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

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  • Zhou, Xuesong
  • Mahmassani, Hani S.

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.

Suggested Citation

  • Zhou, Xuesong & Mahmassani, Hani S., 2007. "A structural state space model for real-time traffic origin-destination demand estimation and prediction in a day-to-day learning framework," Transportation Research Part B: Methodological, Elsevier, vol. 41(8), pages 823-840, October.
    • Handle: RePEc:eee:transb:v:41:y:2007:i:8:p:823-840
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    References listed on IDEAS

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