Time dependent origin-destination estimation from traffic count without prior information

Hsun-Jung Cho*, Yow Jen Jou, Chien Lun Lan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Existing research works on time-dependent origin-destination (O-D) estimation focus on the surveillance data usually assume the prior information of the O-D matrix (or transition matrix) is known (or at least partially known). In this paper, we relax such assumption by combining Gibbs sampler and Kalman filter in a state space model. A solution algorithm with parallel chain convergence control is proposed and implemented. To enhance its efficiency, a parallel structure is suggested with efficiency and speedup demonstrated using PC-cluster. Two numerical examples (one for Taipei Mass Rapid Transit network and the other for Taiwan Area National Freeway network) are included to show the proposed model could be effective of time-dependent origin-destination estimation.

Original languageEnglish
Pages (from-to)145-170
Number of pages26
JournalNetworks and Spatial Economics
Issue number2
StatePublished - 1 Jan 2009


  • Gibbs sampler
  • Kalman Filter
  • Parallel computing
  • State space model
  • Time-dependent origin-destination estimation

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