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The propagation of uncertainty through travel demand models: An exploratory analysis

Author

Listed:
  • Yong Zhao

    () (The University of Texas at Austin, 6.9 E. Cockrell Jr. Hall, Austin, TX 78712-1076, USA)

  • Kara Maria Kockelman

    () (The University of Texas at Austin, 6.9 E. Cockrell Jr. Hall, Austin, TX 78712-1076, USA)

Abstract

The future operations of transportation systems involve a lot of uncertainty - in both inputs and model parameters. This work investigates the stability of contemporary transport demand model outputs by quantifying the variability in model inputs, such as zonal socioeconomic data and trip generation rates, and simulating the propagation of their variation through a series of common demand models over a 25-zone network. The results suggest that uncertainty is likely to compound itself - rather than attenuate - over a series of models. Mispredictions at early stages (e.g., trip generation) in multi-stage models appear to amplify across later stages. While this effect may be counteracted by equilibrium assignment of traffic flows across a network, predicted traffic flows are highly and positively correlated.

Suggested Citation

  • Yong Zhao & Kara Maria Kockelman, 2002. "The propagation of uncertainty through travel demand models: An exploratory analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 36(1), pages 145-163.
    • Handle: RePEc:spr:anresc:v:36:y:2002:i:1:p:145-163
    Note: Received: March 2001/Accepted: August 2001
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    Cited by:

    1. Yin, Yafeng & Madanat, Samer M. & Lu, Xiao-Yun, 2009. "Robust improvement schemes for road networks under demand uncertainty," European Journal of Operational Research, Elsevier, vol. 198(2), pages 470-479, October.
    2. repec:exl:1trans:v:10:y:2015:i:1:p:55-62 is not listed on IDEAS
    3. Sumalee, Agachai & Xu, Wei, 2011. "First-best marginal cost toll for a traffic network with stochastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 41-59, January.
    4. Xu, Xiangdong & Chen, Anthony & Wong, S.C. & Cheng, Lin, 2015. "Selection bias in build-operate-transfer transportation project appraisals," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 245-251.
    5. Hua Sun & Ziyou Gao & W. Szeto & Jiancheng Long & Fangxia Zhao, 2014. "A Distributionally Robust Joint Chance Constrained Optimization Model for the Dynamic Network Design Problem under Demand Uncertainty," Networks and Spatial Economics, Springer, vol. 14(3), pages 409-433, December.
    6. Lam, William H.K. & Shao, Hu & Sumalee, Agachai, 2008. "Modeling impacts of adverse weather conditions on a road network with uncertainties in demand and supply," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 890-910, December.
    7. Parthasarathi, Pavithra & Levinson, David, 2010. "Post-construction evaluation of traffic forecast accuracy," Transport Policy, Elsevier, vol. 17(6), pages 428-443, November.
    8. Westin, Jonas & de Jong, Gerard & Vierth, Inge & Krüger, Niclas & Karlsson, Rune & Johansson, Magnus, 2015. "Baserunning - analyzing the sensitivity and economies of scale of the Swedish national freight model system using stochastic production-consumption-matrices," Working papers in Transport Economics 2015:10, CTS - Centre for Transport Studies Stockholm (KTH and VTI), revised 15 Sep 2016.
    9. Sanko, Nobuhiro & Morikawa, Takayuki & Nagamatsu, Yoshitaka, 2013. "Post-project evaluation of travel demand forecasts: Implications from the case of a Japanese railway," Transport Policy, Elsevier, vol. 27(C), pages 209-218.
    10. Maria Börjesson & Jonas Eliasson & Mattias Lundberg, 2014. "Is CBA Ranking of Transport Investments Robust?," Journal of Transport Economics and Policy, University of Bath, vol. 48(2), pages 189-204, May.
    11. Chow, Joseph Y.J. & Regan, Amelia C., 2011. "Network-based real option models," Transportation Research Part B: Methodological, Elsevier, vol. 45(4), pages 682-695, May.
    12. Gerard Jong & Andrew Daly & Marits Pieters & Stephen Miller & Ronald Plasmeijer & Frank Hofman, 2007. "Uncertainty in traffic forecasts: literature review and new results for The Netherlands," Transportation, Springer, vol. 34(4), pages 375-395, July.
    13. Zhang, Chao & Chen, Xiaojun & Sumalee, Agachai, 2011. "Robust Wardrop's user equilibrium assignment under stochastic demand and supply: Expected residual minimization approach," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 534-552, March.
    14. Xu, Xiangdong & Chen, Anthony & Cheng, Lin & Yang, Chao, 2017. "A link-based mean-excess traffic equilibrium model under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 53-75.
    15. Manzo, Stefano & Nielsen, Otto Anker & Prato, Carlo Giacomo, 2015. "How uncertainty in input and parameters influences transport model :output A four-stage model case-study," Transport Policy, Elsevier, vol. 38(C), pages 64-72.
    16. Chen, Anthony & Zhou, Zhong, 2010. "The [alpha]-reliable mean-excess traffic equilibrium model with stochastic travel times," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 493-513, May.
    17. Börjesson, Maria & Jonsson, R. Daniel & Berglund, Svante & Almström, Peter, 2014. "Land-use impacts in transport appraisal," Research in Transportation Economics, Elsevier, vol. 47(C), pages 82-91.
    18. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
    19. Yang, Chao & Chen, Anthony & Xu, Xiangdong & Wong, S.C., 2013. "Sensitivity-based uncertainty analysis of a combined travel demand model," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 225-244.
    20. Hironori Kato & Yuichiro Kaneko & Masashi Inoue, 2010. "Comparative analysis of transit assignment: evidence from urban railway system in the Tokyo Metropolitan Area," Transportation, Springer, vol. 37(5), pages 775-799, September.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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