Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction

• Published in: Data science and management Vol. 8; no. 3; pp. 257 - 269
• Main Authors: Ma, Changxi, Hu, Yanming, Xu, Xuecai
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2025
• Subjects: Bi-directional long short-term memory networks; Deep learning; Gated recurrent units; Traffic flow prediction; Variational mode decomposition; Deep learning; Traffic flow prediction; Variational mode decomposition; Bi-directional long short-term memory networks; Gated recurrent units
• ISSN: 2666-7649; 2666-7649
• DOI: 10.1016/j.dsm.2024.10.004

Accelerating urbanization and the rapid development of intelligent transportation systems have rendered short-term traffic flow prediction an important research field. Accurate prediction of traffic flow is beneficial for the optimization of traffic planning, improvement of road utilization, reduction of traffic congestion, and reduction in the incidence of traffic accidents. However, data pertaining to traffic flow are typically influenced by a multitude of factors, resulting in data that exhibit a considerable degree of nonlinearity and complexity. To address the issue of noise in raw traffic flow data, this study proposes a hybrid model that combines variational mode decomposition (VMD), a bidirectional long short-term memory network (BiLSTM), and a gated recurrent unit (GRU) for short-term traffic flow prediction. To validate the effectiveness of the model, an experimental validation was conducted based on traffic flow data from UK highways, and the performance of the model was compared with common benchmark models. The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error, coefficient of determination, and root-mean-square error compared to existing prediction techniques, thereby substantiating its efficacy in short-term traffic flow prediction.