FST-GNN: A Fuzzy-Based Spatial-Temporal Graph Neural Network for Traffic Flow Prediction During Emergent Epidemics
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-67195-1_83
Reference9 articles.
1. Wang, F., et al.: On prediction of traffic flows in smart cities: a multitask deep learning based approach. World Wide Web 24(3), 805–823 (2021)
2. Liu, S., et al.: Multicomponent spatial-temporal graph attention convolution networks for traffic prediction with spatially sparse data. Comput. Intell. Neurosci. 2021, 9134942 (2021)
3. Zadeh, L.A.: Information and control. Fuzzy Sets 8(3), 338–353 (1965)
4. Liu, Y., et al.: A novel hybrid model combining a fuzzy inference system and a deep learning method for short-term traffic flow prediction. Knowl.-Based Syst. 255, 109760 (2022)
5. Wang, Y., et al.: A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic. Adv. Eng. Inform. 53, 101678 (2022)
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