Path Flow Estimation Using Time Varying Coefficient State Space Model

Yow-Jen Jou*, Chien Lun Lan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.
Original languageEnglish
Title of host publicationComputational Methods In Science And Engineering, Vol 2: Advances In Computational Science
EditorsG Maroulis, TE Simos
Pages501-+
Volume1148
StatePublished - 2009
Event6th International Conference on Computational Methods in Sciences and Engineering 2008, ICCMSE 2008 - Hersonissos, Crete, Greece
Duration: 25 Sep 200830 Sep 2008

Publication series

NameAIP Conference Proceedings
Volume 1148
ISSN (Print)0094-243X

Conference

Conference6th International Conference on Computational Methods in Sciences and Engineering 2008, ICCMSE 2008
CountryGreece
CityHersonissos, Crete
Period25/09/0830/09/08

Keywords

  • transportation; Monte Carlo Markov Chain; state space model; Kalman filter

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  • Cite this

    Jou, Y-J., & Lan, C. L. (2009). Path Flow Estimation Using Time Varying Coefficient State Space Model. In G. Maroulis, & TE. Simos (Eds.), Computational Methods In Science And Engineering, Vol 2: Advances In Computational Science (Vol. 1148, pp. 501-+). (AIP Conference Proceedings; Vol. 1148 ).