IDEAS home Printed from https://ideas.repec.org/a/ags/ndjtrf/262661.html
   My bibliography  Save this article

Local Sensitivity Analysis of Forecast Uncertainty in a Random-Utility-Based Multiregional Input-Output Model

Author

Listed:
  • Wang, Guangmin
  • Kockelman, Kara M.

Abstract

Transportation systems are critical to regional economies and quality of life. The Random-Utility- Based Multiregional Input-Output Model (RUBMRIO) for trade and travel choices is used here to appreciate the distributed nature of commodity flow patterns across the United States’ 3,109 contiguous counties and 12 industry sectors, for rail and truck operations. This paper demonstrates the model’s sensitivity to various inputs using the method of local sensitivity analysis with interactions (LSAI). This work simulates both individual effects as well as interaction effects of model inputs on outputs by providing sensitivity indices of model outputs to variations of inputs under two scenarios. Model outputs include predictions of domestic and export trade flows, value of goods produced, labor expenditures, and household and industry consumption levels across the counties in the United States. The LSAI technique allows transportation system operators to appreciate the roles of any model input and the associated uncertainty of outputs.

Suggested Citation

  • Wang, Guangmin & Kockelman, Kara M., 2016. "Local Sensitivity Analysis of Forecast Uncertainty in a Random-Utility-Based Multiregional Input-Output Model," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 55(2), August.
  • Handle: RePEc:ags:ndjtrf:262661
    DOI: 10.22004/ag.econ.262661
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/262661/files/v55n2_04-Random-Utility-Based.pdf
    Download Restriction: no

    File URL: https://ageconsearch.umn.edu/record/262661/files/v55n2_04-Random-Utility-Based.pdf?subformat=pdfa
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.262661?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Huang, Tian & Kockelman, Kara M., 2008. "The Introduction of Dynamic Features in a Random-Utility-Based Multiregional Input-Output Model of Trade, Production, and Location Choice," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 47(1).
    2. Saltelli A. & Tarantola S., 2002. "On the Relative Importance of Input Factors in Mathematical Models: Safety Assessment for Nuclear Waste Disposal," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 702-709, September.
    3. A Anas, 1984. "Discrete Choice Theory and the General Equilibrium of Employment, Housing, and Travel Networks in a Lowry-Type Model of the Urban Economy," Environment and Planning A, , vol. 16(11), pages 1489-1502, November.
    4. Lefèvre, Benoit, 2009. "Long-term energy consumptions of urban transportation: A prospective simulation of "transport-land uses" policies in Bangalore," Energy Policy, Elsevier, vol. 37(3), pages 940-953, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zahra Kalantari & Sara Khoshkar & Helena Falk & Vladimir Cvetkovic & Ulla Mörtberg, 2017. "Accessibility of Water-Related Cultural Ecosystem Services through Public Transport—A Model for Planning Support in the Stockholm Region," Sustainability, MDPI, vol. 9(3), pages 1-16, February.
    2. Eric Tate, 2012. "Social vulnerability indices: a comparative assessment using uncertainty and sensitivity analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 325-347, September.
    3. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    4. Plischke, Elmar & Borgonovo, Emanuele, 2019. "Copula theory and probabilistic sensitivity analysis: Is there a connection?," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1046-1059.
    5. Borgonovo, E., 2010. "Sensitivity analysis with finite changes: An application to modified EOQ models," European Journal of Operational Research, Elsevier, vol. 200(1), pages 127-138, January.
    6. Di Maio, Francesco & Nicola, Giancarlo & Borgonovo, Emanuele & Zio, Enrico, 2016. "Invariant methods for an ensemble-based sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 12-19.
    7. Hu, Zhen & Mahadevan, Sankaran, 2019. "Probability models for data-Driven global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 40-57.
    8. Tarantola, S. & Gatelli, D. & Mara, T.A., 2006. "Random balance designs for the estimation of first order global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 717-727.
    9. Tobias Fissler & Silvana M. Pesenti, 2022. "Sensitivity Measures Based on Scoring Functions," Papers 2203.00460, arXiv.org, revised Jul 2022.
    10. Zhao, Yong & Kockelman, Kara M., 2004. "The random-utility-based multiregional input-output model: solution existence and uniqueness," Transportation Research Part B: Methodological, Elsevier, vol. 38(9), pages 789-807, November.
    11. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    12. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    13. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    14. Becker William & Paruolo Paolo & Saltelli Andrea, 2021. "Variable Selection in Regression Models Using Global Sensitivity Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 13(2), pages 187-233, July.
    15. Michael Wegener, 2011. "Transport in Spatial Models of Economic Development," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 3, Edward Elgar Publishing.
    16. Beccacece, F. & Borgonovo, E., 2011. "Functional ANOVA, ultramodularity and monotonicity: Applications in multiattribute utility theory," European Journal of Operational Research, Elsevier, vol. 210(2), pages 326-335, April.
    17. Daneshkhah, A. & Stocks, N.G. & Jeffrey, P., 2017. "Probabilistic sensitivity analysis of optimised preventive maintenance strategies for deteriorating infrastructure assets," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 33-45.
    18. Saltelli, Andrea & Ratto, Marco & Tarantola, Stefano & Campolongo, Francesca, 2006. "Sensitivity analysis practices: Strategies for model-based inference," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1109-1125.
    19. Martin Schlossarek & Miroslav Syrovátka & Ondřej Vencálek, 2019. "The Importance of Variables in Composite Indices: A Contribution to the Methodology and Application to Development Indices," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(3), pages 1125-1160, October.
    20. Xu, Chonggang & Gertner, George, 2011. "Understanding and comparisons of different sampling approaches for the Fourier Amplitudes Sensitivity Test (FAST)," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 184-198, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:ndjtrf:262661. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: http://www.trforum.org/journal/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.