Traffic Speed Prediction in Merging Zone of Urban Expressway Based on Bidirectional Long Short-Term Memory Network

Accurate prediction of microscopic traffic parameters in atypical complex scenes is a prerequisite to ensure stable operation of the intelligent vehicle infrastructure cooperative systems (IVICS). To solve the problem of vehicle speed distribution disorder and difficulty in prediction caused by bottleneck phenomenon during peak hours in the merging area under IVICS conditions, First, using the UAV video, the full-sample high-precision vehicle trajectory data of the intertwined area during peak hours are extracted from a wide-area view. Then, as bidirectional long short-term memory (Bi-LSTM) networks cost long time and affect the prediction performance of the model when training parameters are manually set, a BHO-Bi-LSTM (bayesian hyperparameter optimization bidirectional long short-term memory) integrated vehicle speed prediction model based on Bayesian hyperparameters optimization is proposed. Finally, the classical multiple linear regression model and Bi-LSTM model of vehicle speed prediction are constructed for comparison. The results show that the BHO-Bi-LSTM model outperforms other models, with a goodness-of-fit and rank correlation of 91.05% and 94.87%, respectively, and error mean, error standard deviation, mean square error, root mean square error, and normalized root mean square error of 0.0561, 0.4556, 0.2106, 0.4589, and 0.0785, respectively, which can overcome the disadvantage in prediction of complicated traffic speeds during peak hours. © 2024 Science Press. All rights reserved.