Abstract
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.
Original language | English (US) |
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Pages (from-to) | 823-840 |
Number of pages | 18 |
Journal | Transportation Research Part B: Methodological |
Volume | 41 |
Issue number | 8 |
DOIs | |
State | Published - Oct 2007 |
Externally published | Yes |
Keywords
- Dynamic OD estimation and prediction
- Kalman filter
- Real-time traffic estimation and prediction
- Traffic system management
ASJC Scopus subject areas
- Civil and Structural Engineering
- Transportation