ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES

Mohammed S. Ahmed · A R Cook

767 citations0/0 files storedTransportation Research Record Journal of the Transportation Research Board

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Traffic Prediction and Management Techniques Traffic control and management Forecasting Techniques and Applications

Abstract

This paper investigated the application of analysis techniques develoepd by Box and Jenkins to freeway traffic volume and occupancy time series. A total of 166 data sets from three surveillance systems in Los Angeles, Minneapolis, and Detroit were used in the development of a predictor model to provide short-term forecasts of traffic data. All of the data sets were best represented by an autoregressive integrated moving-average (ARIMA) (0,1,3) model. The moving-average parameters of the model, however, vary from location to location and over time. The ARIMA models were found to be more accurate in representing freeway time-series data, in terms of mean absolute error and mean square error, than moving-average, double-exponential smoothing, and Trigg and Leach adaptive models. Suggestions and implications for the operational use of the ARIMA model in making forecasts one time interval in advance are made. /Author/

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1967Exponential Smoothing with an Adaptive Response Rate 1974Applied Time Series Analysis for Managerial Forecasting.1969The Development of a Self-Adaptive Forecasting Technique

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