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

TLDR

The study applied Box–Jenkins techniques to analyze freeway traffic volume and occupancy time series. The authors built short‑term traffic predictors using 166 datasets from Los Angeles, Minneapolis, and Detroit surveillance systems. ARIMA(0,1,3) models best fit the data, outperforming moving‑average, double‑exponential smoothing, and Trigg–Leach adaptive models, with parameters varying by location and time, and the authors suggest operational use for one‑interval forecasts. Author: /Author/.

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/