Traffic flow prediction parallel method based on chaos and reinforcement learning

Abstract

The invention provides a traffic flow prediction parallel method based on chaos and reinforcement learning. Comprising the following steps: 1, carrying out preprocessing and chaotic analysis on data, generating a comparison chaotic time sequence through a chaotic model, and carrying out reconstruction, standardization and data set division; 2, performing reinforcement learning training and comparison environment construction by using the preprocessed data; 3, constructing an act-critic neural network model to learn an intelligent agent strategy and judge a behavior value; and 4, generating a plurality of training models by the slave process under the parallel framework to interact with the environment, realizing parallel updating through dispersion comparison and reward with the central neural network model of the host process, and finally performing prediction verification by the host process. According to the method, the traffic flow is predicted by adopting reinforcement learning and a chaotic time sequence, and compared with a traditional statistical prediction method, the method has higher interpretability and online adjustment learning ability; parallel reinforcement learning can learn and adjust more quickly, and an optimal prediction result is generated.

Description

technical field [0001] The invention belongs to the fields of chaos theory, reinforcement learning and parallel computing, and in particular relates to a parallel method for predicting traffic flow based on chaos and reinforcement learning. Background technique [0002] With the development of the economic level, the ownership of private cars in China has increased sharply, and the road traffic situation has become more complex, so the prediction of traffic flow has become very important. Through the traffic flow prediction, the road conditions can be planned in advance and the traffic conditions can be guaranteed to the greatest extent. With the development of big data environment and smart city system, the collection of traffic flow prediction data and the dynamic adjustment of prediction methods are becoming more and more important. increasingly real-time. At present, one kind of traffic flow prediction is to use traditional statistical methods to predict the probability...

AI Technical Summary

Problems solved by technology

However, since most of the parallel computing frameworks are aimed at the problems in the background of big data, the learning of reinforcement learning models and frequent parameter updates present problems such as complex structures and insufficient parallelization.

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