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Wardrop Equilibria with Risk-Averse Users

Author

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  • Fernando Ordóñez

    (Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California 90089)

  • Nicolás E. Stier-Moses

    (Graduate School of Business, Columbia University, New York, New York 10027)

Abstract

Network games can be used to model competitive situations in which agents select routes to minimize their cost. Common applications include traffic, telecommunication, and distribution networks. Although traditional network models have assumed that realized costs only depend on congestion, in most applications they also have an uncertain component. We extend the traffic assignment problem first proposed by Wardrop in 1952 by adding random deviations, which are independent of the flow, to the cost functions that model congestion in each arc. We map these uncertainties into a Wardrop equilibrium model with nonadditive path costs. The cost on a path is given by the sum of the congestion on its arcs plus a constant safety margin that represents the agents' risk aversion. First, we prove that an equilibrium for this game always exists and is essentially unique. Then, we introduce three specific equilibrium models that fall within this framework: the percentile equilibrium where agents select paths that minimize a specified percentile of the uncertain cost; the added-variability equilibrium where agents add a multiple of the variability of the cost of each arc to the expected cost; and the robust equilibrium where agents select paths by solving a robust optimization problem that imposes a limit on the number of arcs that can deviate from the mean. The percentile equilibrium is difficult to compute because minimizing a percentile among all paths is computationally hard. Instead, the added-variability and robust Wardrop equilibria can be computed efficiently in practice: The former reduces to a standard Wardrop equilibrium problem and the latter is found using a column generation approach that repeatedly solves robust shortest path problems, which are polynomially solvable. Through computational experiments of some random and some realistic instances, we explore the benefits and trade-offs of the proposed solution concepts. We show that when agents are risk averse, both the robust and added-variability equilibria better approximate percentile equilibria than the classic Wardrop equilibrium.

Suggested Citation

  • Fernando Ordóñez & Nicolás E. Stier-Moses, 2010. "Wardrop Equilibria with Risk-Averse Users," Transportation Science, INFORMS, vol. 44(1), pages 63-86, February.
  • Handle: RePEc:inm:ortrsc:v:44:y:2010:i:1:p:63-86
    DOI: 10.1287/trsc.1090.0292
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    References listed on IDEAS

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    Cited by:

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    2. Prakash, A. Arun & Seshadri, Ravi & Srinivasan, Karthik K., 2018. "A consistent reliability-based user-equilibrium problem with risk-averse users and endogenous travel time correlations: Formulation and solution algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 171-198.
    3. Thanasis Lianeas & Evdokia Nikolova & Nicolas E. Stier-Moses, 2019. "Risk-Averse Selfish Routing," Mathematics of Operations Research, INFORMS, vol. 44(1), pages 38-57, February.
    4. Correa, José & Hoeksma, Ruben & Schröder, Marc, 2019. "Network congestion games are robust to variable demand," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 69-78.
    5. E. Nikolova & N. E. Stier-Moses, 2014. "A Mean-Risk Model for the Traffic Assignment Problem with Stochastic Travel Times," Operations Research, INFORMS, vol. 62(2), pages 366-382, April.
    6. Leilei Zhang & Tito Homem-de-Mello, 2017. "An Optimal Path Model for the Risk-Averse Traveler," Transportation Science, INFORMS, vol. 51(2), pages 518-535, May.
    7. Markus Lang & Marc Laperrouza & Matthias Finger, 2013. "Competition Effects in a Liberalized Railway Market," Journal of Industry, Competition and Trade, Springer, vol. 13(3), pages 375-398, September.
    8. Zhaoqi Zang & Xiangdong Xu & Kai Qu & Ruiya Chen & Anthony Chen, 2022. "Travel time reliability in transportation networks: A review of methodological developments," Papers 2206.12696, arXiv.org, revised Jul 2022.
    9. Qi, Jin & Sim, Melvyn & Sun, Defeng & Yuan, Xiaoming, 2016. "Preferences for travel time under risk and ambiguity: Implications in path selection and network equilibrium," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 264-284.
    10. Ahipaşaoğlu, Selin Damla & Meskarian, Rudabeh & Magnanti, Thomas L. & Natarajan, Karthik, 2015. "Beyond normality: A cross moment-stochastic user equilibrium model," Transportation Research Part B: Methodological, Elsevier, vol. 81(P2), pages 333-354.
    11. Selin Damla Ahipaşaoğlu & Uğur Arıkan & Karthik Natarajan, 2019. "Distributionally Robust Markovian Traffic Equilibrium," Transportation Science, INFORMS, vol. 53(6), pages 1546-1562, November.
    12. Zhu, Zheng & Mardan, Atabak & Zhu, Shanjiang & Yang, Hai, 2021. "Capturing the interaction between travel time reliability and route choice behavior based on the generalized Bayesian traffic model," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 48-64.
    13. Thomas Gstraunthaler & Galina Sagieva, 2011. "The Internationalization of Venture Capital: Challenges and Opportunities," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 5(4), pages 66-76.
    14. Roberto Cominetti & Alfredo Torrico, 2016. "Additive Consistency of Risk Measures and Its Application to Risk-Averse Routing in Networks," Mathematics of Operations Research, INFORMS, vol. 41(4), pages 1510-1521, November.
    15. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    16. Nie, Yu (Marco), 2011. "Multi-class percentile user equilibrium with flow-dependent stochasticity," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1641-1659.

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