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

Abstract

Forecasting traffic flow is essential for optimizing resource allocation and improving urban traffic management efficiency. Despite significant advances in deep learning-based approaches, existing models still face challenges in effectively capturing dynamic spatio-temporal dependencies due to the limited representation of node transmission capabilities and distance-sensitive interactions in road networks. This limitation restricts the ability to capture 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. The proposed model integrates a Graph Attention Network (GAT) to construct 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 accurate 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, validating the effectiveness of incorporating transmission awareness and distance decay mechanisms for dynamic traffic forecasting.