Towards Online Quasi-dynamic o-d Flow Estimation/Updating

The paper deals with the proposition of a Kalman filter specification for quasi-dynamic estimation/updating of origin-destination (o-d) flows from traffic counts, i.e. under the assumption that o-d shares are constant across a reference period (i.e. a quasi-dynamic interval), whilst total flows leaving each origin vary for each sub-period within the reference period. Drawing upon the effectiveness and the reliability of the assumption of quasi-dynamic o-d flow pattern and of the performances of the quasi-dynamic estimator in offline contexts, the paper illustrates a first formulation of a non-linear quasi-dynamic Kalman filter, which can embed diverse specifications of the state variables and of the corresponding transition and measurement equations. Results of preliminary tests on a synthetic network are presented, and the overall research pattern is also outlined, together with concerned research and practical perspectives.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 1471-1476
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01599798
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:22PM