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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
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Supplemental Notes:
- Abstract reprinted with permission from Elsevier
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Authors:
- Zhou, Xuesong
- Mahmassani, Hani S
- Publication Date: 2007-10
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References;
- Pagination: pp 823-840
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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