Spatial-temporal Graph Transformer Network for Spatial-temporal Forecasting

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.