Graph neural network for traffic forecasting: A survey
Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …
learning models, including convolution neural networks and recurrent neural networks, have …
Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management
Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …
people. Accurate flood forecasts and control are essential to lessen these effects and …
Bernnet: Learning arbitrary graph spectral filters via bernstein approximation
Many representative graph neural networks, $ eg $, GPR-GNN and ChebNet, approximate
graph convolutions with graph spectral filters. However, existing work either applies …
graph convolutions with graph spectral filters. However, existing work either applies …
Convolutional neural networks on graphs with chebyshev approximation, revisited
Designing spectral convolutional networks is a challenging problem in graph learning.
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …
A review of traffic congestion prediction using artificial intelligence
M Akhtar, S Moridpour - Journal of Advanced Transportation, 2021 - Wiley Online Library
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 …
of machine learning of artificial intelligence (AI). With the introduction of big data by …
A hybrid visualization model for knowledge mapping: Scientometrics, SAOM, and SAO
Predicting the crowd flow in various areas of the city is of strategic importance for traffic
control and public safety. In recent years, crowd flow prediction based on spatio-temporal …
control and public safety. In recent years, crowd flow prediction based on spatio-temporal …
Remaining useful life assessment for lithium-ion batteries using CNN-LSTM-DNN hybrid method
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 …
reliability. In addition, it is utilized as an early warning system to prevent the battery's failure …
A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction
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 …
multiple forms of dependencies of past time series. Traditional prediction methods and deep …