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

Xuesong Zhou, Hani S. Mahmassani

Research output: Contribution to journalArticle

101 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)823-840
Number of pages18
JournalTransportation Research Part B: Methodological
Volume41
Issue number8
DOIs
StatePublished - Oct 2007
Externally publishedYes

Fingerprint

traffic
demand
demand pattern
learning
Polynomials
trend
system model
fluctuation
time
Prediction
Demand estimation
Destination
State-space model
methodology
Deviation
Dynamic traffic assignment

Keywords

  • Dynamic OD estimation and prediction
  • Kalman filter
  • Real-time traffic estimation and prediction
  • Traffic system management

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Transportation

Cite this

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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.",
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