Application effect of short-term traffic flow prediction method based on CNNBLSTM algorithm.

ABSTRACT: Reduced forecast efficiency and accuracy are the result of traditional traffic flow prediction algorithms' inability to adequately capture the spatiotemporal characteristics and dynamic changes of traffic flow. To address this problem, this study proposes a short-term traffic flow prediction method based on an improved convolutional neural network and a bidirectional long short-term memory algorithm. The method firstly identifies, repairs and decomposes the abnormal traffic flow data by smoothing the estimation threshold and adaptive noise integration empirical modal decomposition method to improve the data quality and stability. The suggested model is then supplemented with the enhanced Adam and Lookahead algorithms in an effort to increase the model's prediction accuracy and rate of conv

SUBMITTER: Sui G 

PROVIDER: S-EPMC12233303 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature