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Title	Mmgcrn: Multimodal And Multiview Graph Convolutional Recurrent Network For Traffic Prediction
ID_Doc	37213
Authors	Meng X.; Xie W.; Cui J.
Year	2024
Published	Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
DOI	http://dx.doi.org/10.18293/SEKE2024-127
Abstract	Traffic prediction is essential for intelligent transportation systems and smart city applications, yet existing spatio-temporal models face limitations. These include inadequate spatial feature extraction, neglect of spatial heterogeneity, and omission of factors like traffic speed and travel time. To address the above challenges, we propose a multimodal and multiview traffic flow prediction method called MMGCRN, which adaptively learns the spatio-temporal correlation features of multimodal traffic data. In MMGCRN, we design a spatial heterogeneous perception cross-attention module to model the spatial heterogeneous relationships of nodes. In addition, we use spatio-temporal embeddings to generate the dynamic feature correlation graph of nodes across time steps, which is combined with the topological graph and semantic graph and fed into the Multiview Graph Convolutional Recurrent Network (MGCRN) to extract the multi-dimensional correlations between nodes. We learn and fuse different modal traffic data through multiple MGCRN modules. Finally, experiments on two real-world datasets show that in 60-minute-ahead long-term forecasting, the MMGCRN model achieves a minimum improvement of 0.96% and a maximum improvement of 8.69% over baseline models. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.
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