MODELING AND FORECASTING VEHICULAR TRAFFIC FLOW AS A SEASONAL STOCHASTIC TIME SERIES PROCESS

Extensive data collection is now commonplace for urban freeway and street network systems. Research efforts are underway to unleash the system management potential inherent in this unprecedented access to traffic condition data. A key element in this research area is traffic condition forecasting. Reliable and accurate condition forecasts will enable transportation management systems to dynamically anticipate the future state of the system rather than merely respond to the current situation. Recent traffic flow prediction efforts have focused on application of neural networks, nonparametric regression using nearest neighbor algorithms, multiple class linear regression based on automatic clustering, time series analysis techniques, and hybrid models combining two or more of these approaches. Seasonal time series methods, such as seasonal ARIMA models and Holt-Winters smoothing, have not been among the investigated forecasting techniques. The need to explore these techniques is motivated by a strong theoretical expectation that they will provide accurate and parsimonious traffic condition models. The findings presented in this report establish the traffic flow prediction superiority of seasonal time series methods, especially seasonal ARIMA modeling, over the recently developed methods. The research also contributes a specific application of time series outlier modeling theory to vehicular traffic flow data. This outlier detection and modeling procedure uncovered a common ARIMA model form among the seasonally stationary series used in this research. This common model form is ARIMA (1,0,1)(O,1,1)S, where S is the length of the series seasonal cycle. A glossary of terms is included.

• Corporate Authors:

  Mid-Atlantic Universities Transportation Center

  Pennsylvania State University
  201 Transportation Research Building
  University Park, PA  United States  16802-4710

  Virginia Department of Transportation

  Office of Public Affairs, 1221 East Broad Street
  Richmond, VA  United States  23219

  University of Virginia, Charlottesville

  Department of Civil Engineering
  351 McCormick Road, P O Box 400742
  Charlottesville, VA  United States  22904-4742
• Authors:
  • Williams, B M
  • Hoel, L A
• Publication Date: 1999-6

Language

• English

Media Info

• Features: Figures; References; Tables;
• Pagination: 243 p.

Subject/Index Terms

• TRT Terms: Algorithms; Exponential distributions; Forecasting; Freeways; Linear regression analysis; Mathematical models; Mathematical prediction; Neural networks; Nonparametric analysis; Regression analysis; Short range planning; Stochastic processes; Time series analysis; Traffic control; Traffic flow; Traffic forecasting
• Uncontrolled Terms: Outlier detection
• Geographic Terms: Beaune (France); London (England); Washington (District of Columbia)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I71: Traffic Theory;

Filing Info

• Accession Number: 00790667
• Record Type: Publication
• Report/Paper Numbers: LTVA/29242/CE99/103
• Contract Numbers: USDOT-TPSU-UV-0003-1113
• Files: UTC, TRIS, STATEDOT
• Created Date: Apr 17 2000 12:00AM