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


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  • 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)

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    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.

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    Bibliographic Info

    Article provided by Springer in its journal The Annals of Regional Science.

    Volume (Year): 36 (2002)
    Issue (Month): 1 ()
    Pages: 145-163

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    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. 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.
    2. 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.
    3. Maria Börjesson & Jonas Eliasson & Mattias Lundberg, 2014. "Is CBA Ranking of Transport Investments Robust?," Journal of Transport Economics and Policy, London School of Economics and University of Bath, vol. 48(2), pages 189-204, May.
    4. 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.
    5. Pavithra Parthasarathi & David Levinson, 2008. "Post-Construction Evaluation of Traffic Forecast Accuracy," Working Papers 201005, University of Minnesota: Nexus Research Group.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.


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