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What is Hybrid GCN-BiLSTM for Traffic Forecasting about?

This project report presents a novel hybrid deep learning framework combining Graph Convolutional Networks (GCN) and Bidirectional LSTM (BiLSTM) for accurate short-term traffic forecasting in Vehicular Ad Hoc Networks (VANETs). The proposed GCN-BiLSTM-Diffusion model effectively captures spatial correlations and temporal patterns while mitigating noise, demonstrating superior performance on the PEMS-BAY dataset compared to baseline models. The architecture is designed for real-time deployment, making it sui

Author

m24cps008

Language

EN