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Smart city article details

Title	Spatial-Temporal Graph Transformer Network For Spatial-Temporal Forecasting
ID_Doc	52516
Authors	Dao M.-S.; Zetsu K.; Hoang D.-T.
Year	2024
Published	Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
DOI	http://dx.doi.org/10.1109/BigData62323.2024.10825469
Abstract	In the context of smart cities, there is a growing demand for risk-free navigation systems that help citizens avoid congestion and enjoy outdoor activities in clean environments. Such systems require the ability to predict traffic and environmental hotspots by analyzing spatial-temporal big data from various sources, including IoT devices, stations, CCTV, and personal device networks. However, analyzing and forecasting spatial-temporal data presents significant challenges due to the complex interplay of spatial and temporal dependencies. To address these challenges, we introduce a novel dual attention mechanism graph transformer that leverages both spatial and temporal information to capture intricate patterns in spatial-temporal data. We evaluate our model on two forecasting tasks: air pollution and traffic flow. Our results demonstrate superior performance compared to other graph neural network models. Consequently, this model has been integrated into a traffic-risk navigator application, which will be evaluated in Yokohama, Japan, using real data collected from air pollution stations and traffic monitors. © 2024 IEEE.
Author Keywords	Air Pollution Forecasting; Graph Big Data; Graph Transformer; Spatial-temporal Big Data; Traffic Flow Forecasting

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