VEHICLE VOLUME FORECASTING SYSTEM ON TOLL ROADS USING DOUBLE MOVING AVERAGE AND DOUBLE EXPONENTIAL SMOOTHING METHODS

Abstract

Forecasting or forecasting has been used as one of the considerations, especially in the field of business and economics. This is done so that company losses can be minimized and company profits can be maximized. Therefore we need a system that can make it easier to get accurate forecasting results. However, in real cases procurement of IT consultants requires quite expensive costs. So the research provides a solution to design a website-based vehicle volume forecasting system on toll roads conducted at PT. Jasamarga Pandaan Tol. This system is expected to facilitate the Traffic Collection team in projecting the volume of vehicles on toll roads in the future. The method used in forecasting vehicle volume is the Double Moving Average and Double Exponential Smoothing methods. The Double Moving Average method is a time series method where the results of this method are influenced by the order or time period. The Exponential Smoothing method is a time series method where the results of this method are influenced by the alpha parameter. The results of these two methods will be compared based on the accuracy of the data so that the user can easily see the results of forecasting vehicle volume in the future. Mean Absolute Percentage Error (MAPE) is the absolute (absolute) average percentage error. Definition of Mean Absolute Percentage Error is a statistical measurement of the accuracy of estimates (predictions) in forecasting methods. The results of the accuracy of applying the Double Moving Average method give an average MAPE value of 30,124% and the Double Exponential Smoothing method gives an average MAPE value of 5,368%.