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

Bibliographic Data

ID22032299
AuthorsYan Zhou (0000-0002-3659-5881, University of Electronic Science and Technology of China), Xiaodi Wang (0000-0001-7039-2503, University of Electronic Science and Technology of China, corresponding author), Jipeng Jia (University of Electronic Science and Technology of China)
Year2025
Volume14
Issue12
Pages459
Publication date2025-11-23
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi14120459
OpenAlexW4416594583
LanguageEN
References cited34

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

Adjacency matrix · Attention network · Data modeling · Graph · Traffic generation model · Transformer · Traffic control and management · Traffic Prediction and Management Techniques · Transportation Planning and Optimization

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