A Traffic Flow Forecasting Method Based on Transfer-Aware Spatio-Temporal Graph Attention Network

Forecasting traffic flow is vital for optimizing resource allocation and improving urban traffic management efficiency. However, most existing traffic forecasting methods primarily emphasize road node connectivity when modelling spatial dependencies, often overlooking the impact of node transmission characteristics and inter-node distances on spatial feature propagation. This limitation restricts capturing temporal dynamics in spatial dependencies within traffic flow. To address this challenge, this study proposes a Transfer-aware Spatio-Temporal Graph Attention Network with Long-Short Term Memory and Transformer module (TAGAT-LSTM-trans). The model constructs a transfer probability matrix to represent each node’s ability to transmit traffic characteristics and introduces a distance decay matrix to replace the traditional adjacency matrix, thereby offering a more accurate representation of spatial dependencies between nodes. Our proposed model integrates a Graph Attention Network (GAT) to build a TA-GAT module for capturing spatial features, while a gating network dynamically aggregates information across adjacent time steps. Temporal dependencies are modelled using LSTM and a Transformer encoder, with fully connected layers ensuring precise forecasts. Experiments on real-world highway datasets show that TAGAT-LSTM-trans outperforms baseline models in spatio-temporal dependency modelling and traffic flow forecasting accuracy.