… prediction for short-term network traffic is a difficult problem. This paper proposes a real-time
network traffic prediction model based on Long Short-Term … of the prediction model. Different …

… for long term prediction of pedestrians from on-board observations. We show predictions
over a … Key to our success is a Bayesian approach and long term prediction of odometry. We …

… traffic predictions. … traffic prediction baselines on MAE and RMSE over 10%, and achieve
a stable performance for two specific tasks (long-term traffic prediction and peak time prediction)…

… makes use of macroscopic traffic flow modeling for real-time traffic prediction. That approach
… ; our goal was to develop a traffic prediction methodology robust and accurate enough to …

… traffic network, traffic flow prediction is an important task for real-time operation of traffic …
In the traditional traffic prediction research, short-term traffic flow prediction based on …

… We propose a LSTM model to predict the short-term traffic flow. LSTM is able to exploit the
long-term dependency in the traffic flow data sequence. As one of the deep learning approach…

… The proposed works would combine short-term and longterm Random Forest prediction
model of traffic and compare it with the non-combined method. This method would also be …

… long-period data to the target city. In the experiments, we compare our model with other
state-of-the-art methods in real-world traffic prediction … to make accurate traffic prediction in a data…

… In [39], DBN has also been explored for longer term (ie, day ahead) traffic prediction. …
of applying dynamical traffic information for short term traffic prediction. To achieve this target…

… If an exogenous explanatory variable is available to help model the response (eg, using
precipitation data to help predict traffic flow in this study), then dynamic regression may be used. …