic flow prediction method, system and storage medium. Background technique [0002] With the continuous improvement of the level of science and technology, the degree of intelligence of traffic continues to increase, and the collection of traffic data is more diverse. Massive traffic data is collected. How to mine the hidden information of traffic data and apply it to the future traffic flow? Accurate and efficient prediction, providing theoretical basis for traffic decision makers, and providing data reference for travelers has also become a new topic of intelligent transportation. [0003] The neural network model has been widely used in traffic flow forecasting and is a hot research direction of intelligent transportation. However, the existing single neural network model is difficult to mine the potential information of t...

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Problems solved by technology

[0003] The neural network model has been widely used in traffic flow forecasting, and is a hot research direction of intelligent transportation, but the existing single neural network model is difficult to mine the potential information of the data and prevent the information from being distributed in the network because of its relatively simple structure. loss in the flow, making it difficult to improve the prediction accuracy of traffic flow

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