Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

In recent decades, the development of transport infrastructure has had a great development,
although traffic problems continue to spread due to increase due to the increase in the …

Spatiotemporal wind power prediction technology could provide technical support for wind
farm energy regulation and dynamic planning. In the paper, a novel ensemble deep graph …

Due to great advances in wireless communication, the connected Internet of vehicles
(CIoVs) has become prevalent. Naturally, internal connections among active vehicles are an …

Traffic volume propagating from upstream road link to downstream road link is the key
parameter for designing intersection signal timing scheme. Recent works successfully used …

Abstract Artificial Neural Networks (ANNs) are a type of machine learning algorithm inspired
by the structure and function of the human brain. In the context of supply chain management …

Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable
intelligent process control, analysis, and management, such as complex equipment …

With the recent increase in urban drift, which has led to an unprecedented surge in urban
population, the smart city (SC) transportation industry faces a myriad of challenges …

Compared with mechanism-based modeling methods, data-driven modeling based on big
data has become a popular research field in recent years because of its applicability …

Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic
environment characterized by complex spatial and temporal dynamics at various scales …