… In urban traffic prediction, we collectively encode the traffic of all nodes (locations). To make
nodes’ encoding states in the same latent embedding space such that we can quantify the …

… data for traffic prediction in urban areas, filtering literary articles no older than five years old,
where the authors addressed the use of algorithms for predicting traffic flow in urban areas, …

… and summarizes deep learning based urban traffic predictions, and we believe this work …
in urban traffic prediction. Then we discuss the potential of deep learning in traffic prediction …

… traffic sensors. By leveraging state-of-the-art deep learning technologies on such data, urban
traffic prediction … In this study, we first synthetically review the deep traffic models as well as …

… The performance of the traffic prediction is mainly … traffic prediction including innovative
sources such as social media and cellular network data. Contributions towards traffic prediction …

… predict the traffic flow of Traffic Network points, where we do not have any historical data about
them, based on the traffic patterns of Traffic … define the traffic flows prediction concepts. 2) …

… of Metropolitan Traffic Prediction. Our dataset(XiAn Road Traffic) collected 308 urban road
data and … XiAn Road Traffic can provide help for urban road state prediction and intelligent …

… traffic prediction. Recently, although there has been significant growth of works in ST prediction
(including urban traffic prediction)… framework on three typical traffic prediction tasks in the …

… to traffic flow prediction. Vythoulkas and Goodwin [15] firstly proposed the use of system
identification and ANNs to predict traffic conditions for an urban … real-time traffic changes after …

… FNM) to predict the traffic flows in an urban street network, which has long been considered
a major element in the responsive urban traffic … While the GN groups traffic patterns of similar …