… -term prediction is still limited. To this end, this paper focuses on solving the problem of
long-term traffic predictions. Existing traffic prediction models mainly include datadriven statistical …

… Therefore, we design a calibration layer to slightly adjust the prediction results. The goal of
this research is to develop an effective long-term traffic flow forecasting method, which we call …

… Model-driven approaches are extensively used for longterm prediction, especially when
planning changes to existing infrastructure. In this situation, we are modeling future …

… that road traffic speed information will be completely different from actual road traffic speeds.
… obtain more accurate predictions of traffic speeds on the road by using long-term evolution (…

… As shown in Table 2, LSGCN performs well in both longterm and short-term prediction for …
ly in the long-term prediction of PeMSD4, both long-term prediction and short-term prediction of …

… In this work, we propose different models for short-term traffic prediction. For every model we
discuss the parameters that include: 1) The number of sensors a model covers for prediction…

… The results obtained demonstrate that, once trained, our solutions provide high-accuracy
long-term (10-hour long) traffic predictions, while operating with short observation intervals (2 …

… a traffic prediction method based on a deep neural network for daily long-term traffic
prediction by considering the relationship between external factors and traffic flow [13]. Recently, …

… spatial–temporal correlations for short and long term prediction. … both long and short-term
traffic prediction tasks. To the best of our knowledge, this is the first study on traffic prediction …

… Aiming at the Periodicity and long correlation of longterm span network traffic, this paper
proposes an improved network traffic prediction algorithm: Long-Term Span Traffic Prediction …