A novel hybrid neural network for high-accuracy vehicle-to-infrastructure network traffic prediction

To address the challenges in Vehicle-to-Infrastructure (V2I) network traffic prediction, this study proposes an innovative solution. We first establish a novel paradigm that integrates physical models to systematically convert publicly available vehicle trajectory data into V2I traffic data. On this basis, a gCNN–BiLSTM–MHA deep learning model is constructed, whose core advantage lies in its use of a lightweight GhostNet-based convolutional network (gCNN) to improve computational efficiency, while leveraging the synergistic effect of a bidirectional long short-term memory network (BiLSTM) and a multi-head attention mechanism (MHA) to effectively balance prediction efficiency and accuracy. The model’s superiority is comprehensively validated: compared to baseline models like LSTM, it demonstrates significant advantages across a series of key evaluation metrics — including running time, MBD, MAE, MAPE, RMSE, and R 2 — achieving an overall balanced performance. Furthermore, the model exhibits excellent performance on multiple benchmark datasets, confirming its strong robustness and high applicability for complex V2I network traffic prediction tasks.