Traffic Forecasting for King Fahd Causeway: Comparison of Parametric Technique with Artificial Neural Networks

Traffic prediction involves forecasting traffic in terms of Annual Average Daily Traffic (AADT), Design Hour Volumes (DHV) and Directional Design Hour Volumes (DDHV). These forecasts are used for a wide variety of purposes from the planning to the design and operational stages of the highway network. The forecasting needs the historical traffic data as well as the systems characteristics, apart from that choice of an appropriate model or technique is also an important consideration. This paper gives an overview of the traffic forecasting process and the models that are used for this purpose with emphasis on the use of Artificial Neural Networks (ANNs) and other modern techniques. ANNs are being compared with the traditional Parametric techniques used in this regard by applying linear regression analysis and ANNs for daily traffic forecasting on King Fahd causeway. It was observed from the estimated error values of both techniques that ANNs have better accuracy than linear regression technique for predicting daily traffic. Keywords— Traffic Forecasting, Artificial Neural Networks, Linear Regression