Smart City Gnosys

Smart city article details

Title	Stm2Cn: A Multi-Graph Attention-Based Framework For Sensor Data Prediction In Smart Cities
ID_Doc	53042
Authors	Jin Z.; Xu J.; Huang R.; Shao W.; Xiao X.
Year	2022
Published	Proceedings of the International Joint Conference on Neural Networks, 2022-July
DOI	http://dx.doi.org/10.1109/IJCNN55064.2022.9892249
Abstract	Accurate long-term predictions help governments make decisions and residents travel, which is essential for the development of smart cities. Fortunately, due to the deployment of low-cost sensors, a large amount of time-series data such as parking availability data and air quality data has been stored, which makes it possible for long-term predictions. Many state-of-the-art studies based on multiple graphs have shown excellent performance in long-term prediction tasks. However, few previous studies employ multiple attention mechanisms to their models based on multi-graphs and thus fail to comprehensively capture the dynamic spatio-temporal correlations as well as the inner relationships among graphs. To this end, we propose a spatio-temporal multi-attention multi-graph convolutional network (STM2CN) framework for long-term prediction. We applied four different graphs to mine the potential contextual relationships and employed three attention mechanisms to capture the multiple graph and spatio-temporal correlations. Experiments on two large-scale real-world datasets demonstrate that the proposed STM2CN framework outperformed the state-of-the-art baselines. © 2022 IEEE.
Author Keywords	attention mechanisms; graph convolutional networks; long-term predictions; multiple graphs; sensor networks

Similar Articles

Id	Similarity	Authors	Title	Published
6155	0.914	Jin X.-B.; Ma H.; Xie J.-Y.; Kong J.; Deveci M.; Kadry S.	Ada-Stgmat: An Adaptive Spatio-Temporal Graph Multi-Attention Network For Intelligent Time Series Forecasting In Smart Cities	Expert Systems with Applications, 269 (2025)
52567	0.909	Jin G.; Liang Y.; Fang Y.; Shao Z.; Huang J.; Zhang J.; Zheng Y.	Spatio-Temporal Graph Neural Networks For Predictive Learning In Urban Computing: A Survey	IEEE Transactions on Knowledge and Data Engineering, 36, 10 (2024)
52516	0.904	Dao M.-S.; Zetsu K.; Hoang D.-T.	Spatial-Temporal Graph Transformer Network For Spatial-Temporal Forecasting	Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 (2024)
23809	0.9	Ouyang X.; Li Y.; Guo D.; Huang W.; Yang X.; Yang Y.; Zhang J.; Li T.	Enhancing Few-Sample Spatio-Temporal Prediction Via Relational Fusion-Based Hypergraph Neural Network	Information Fusion, 121 (2025)
52517	0.9	Huang X.; Pan Z.; Zhao G.	Spatial-Temporal Interactive Graph Convolutional Networks For Traffic Forecasting	2024 4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024 (2024)
52507	0.899	Liu S.; Zhu J.; Lei W.; Zhang P.	Spatial-Temporal Attention Graph Wavenet For Traffic Forecasting	2023 5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023 (2023)
20803	0.893	Wang S.; Chen Y.; Xu H.; Hu J.; Zeng P.; Hu Z.	Dngnn: Efficient Deep Noisy Graph Neural Network For Spatio-Temporal Series Forecasting	2025 2nd International Conference on Algorithms, Software Engineering and Network Security, ASENS 2025 (2025)
19926	0.893	Yin X.; Wu L.; Zhang Y.; Han Y.; Zhai K.	Diffusion Convolution Graph Attention Network For Spatial-Temporal Prediction	Lecture Notes in Electrical Engineering, 996 LNEE (2023)
20800	0.893	Diao Z.; Wang X.; Zhang D.; Xie G.; Chen J.; Pei C.; Meng X.; Xie K.; Zhang G.	Dmstg: Dynamic Multiview Spatio-Temporal Networks For Traffic Forecasting	IEEE Transactions on Mobile Computing, 23, 6 (2024)
46173	0.891	Ma Q.; Zhang Z.; Zhao X.; Li H.; Zhao H.; Wang Y.; Liu Z.; Wang W.	Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting	International Conference on Information and Knowledge Management, Proceedings (2023)