Accurate and stable short-term traffic flow prediction is an indispensable part in current
intelligent transportation systems. In this paper, a novel short-term traffic flow forecasting …

Short-term traffic flow prediction is one of the most important issues in the field of intelligent
transportation systems. It plays an important role in traffic information service and traffic …

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the
short-term traffic flow forecasting methods based on deep learning need to be further …

The existing short-term traffic flow prediction models fail to provide precise prediction results
and consider the impact of different traffic conditions on the prediction results in an actual …

Accurate and reliable traffic flow forecasting is of importance for urban planning and
mitigation of traffic congestion, and it is also the basis for the deployment of intelligent traffic …

Deep learning has achieved good performance in short-term traffic forecasting recently.
However, the stochasticity and distribution imbalance are main characteristics to traffic flow …

This study proposes a short-term traffic flow prediction framework. The vector autoregression
(VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model …

The accurate prediction of real-time traffic flow is indispensable to intelligent transport
systems. However, the short-term prediction remains a thorny issue, due to the complexity …

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation
Systems. Accurate prediction result is the precondition of traffic guidance, management, and …

This paper proposes a novel neural network (NN) training method that employs the hybrid
exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to …