… traffic forecasting in ITS systems, we will first introduce the corresponding ITS applications and
discuss how traffic forecasting … based prediction methods and the recurrent neural network …

… are graph neural networks (in blue) for network-wide traffic prediction. Other popular deep-…
classify them in terms of predicting targets, deep-learning models, wireless traffic sensors, and …

… historical data in forecasting traffic congestion. … learning is the most popular branch of AI.
Other classes of AI include probabilistic models, deep learning, artificial neural network systems…

… In short-term traffic forecasting, the prediction horizon usually ranges from seconds to …
for future work in deep neural network (DNN)-based short-term traffic prediction (STTP) by …

… In this paper, we propose a model for predicting network traffic … traffic flow forecasting model
with a deep learning approach,” IEEE transactions on neural networks and learning systems, …

… management, alongside an accurate traffic simulation model. However, missing data … in
predicting the congestion levels, resulting in a less efficient rerouting. The lack of a realistic traffic …

… a traditional DNN network (Deep Neural Network) to obtain predictive results. One … Neural
Network techniques are the most common tools used in network traffic volume prediction…

… Thus, learning an optimized graph from the observed traffic … Recurrent Neural Network
(OGCRNN) for traffic prediction. The … JC Golias, “Short-term traffic forecasting: Where we are and …

… Graph Recurrent Attention neural Networks (VGRAN) for robust traffic forecasting. It … is
capable of learning latent variables regarding the sensor representation and traffic sequences. …

… Therefore, this study aims to contribute a deep learning based spatiotemporal neural network
to predict citywide traffic flow at the road level in fine temporal scale of 10 minutes with high …