Traffic flow prediction method based on genetic algorithm optimized LSTM neural network

Abstract

The invention discloses a traffic flow prediction method based on a genetic algorithm optimized LSTM neural network. The traffic flow prediction method based on the genetic algorithm optimized LSTM neural network comprises the steps of: S1, acquiring traffic flow data, performing data normalization pre-processing, and dividing the traffic flow data into a training data set and a test data set; S2,predicting various parameters of a model by adopting the genetic algorithm optimized LSTM neural network; S3, inputting genetic algorithm optimized parameters and the training data set, and performing iterative optimization of an LSTM neural network prediction model; and S4, predicting the test data set by using the trained LSTM neural network model, and evaluating the model error. According to the traffic flow prediction method based on the genetic algorithm optimized LSTM neural network in the invention, by utilization of the rapid optimization feature of the genetic algorithm and the LSTMneural network on parameter combination, the relatively high prediction precision can be obtained; furthermore, the method has good applicability on data samples in different intervals; the calculation amount is reduced through the model; and the prediction performance is better.

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...

AI Technical Summary

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

Images