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

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/

Media Info

  • Media Type: Print
  • Features: Figures; References; Tables;
  • Pagination: pp 1-9
  • Monograph Title: Urban systems operations
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 00308575
  • Record Type: Publication
  • ISBN: 0309029724
  • Files: TRIS, TRB
  • Created Date: Apr 22 1980 12:00AM