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Unconstrained Extremal Formulation of Some Transportation Equilibrium Problems

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  • Carlos F. Daganzo

    (University of California, Berkeley, California)

Abstract

This paper presents transportation equilibrium results that apply to both discrete choice models and network problems. Specifically, it shows that many network equilibrium problems admit an unconstrained extremal formulation and that unconstrained optimization algorithms may be used for their solution. Similar results are derived for equilibrium problems involving discrete choice models. It also shows that a certain class of stochastic networks exhibit unique equilibria and that simulation algorithms with fixed step sizes converge almost surely to the equilibrium point.

Suggested Citation

  • Carlos F. Daganzo, 1982. "Unconstrained Extremal Formulation of Some Transportation Equilibrium Problems," Transportation Science, INFORMS, vol. 16(3), pages 332-360, August.
    • Handle: RePEc:inm:ortrsc:v:16:y:1982:i:3:p:332-360
    DOI: 10.1287/trsc.16.3.332
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    Cited by:

    1. Chen, Anthony & Zhou, Zhong & Lam, William H.K., 2011. "Modeling stochastic perception error in the mean-excess traffic equilibrium model," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1619-1640.
    2. Watling, David Paul & Rasmussen, Thomas Kjær & Prato, Carlo Giacomo & Nielsen, Otto Anker, 2015. "Stochastic user equilibrium with equilibrated choice sets: Part I – Model formulations under alternative distributions and restrictions," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 166-181.
    3. Oyama, Yuki & Hara, Yusuke & Akamatsu, Takashi, 2022. "Markovian traffic equilibrium assignment based on network generalized extreme value model," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 135-159.
    4. Zhou, Zhong & Chen, Anthony & Wong, S.C., 2009. "Alternative formulations of a combined trip generation, trip distribution, modal split, and trip assignment model," European Journal of Operational Research, Elsevier, vol. 198(1), pages 129-138, October.
    5. S. F. A. Batista & Ludovic Leclercq, 2019. "Regional Dynamic Traffic Assignment Framework for Macroscopic Fundamental Diagram Multi-regions Models," Transportation Science, INFORMS, vol. 53(6), pages 1563-1590, November.
    6. Yao, Jia & Chen, Anthony & Ryu, Seungkyu & Shi, Feng, 2014. "A general unconstrained optimization formulation for the combined distribution and assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 137-160.
    7. Jiang Ying, 2005. "Sensitivity Analysis Based Method for Optimal Road Network Pricing," Annals of Operations Research, Springer, vol. 133(1), pages 303-317, January.
    8. Xie, Chi & Travis Waller, S., 2012. "Stochastic traffic assignment, Lagrangian dual, and unconstrained convex optimization," Transportation Research Part B: Methodological, Elsevier, vol. 46(8), pages 1023-1042.
    9. Damberg, Olof & Lundgren, Jan T. & Patriksson, Michael, 1996. "An algorithm for the stochastic user equilibrium problem," Transportation Research Part B: Methodological, Elsevier, vol. 30(2), pages 115-131, April.
    10. Watling, David, 1996. "Asymmetric problems and stochastic process models of traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 30(5), pages 339-357, October.
    11. Bell, Michael G. H., 1995. "Stochastic user equilibrium assignment in networks with queues," Transportation Research Part B: Methodological, Elsevier, vol. 29(2), pages 125-137, April.
    12. David Watling, 2002. "A Second Order Stochastic Network Equilibrium Model, II: Solution Method and Numerical Experiments," Transportation Science, INFORMS, vol. 36(2), pages 167-183, May.

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