This article investigates the application of Kalman filter with discrete wavelet analysis in
short‐term traffic volume forecasting. Short‐term traffic volume data are often corrupted by …

Wavelet networks (WNs) are recently developed neural network models. WN models
combine the strengths of discrete wavelet transform and neural network processing to …

Short-term traffic flow forecasting is a key step to achieve the performance of intelligent
transportation system (ITS). Timely and accurate traffic information prediction is also the …

Traffic flow prediction with high accuracy is definitely considered as one of most important
parts in the Intelligent Transportation Systems. As interfering by some external factors, the …

Accurate and timely forecasting of traffic flow is of paramount importance for effective
management of traffic congestion in intelligent transportation systems. A detailed …

Short term traffic flow forecasting has received sustained attention for its ability to provide the
anticipatory traffic condition required for proactive traffic control and management. Recently …

Two models employing Kalman filtering theory are proposed for predicting short-term traffic
volume. Prediction parameters are improved using the most recent prediction error and …

Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow
data are difficult to predict accurately. The purpose of this paper is to improve the short-term …

Based on the previous literature review, this paper builds a short-term traffic speed
forecasting model using Support Vector Machine (SVM) regression theory (referred as SVM …

Short‐term traffic flow forecasting is a fundamental and challenging task since it is required
for the successful deployment of intelligent transportation systems and the traffic flow is …