Abstract Deep Neural Network (DNN) models for both resource-rich environments and
resource-constrained devices have become abundant in recent years. As of now, the …

Cyber-physical systems (CPS) refer to systems that integrate communication, control, and
computational elements into physical processes to facilitate the control of physical systems …

Multivariate time series forecasting focuses on predicting future values based on historical
context. State-of-the-art sequence-to-sequence models rely on neural attention between …

High-performance traffic flow prediction model designing, a core technology of Intelligent
Transportation System, is a long-standing but still challenging task for industrial and …

In recent years, spatial–temporal graph modeling based on graph convolutional neural
networks (GCN) has become an effective method for mining spatial–temporal dependencies …

Abstract Machine learning based traffic forecasting models leverage sophisticated
spatiotemporal auto-correlations to provide accurate predictions of city-wide traffic states …

Forecasting floods encompasses significant complexity due to the nonlinear nature of
hydrological systems, which involve intricate interactions among precipitation, landscapes …

In recent years, researchers have made notable advancements in various disciplines using
large-scale foundation models. However, foundation models in the transportation system …

Real-time and high-precision traffic flow prediction plays a crucial role in transportation
management, contributing to control dispatch and reducing traffic congestion. Due to the …

We address the problem of traffic prediction on large-scale road networks. We propose a
novel deep learning model, Virtual Dynamic Graph Convolution Neural Network and …