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 …

Several univariate and multivariate models have been proposed for performing short-term
forecasting of traffic flow. Two different univariate [historical average and ARIMA …

This paper contains some suggestions on how to use traffic counts on links in networks and
non-linear regression in order to estimate parameters in models for traffic prediction, for …

There are numerous studies on traffic volume prediction, using either non-parametric or
parametric methods. The main shortcoming of parametric methods is low prediction …

This paper attempts to develop time-series models for forecasting traffic volume in urban
arterials. The Box-Jenkins approach is used to estimate the time-series models. A 1-min …

We develop a Kalman filter for predicting traffic flow at urban arterials based on data
obtained from connected vehicles. The proposed algorithm is computationally efficient and …

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 …

A radial basis function (RBF) neural network has recently been applied to time-series
forecasting. The test results of an RBF neural network in forecasting short-term freeway …

Congestion is increasing in many urban areas. This has led to a growing awareness of the
importance of accurate traffic-flow predictions. In this paper, we introduce a prediction …

Short-term prediction of traffic conditions is one of the central topics in contemporary ITS
research and practice. However, it is hard to select the most appropriate traffic prediction …