Sensitivity-based uncertainty analysis of a combined travel demand model

Chao Yang, Anthony Chen, Xiangdong Xu, S. C. Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)

Abstract

Travel demand forecasting is subject to great uncertainties. A systematic uncertainty analysis can provide insights into the level of confidence on the model outputs, and also identify critical sources of uncertainty for enhancing the robustness of the travel demand model. In this paper, we develop a systematic framework for quantitative uncertainty analysis of a combined travel demand model (CTDM) using the analytical sensitivity-based method. The CTDM overcomes limitations of the sequential four-step procedure since it is based on a single unifying rationale. The analytical sensitivity-based method requires less computational effort than the sampling-based method. Meanwhile, the uncertainties stemming from inputs and parameters can be treated separately so that the individual and collective effects of uncertainty on the outputs can be clearly assessed and quantified. Numerical examples are finally used to demonstrate the proposed sensitivity-based uncertainty analysis method for the CTDM.
Original languageEnglish
Pages (from-to)225-244
Number of pages20
JournalTransportation Research Part B: Methodological
Volume57
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes

Keywords

  • Combined travel demand model
  • Nonlinear program
  • Sensitivity analysis
  • Uncertainty analysis

ASJC Scopus subject areas

  • Transportation

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