A Novel Attention‐Weighted VMD‐LSSVM Model for High‐Accuracy Short‐Term Traffic Prediction

ABSTRACT To improve the management and operational efficiency of Intelligent Transportation Systems (ITS), address the nonlinear complexity of short‐term traffic flow, mitigate the issue of significant noise in traffic flow datasets, and tackle the challenges in determining parameters for Support Vector Machines (SVM) and Long Short‐Term Memory (LSTM) networks, this paper proposes a short‐term traffic flow prediction model based on Variational Mode Decomposition (VMD) and Least Squares Support Vector Machine (LSSVM) integrated with an attention mechanism. Multiple intrinsic mode functions (IMFs) decomposed by VMD are input into the LSSVM model, and the parameters and weights of the model are automatically adjusted using the attention mechanism. Experimental results on the Italian highway traffic flow dataset show that the prediction accuracy of the VMD‐LSSVM‐Attention model is improved by an average of about 38.6% compared with the traditional VMD‐SVM, VMD‐LSTM‐Attention, VMD‐LSSVM and LSSVM‐Attention models, and the model is more stable. Furthermore, in generalisation validation experiments on the Rotterdam and Madrid datasets, the model improved prediction accuracy by 5.17% to 20.97% compared to the best‐performing advanced models. This model provides a prediction method for the traffic flow prediction module in the intelligent transportation system (ITS) architecture.