A Traffic Flow Prediction Method Based on Genetic Algorithm Optimizing LSTM Neural Network

Abstract

The invention discloses a traffic flow forecasting method based on genetic algorithm optimization LSTM neural network, comprising the following steps: step S1, collecting traffic flow data, and performing data normalization preprocessing, which is divided into training data set and test data set; Step S2, using the genetic algorithm to optimize each parameter of the LSTM neural network prediction model; Step S3, inputting the parameters optimized by the genetic algorithm and the training data set, and performing iterative optimization of the LSTM neural network prediction model; Step S4, using the trained LSTM The neural network model makes predictions on the test dataset and evaluates the model error. The present invention utilizes the characteristics of genetic algorithm and LSTM neural network to quickly optimize the parameter combination, can obtain higher prediction accuracy, and has good applicability to data samples at different intervals, the model reduces the amount of calculation, and shows better predictive performance.

Description

technical field [0001] The invention relates to technical fields such as deep learning methods and traffic flow prediction, and in particular to a traffic flow prediction method based on genetic algorithm optimization LSTM neural network. Background technique [0002] The prediction of short-term traffic flow is an important basis for traffic management departments to take traffic control and guidance measures. Through the prediction of short-term traffic flow, traffic management and control methods can be adjusted in advance to improve the efficiency of traffic operation. In order to better reflect the road traffic operation status, the short-term real-time prediction of traffic flow is the focus of research in the field of intelligent transportation. Traffic flow data is time series data. With the advancement of machine learning and deep learning, the prediction methods for traffic flow are also improving. [0003] Early forecasts of traffic flow were based on traditiona...

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Problems solved by technology

[0006] The content of the present invention is to solve the problem that in the traffic flow forecasting, the optimal parameter combination of the LSTM neural network cannot be found due to the large calculation overhead caused by large-scale parameter tuning, long training time, poor prediction performance, and long time-consuming. question

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