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A variational inequality formulation for inferring dynamic origin-destination travel demands

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  • Nie, Yu (Marco)
  • Zhang, H.M.

Abstract

In this paper, we develop a relaxation strategy to the dynamic O-D estimation problem (DODE) problem. Cast as a variational inequality (VI), the DODE problem endogenizes the determination of the dynamic path-link incidence relationship (i.e., the dynamic assignment matrix) and takes users' response to traffic congestion into account. In our formulation, traffic dynamics on road links can be modeled by the Lighthill, Whitham and Richards theory, a delay-function model, or a point-queue model, coupled with CTM-like flow distribution models at nodes. Which model to use depends, of course, on specific modeling situations. Different from numerous previous studies, our formulation avoids the bi-level structure that poses analytical and numerical difficulties. This is achieved by balancing the path cost and the path deviation (the latter measures the difference between estimated and measured traffic conditions), weighed by a dispersion parameter which determines the extent to which users' behavior is respected. We prove the equivalence between the VI problem and the derived dynamic DODE optimality conditions, and establish the conditions under which a solution to the VI problem exists. A column generation algorithm is proposed to solve the VI problem. Numerical results based on synthetic data are also presented.

Suggested Citation

  • Nie, Yu (Marco) & Zhang, H.M., 2008. "A variational inequality formulation for inferring dynamic origin-destination travel demands," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 635-662, August.
  • Handle: RePEc:eee:transb:v:42:y:2008:i:7-8:p:635-662
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    Cited by:

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    2. Ge, Qian & Han, Ke & Liu, Xiaobo, 2021. "Matching and routing for shared autonomous vehicles in congestible network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    3. Shen, Wei & Wynter, Laura, 2012. "A new one-level convex optimization approach for estimating origin–destination demand," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1535-1555.
    4. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    5. Ge, Qian & Fukuda, Daisuke, 2019. "A macroscopic dynamic network loading model for multiple-reservoir system," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 502-527.
    6. Ali Najmi & Taha H. Rashidi & James Vaughan & Eric J. Miller, 2020. "Calibration of large-scale transport planning models: a structured approach," Transportation, Springer, vol. 47(4), pages 1867-1905, August.
    7. Friesz, Terry L. & Han, Ke & Neto, Pedro A. & Meimand, Amir & Yao, Tao, 2013. "Dynamic user equilibrium based on a hydrodynamic model," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 102-126.
    8. Friesz, Terry L. & Kim, Taeil & Kwon, Changhyun & Rigdon, Matthew A., 2011. "Approximate network loading and dual-time-scale dynamic user equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 176-207, January.
    9. Chi Xie & Jennifer Duthie, 2015. "An Excess-Demand Dynamic Traffic Assignment Approach for Inferring Origin-Destination Trip Matrices," Networks and Spatial Economics, Springer, vol. 15(4), pages 947-979, December.

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