Estimation of sparse O-D matrix accounting for demand volatility

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Tao Wen - , Commonwealth Scientific & Industrial Research Organisation (CSIRO) (Author)
  • Chen Cai - , Commonwealth Scientific & Industrial Research Organisation (CSIRO) (Author)
  • Lauren Gardner - , University of New South Wales (Author)
  • Steven Travis Waller - , University of New South Wales (Author)
  • Vinayak Dixit - , University of New South Wales (Author)
  • Fang Chen - , Commonwealth Scientific & Industrial Research Organisation (CSIRO) (Author)

Abstract

A critical issue in origin-destination (O-D) demand estimation is under-determination: the number of O-D pairs to be estimated is often much greater than the number of monitored links. In real world, some centroids tend to be more popular than others, and only few trips are made for intro-zonal travel. Consequently, a large portion of trips will be made for a small portion of O-D pairs, meaning many O-D pairs have only a few or even zero trips. Mathematically, this implies that the O-D matrix is sparse. Also, the correlation between link flows is often neglected in the O-D estimation problem, which can be obtained from day-to-day loop detector count data. Thus, sparsity regularisation is combined with link flow correlation to provide additional inputs for the O-D estimation process to mitigate the issue of under-determination and thereby improve estimation quality. In addition, a novel strategic user equilibrium model is implemented to provide route choice of users for the O-D estimation problem, which explicitly accounts for demand and link flow volatility. The model is formulated as a convex generalised least squares problem with regularisation, the usefulness of sparsity assumption, and link flow correlation is presented in the numerical analysis.

Details

Original languageEnglish
Pages (from-to)1020-1026
Number of pages7
JournalIET intelligent transport systems
Volume12
Issue number9
Publication statusPublished - 1 Nov 2018
Peer-reviewedYes
Externally publishedYes

External IDs

ORCID /0000-0002-2939-2090/work/141543784