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 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 …

This research is devoted to a systematic review of multivariate models in the context of their
application to short-term traffic flow forecasting. A set of discussed models includes …

Urban traffic congestion is one of the most severe problems of everyday life in Metropolitan
areas. In an effort to deal with this problem, intelligent transportation systems (ITS) …

Traffic prediction is critical for the success of intelligent transportation systems (ITS).
However, most spatio-temporal models suffer from high mathematical complexity and low …

Short-term traffic prediction on freeways is one of the critical components of advanced
traveler information systems. The traditional methods of prediction have used univariate …

The seasonal autoregressive integrated moving average (SARIMA) model is one of the
popular univariate time-series models in the field of short-term traffic flow forecasting. The …

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 …

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 …

The application of seasonal time series models to the single-interval traffic flow forecasting
problem for urban freeways is addressed. Seasonal time series approaches have not been …