… algorithm for traffic estimation and short-term traffic predictions. Our algorithm uses a (discretization
of) macroscopic traffic model to produce traffic state estimations and predictions, …

… of RNN techniques towards the large scale traffic matrix prediction, in this paper we apply …
Section III provides the background information of RNN networks and traffic matrix prediction. …

… in traffic prediction from multiple perspectives. Specifically, we first summarize the existing
traffic prediction … Second, we list the state-of-the-art approaches in different traffic prediction …

… a geographical graph G , predicting the traffic state of the road network for the … -term single-step
traffic prediction scenario, S is set to 1 in the experiments. Therefore, the core of the traffic …

… estimate traffic flows. However, only several novel studies for multi-interval prediction have
been reported to forecast near-term traffic variables such as travel time, speed and traffic flow. …

… In order to conduct accurate and efficient short-term traffic predictions, this study presents
a local learning framework based on the partial least square (PLS) regression. PLS is a low-…

… We find that the results for the "near-term" 12 datasets are similar to those from the "long-term"
9 datasets, demonstrating that once a model has been built, it can potentially be used for …

This paper proposes a model of neural network which can be used to combine Long Short
Term Memory networks (LSTM) with Deep Neural Networks (DNN). Autocorrelation coefficient …

… to predict the future traffic … -term traffic congestion prediction with the real vehicle passage
records data in Jinan, China. We contrast the performance of PCNN with state-of-the-art traffic …

… Extensive experiments on real-world traffic prediction tasks demonstrate the superiority of …
with two types of traffic prediction tasks: traffic flow prediction and traffic speed prediction. The …