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 years, traffic congestion prediction has led to a growing research area, especially
of machine learning of artificial intelligence (AI). With the introduction of big data by …

Many representative graph neural networks, $ eg $, GPR-GNN and ChebNet, approximate
graph convolutions with graph spectral filters. However, existing work either applies …

The prediction of a Lithium-ion battery's lifetime is very important for ensuring safety and
reliability. In addition, it is utilized as an early warning system to prevent the battery's failure …

Developing accurate lake water level (WL) forecasting models is important for flood control,
shoreline maintenance and sustainable water resources planning and management. In this …

Multivariate time series (MTS) prediction aims at predicting future time series by extracting
multiple forms of dependencies of past time series. Traditional prediction methods and deep …

Congestion on road networks has a negative impact on sustainability in many cities through
the exacerbation of air pollution. Smart congestion management allows road users to avoid …

Recently, linear GCNs have shown competitive performance against non-linear ones with
less computation cost, and the key lies in their propagation layers. Spectral analysis has …

Designing spectral convolutional networks is a challenging problem in graph learning.
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …

In recent times, hurricanes Matthew, Harvey, and Irma have disrupted the lives of millions of
people across multiple states in the United States. Under hurricane evacuation, efficient …