Dynamic Spatio-Temporal Hypergraph Convolutional Network for Traffic Flow Forecasting
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| Status: | |
| Autorzy: | Ye Zhiwei, Wang Hairu, Przystupa Krzysztof, Majewski Jacek, Hots Nataliya, Su Jun |
| Dyscypliny: | |
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| Rok wydania: | 2024 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 22 |
| Wolumen/Tom: | 13 |
| Strony: | 1 - 17 |
| Impact Factor: | 2,6 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | This work was financed as part of the Lublin University of Technology projects FD-24/IM-5/087 and FD-24/EE-2/801. This research was supported by the National Natural Science Foundation of China (Grant Nos. 62376089, 62302153, 62302154), the Key Research and Development Program of Hubei Province, China (Grant No. 2023BEB024), the Young and Middle-aged Scientific and Technological Innovation Team Plan in Higher Education Institutions in Hubei Province, China (Grant No. T2023007), and the National Natural Science Foundation of China (Grant No. U23A20318). |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 12 listopada 2024 |
| Abstrakty: | angielski |
| Graph convolutional networks (GCN) are an important research method for intelligent transportation systems (ITS), but they also face the challenge of how to describe the complex spatio-temporal relationships between traffic objects (nodes) more effectively. Although most predictive models are designed based on graph convolutional structures and have achieved effective results, they have certain limitations in describing the high-order relationships between real data. The emergence of hypergraphs breaks this limitation. A dynamic spatio-temporal hypergraph convolutional network (DSTHGCN) model is proposed in this paper. It models the dynamic characteristics of traffic flow graph nodes and the hyperedge features of hypergraphs simultaneously, achieving collaborative convolution between graph convolution and hypergraph convolution (HGCN). On this basis, a hyperedge outlier removal mechanism (HOR) is introduced during the process of node information propagation to hyper-edges, effectively removing outliers and optimizing the hypergraph structure while reducing complexity. Through in-depth experimental analysis on real-world datasets, this method has better performance compared to other methods. |
