This paper proposes a coordinated topology reconfiguration method for power distribution
networks (PDNs) and urban drainage networks (UDNs) to facilitate the load restoration and …

Traffic crashes present substantial challenges to human safety and socio-economic
development in urban areas. Developing a reliable and responsible traffic crash prediction …

Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets
predominantly feature zero values, indicating no incidents, with sporadic high-risk values for …

Quantifying uncertainty is crucial for robust and reliable predictions. However, existing
spatiotemporal deep learning mostly focuses on deterministic prediction, overlooking the …

Recent research mainly applies deep learning (DL) methods to short-term traffic forecasting.
However, there is a growing interest in long-term forecasting, which allows action …

In this study, we develop a robust real‐time forecast system for the SYM‐H index using Deep
Neural Networks and real‐time Solar Wind measurements along with Interplanetary …

Traffic flow forecasting is crucial for intelligent transportation systems. Currently, most
models need to pay more attention to the delay (history) state to improve forecasting …

Accurate and reliable prediction has profound implications to a wide range of applications.
In this study, we focus on an instance of spatio-temporal learning problem--traffic prediction …