Adaptive Seasonal Time Series Models for Forecasting Short-Term Traffic Flow

Conventionally, most traffic forecasting models have been applied in a static framework in which new observations are not used to update model parameters automatically. The need to perform periodic parameter reestimation at each forecast location is a major disadvantage of such models. From a practical standpoint, the usefulness of any model depends not only on its accuracy but also on its ease of implementation and maintenance. This paper presents an adaptive parameter estimation methodology for univariate traffic condition forecasting through use of three well-known filtering techniques: the Kalman filter, recursive least squares, and least mean squares. Results show that forecasts obtained from recursive adaptive filtering methods are comparable with those from maximum likelihood estimated models. The adaptive methods deliver this performance at a significantly lower computational cost. As recursive, self-tuning predictors, the adaptive filters offer plug-and-play capability ideal for implementation in real-time management and control systems. The investigation presented in this paper also demonstrates the robustness and stability of the seasonal time series model underlying the adaptive filtering techniques.

• Record URL:
  https://doi.org/10.3141/2024-14
• 
• Summary URL:
  http://scholar.google.com/scholar_lookup?title=Adaptive+Seasonal+Time+Series+Models+for+Forecasting+Short-Term+Traffic+Flow&author=S.+Shekhar&author=B.+Williams&publication_year=2007
• Availability:
  • Find a library where document is available. Order URL: http://www.trb.org/Main/Public/Blurbs/159707.aspx
• Authors:
  • Shekhar, Shashank
  • Williams, Billy M
• Publication Date: 2007

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 116-125
• Monograph Title: Information Technology, Geographic Information Systems, and Artificial Intelligence
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 2024
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Kalman filtering; Least squares method; Real time control; Real time information; Time series; Traffic flow; Traffic forecasting
• Uncontrolled Terms: Adaptive filters; Plug and play capability; Seasonal variations; Short term; Short term forecasts
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01042605
• Record Type: Publication
• ISBN: 9780309104517
• Files: TRIS, TRB, ATRI
• Created Date: Feb 8 2007 6:42PM