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

The trend of the era of the Internet of Everything has promoted the integration of various
industries and the Internet of Things (IoT) technology, and the scope of influence of the IoT is …

Accurate air quality prediction can help cope with air pollution and improve the life quality.
With the development of the deployments of low-cost air quality sensors, increasing data …

Decentralized traffic signal control methods, such as max-pressure (MP) control or back-
pressure (BP) control, have gained increasing attention in recent years. MP control, in …

Power forecast for each renewable power plant (RPP) in the renewable energy clusters is
essential. Though existing graph neural networks (GNN)-based models achieve satisfactory …

Ridesplitting demand prediction plays an important role in vehicle scheduling and intelligent
transportation system construction. Accurate ridesplitting demand prediction is crucial for …

The constraints of road network topology and dynamically changing traffic states over time
make the task of traffic flow prediction extremely challenging. Most existing methods use …

In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge
quantum-based decentralized and heterogeneity-aware federated learning framework for …

Ridesharing has received global popularity due to its convenience and cost efficiency for
both drivers and passengers and its strong potential to contribute to the implementation of …

Cellular traffic prediction is an essential aspect of mobile network management that uses
data analytics and machine learning to forecast the volume and pattern of communication …