To handle this issue, this paper proposes a graph deep learning-based fast traffic flow forecasting method in urban road networks. Firstly, the theory about graph convolution operations is deduced and ...

Traffic flow prediction is one of the major tasks in urban intelligence. Various deep learning models have been proposed. However, the main interest was to capture spatial and temporal features; ...

Since graph convolutional neural networks have received more attention, previous studies in the literature have usually adopted graph convolutional neural networks or their alternative form of ...

And the traffic flow prediction on the urban road network contributes greatly to the prediction of traffic emission's evolution. Due to the complex non-Euclidean topological structure of traffic ...

This fact motivated us to predict the traffic flow volume between Minneapolis and St. Paul at a specific point in Minnesota. We aim to build a multi-step Recurrent Nural Network (RNN) with a Long ...