A structural state space model for real-time traffic origin-destination demand estimation and prediction in a day-to-day learning framework

Dynamic origin-destination (OD) estimation and prediction is an essential support function for real-time dynamic traffic assignment model systems for ITS applications. This paper presents a structural state space model to systematically incorporate regular demand pattern information, structural deviations and random fluctuations. By considering demand deviations from the a priori estimate of the regular pattern as a time-varying process with smooth trend, a polynomial trend filter is developed to capture possible structural deviations in real-time demand. Based on a Kalman filtering framework, an optimal adaptive procedure is further proposed to capture day-to-day demand evolution, and update the a priori regular demand pattern estimate using new real-time estimates and observations obtained every day. These models can be naturally integrated into a real-time dynamic traffic assignment system and provide an effective and efficient approach to utilize the real-time traffic data continuously in operational settings. A case study based on the Irvine test bed network is conducted to illustrate the proposed methodology.

• Record URL:
  http://www.sciencedirect.com/science/article/B6V99-4ND7J7B-2/2/12b6323dad96547466af8897f724018a
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • Abstract reprinted with permission from Elsevier
• Authors:
  • Zhou, Xuesong
  • Mahmassani, Hani S
• Publication Date: 2007-10

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References;
• Pagination: pp 823-840
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 41
  • Issue Number: 8
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Days; Demand; Deviation (Statistics); Dynamic models; Estimation theory; Intelligent transportation systems; Learning; Mathematical prediction; Methodology; Origin and destination; Polynomials; Spatial analysis; Structural models; Traffic assignment; Traffic models
• Uncontrolled Terms: Frameworks; Traffic patterns
• Subject Areas: Bridges and other structures; Highways; Operations and Traffic Management; Planning and Forecasting; I10: Economics and Administration; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01076611
• Record Type: Publication
• Files: TRIS, ATRI
• Created Date: Aug 27 2007 4:46PM