In this paper, an aggregation approach is proposed for traffic flow prediction that is based on
the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and …

The traffic flow prediction plays a key role in modern Intelligent Transportation Systems (ITS).
Although great achievements have been made in traffic flow prediction, it is still a challenge …

The accurate short-term traffic flow forecasting is fundamental to both theoretical and
empirical aspects of intelligent transportation systems deployment. In order to play the …

Real-time traffic flow forecasting is one of important issues of ITS research. Some forecasting
models including history average, time-series, Kalman filtering, non-parametric regression …

We propose a novel approach for network-wide traffic state prediction where the statistical
time series model ARIMA is used to postprocess the residuals out of the fundamental …

Background Accurate prediction of traffic flow is an integral component in most of the
Intelligent Transportation Systems (ITS) applications. The data driven approach using Box …

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 literature on short-term traffic flow forecasting has undergone great development
recently. Many works, describing a wide variety of different approaches, which very often …

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 …

Based on the observation that the correlation between observed traffic at two measurement
points or traffic stations may be time-varying, attributable to the time-varying speed which …