A Prediction Method for Highway Traffic Flow Based on the IHPO-VMD-LSTM-Informer Model

Abstract

Accurate and timely predictions of highway traffic flow are crucial for implementing intelligent highway management. This paper introduces a novel prediction approach for highway traffic flow by employing the IHPO-VMD-LSTM-Informer model, aiming at enhancing prediction accuracy. Initially, key indicators measuring highway traffic are identified, and Nonlinear Principal Component Analysis (NPCA) is applied to minimize the dimensionality and interdependence among these indicators. This reduction process replaces the original complex indicators with fewer numbers of principal components, thereby simplifying the feature matrix's structure. Subsequently, Variational Modal Decomposition (VMD) processes historical highway traffic flow data, enhanced by the strategically improved Hunter-Prey Optimization (HPO) algorithm. This optimization facilitates adaptive parameter adjustments for the VMD, enabling effective decomposition of highway traffic flow time series data. The Sample Entropy (SE) of Intrinsic Modal Functions (IMFs) from this decomposition is used with the substantial indicators to form a comprehensive feature matrix. Then, the predictive module combines a Long Short-Term Memory (LSTM) network with the Informer architecture to accurately predict highway traffic flow from the feature matrix. The effectiveness of the proposed model is verified using a public motorway traffic dataset KDD CUP 2017. The results indicate that the proposed model outperforms available ones in terms of prediction accuracy, where MAPE and RMSE have 8.09 and 2,84, thus significantly advancing intelligent highway management.