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- W4385883233 abstract "Traffic speed prediction problem is the key issue to recent intelligent transportation system, especially in urban environment. Traffic speed prediction is a challenging task duo to the complex spatial-temporal correlations between different urban road sections are uncertain and dynamic. Existing models usually use different components to capture temporal correlation and spatial correlation, respectively, ignoring the heterogeneity of spatio-temporal data in spatio-temporal dimension, while more complicated model also leads to higher computing complexity. A novel multi-view fusion spatio-temporal graph convolution network, called as MFGCN, is proposed to model various kinds of semantic correlations more comprehensively in the urban road network. In this model, a multi-graph model is constructed to represent various correlations, such as spatial correlation, temporal correlation, and dynamically generated synchronous correlation, and so on. Furthermore, the synchronous correlation is dynamically modelled as spatio-temporal heterogeneous correlation by utilizing the fuzzy C-means clustering approach, in which the number of clustering centers is adjusted according to traffic datasets size. Moreover, fuzzy correlation can also tackle the uncertain relationship between dynamically traffic road network, and all above correlations are fused with fuzzy correlation. Finally, some experiments are carried out on well-known real traffic datasets, in order to show the effectiveness and merit of the proposed model with good prediction performance." @default.
- W4385883233 created "2023-08-17" @default.
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- W4385883233 date "2023-01-01" @default.
- W4385883233 modified "2023-10-14" @default.
- W4385883233 title "A Novel Fuzzy-Clustering-Based Deep Learning Approach for Spatio-Temporal Traffic Speed Prediction" @default.
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- W4385883233 doi "https://doi.org/10.1007/978-3-031-39774-5_63" @default.
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