Short-time traffic flow prediction method based on long-time and short-time memory recurrent neural network

Abstract

The invention discloses a short-time traffic flow prediction method based on a long-time and short-time memory recurrent neural network. The method comprises the following steps: aggregating inputted historical traffic flow data according to a prediction time interval of a short-time traffic flow; carrying out pretreatment on the aggregated historical traffic flow; setting a reasonable parameter for a long-time and short-time memory recurrent neural network; training a neural network prediction model by using data after pretreatment; and invoking a traffic flow, predicted by the prediction model, of a designated time interval and evaluating a prediction error. According to the invention, because the long-time and short-time memory recurrent neural network has the advantage of being capable of memorizing inputted historical data for long time, the high prediction precision is realized; and the expansibility for different prediction intervals is good.

Description

technical field [0001] The invention belongs to the field of intelligent traffic systems, and in particular relates to a short-term traffic flow prediction method based on a long-short-term memory recursive neural network. Background technique [0002] As one of the core contents of Intelligent Transportation System (Intelligent Transportation System, ITS), intelligent traffic control and guidance system has always been a hot topic in the research of intelligent transportation system. The realization of intelligent control and guidance can effectively alleviate traffic congestion, reduce environmental pollution, and provide efficient and safe road traffic conditions. The premise and key of these realizations is to be able to accurately predict short-term traffic flow, and the accuracy of prediction directly determines the effectiveness of control and induction. [0003] According to the literature search of the prior art, it is found that the existing prediction models can ...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the existing method cannot memorize the historical traffic flow data, and thus cannot dynamically determine the optimal historical length, resulting in unsatisfactory prediction accuracy, the purpose of the present invention is to dynamically determine the optimal history length for better forecasting

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