Travel time prediction method for optimizing LSTM neural network through particle swarm optimization algorithm

Abstract

The invention discloses a travel time prediction method for optimizing an LSTM neural network through a particle swarm optimization algorithm, and the method comprises the following steps: S1, collecting travel time data, performing data normalization, and dividing the data into a training set and a test set proportionally; S2, optimizing each parameter of an LSTM neural network prediction model by using the particle swarm optimization algorithm; S3, inputting the parameters, optimized through the particle swarm optimization algorithm, and the training set, and performing the iterative optimization of the LSTM neural network prediction model; S4, predicting the test set through the trained LSTM neural network model, and evaluating a model error. The method is quick in optimization. Compared with a random forest, SVM and KNN in the traditional prediction algorithm, the method of the invention has the least mean square error and square error for the data prediction, and the model reducesthe calculation burden, so the method shows better prediction performance.

Description

technical field [0001] The invention relates to technical fields such as deep learning methods and travel time prediction, and in particular to a travel time prediction method optimized by a particle swarm algorithm to optimize an LSTM neural network. Background technique [0002] The prediction of vehicle travel time is an important basis for traffic management departments to take traffic control and guidance measures. Through the prediction of the travel time, the traffic management and control means can be adjusted in advance to improve the efficiency of traffic operation, and at the same time play an important role in vehicle induction. Travel time data is time-series data. With the advancement of machine learning and deep learning, the prediction method of travel time is also improving. [0003] At the research level of statistical characteristics, there are trend extrapolation method, linear regression, hidden Markov prediction model and Kalman filter, etc. At the le...

AI Technical Summary

Problems solved by technology

[0006] The content of the present invention is to solve the problem that in the LSTM neural network travel time prediction, due to the large-scale parameter combination optimization, the calculation resource consumption is large, the prediction performance is poor, and the optimal parameter combination of the LSTM neural network cannot be found.

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