_quarto.yml

project:
  type: book


# This defines the .qmd files that are in your document
book:
  output-file: "sensitivity-uncertainty"
  title: Evaluating the Impacts of Parameter Uncertainty in a Practical Transportation Demand Model
  author:
    - name: Natalie Gibbons
      email: natalie.gibbons@kimley-horn.com
      affiliations:
      - id: 1
        name: Kimley-Horn Associates
        department: Traffic Engineering
        address:
        city: Sarasota
        state: FL
    - name: Gregory S. Macfarlane
      orcid: 0000-0003-3999-7584
      corresponding: true
      email: gregmacfarlane@byu.edu
      affiliations:
      - id: 2
        name: Civil and Construction Engineering, Brigham Young University
        department: Civil and Construction Engineering
        address: 430 EB
        city: Provo
        state: UT
        postal-code: 84602
        # to add additional authors, simply add another -name: tag 
  date: "10/26/2024"
  downloads: pdf
  chapters:
    - index.qmd
    - 02-literature.qmd
    - 03-methods.qmd
    - 04-results.qmd
    - 05-conclusions.qmd
    - 06-references.qmd
  abstract: | 
    The inherent uncertainty in travel forecasting models — arising from potential and  unkown errors  in input data, parameter   estimation, or model formulation — is receiving  increasing attention from the scholarly and practicing community.  In this research, we investigate the variance in forecasted traffic volumes  resulting from varying the mode and destination choice parameters in an  advanced trip-based travel demand model. Using Latin hypercube sampling to  construct several hundred combinations of parameters across the plausible  parameter space, we introduce substantial changes to implied travel impedances and modal utilities. However, the aggregate effects of of these  changes on forecasted traffic volumes is small, with a variance of  approximately 1 percent on high-volume facilities. It is likely that in this  example --- and perhaps in others --- the static network assignment  places constraints on the possible volume solutions and limits the practical impacts of parameter uncertainty. Further research should examine the robustness of this finding to other less constrained networks and to activity-based travel model frameworks.
    
bibliography: book.bib
csl: elsevier-harvard.csl
keywords: 
  - Travel modeling
  - Uncertainty

format:
  html:
    theme: cosmo
  mdpi-pdf:
    journal: futuretransp
    moreauthors: true
    cite-method: citeproc
    keep-tex: true
    top-level-division: section


execute: 
  cache: true