Dynamic Urban Knowledge Graph-Informed Inner-City Traffic Flow Forecasting via Spatiotemporal Graph Neural Network

Traffic flow forecasting plays a vital role in improving transportation efficiency, safety, and sustainability in cities. Traditional methods focus on capturing the intricate spatiotemporal dynamics within traffic flows. With the development of web and sensing technology, urban knowledge graphs (KG) emerge as an auxiliary knowledge that represents dependencies among traffic entities. Prior works have highlighted the importance of incorporating knowledge from urban KGs to enhance the accuracy of traffic flow forecasting. Unfortunately, these methods inadequately capture the dynamics within the evolving urban KGs, resulting in sub-optimal traffic forecasts.To bridge this gap, we first perform an analytical study to show the impact of short-term and long-term dynamics of urban KGs on the traffic flow forecasting task. Based on our preliminary observation, we propose Dynamic Knowledge Graph-Informed Traffic Flow Forecasting (DKG-TF2), which is a graph neural network-based spatiotemporal forecasting model, enabling to capture of both static and dynamic characteristics from urban KGs, and integrates it with traffic flow forecasting. Evaluated over a real-world dataset, we demonstrate that the DKG-TF2 model significantly outperforms competitive baselines and achieve a root mean squared error of 3.4923. Further ablation studies reveal the complementary roles of static attributes and dynamic attributes in enhancing forecasting accuracy and model robustness.​