Deep Learning with Improved Metaheuristic Optimization for Traffic Flow Prediction

Abstract

Aiming at the current dilemma of inaccurate prediction accuracy in the field of traffic flow prediction, this paper proposes a novel traffic flow prediction method using the Revised Enhanced Extreme Gray Wolf Optimizer (REEGWO) to optimize convolutional neural networks (CNNs) and combining with bi-directional long and short-term memory (BiLSTM) networks. The experimental results show that the model can effectively converge the training loss error and RMSE, and significantly outperforms the existing classical methods in terms of goodness-of-fit, average absolute error, average deviation error and average absolute percentage error, providing an efficient and accurate solution for traffic flow prediction.