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An algorithm for the stochastic user equilibrium problem

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  • Damberg, Olof
  • Lundgren, Jan T.
  • Patriksson, Michael

Abstract

In this paper we present a new algorithm for the approximate solution of the logit-based stochastic user equilibrium problem. The main advantage of this algorithm is that it provides route flows explicitly, of particular interest in the evaluation of route guidance and information systems; in previously proposed methods for the stochastic user equilibrium problem, only link flows are provided. The proposed algorithm alternates between two main phases. In the subproblem phase, profitable routes are generated. In the restricted master problem phase, a descent method is used to solve the restriction to the original problem to the subset of the total set of routes generated so far. We present and evaluate alternative strategies for generating routes algorithmically, and discuss the possibility of utilizing such strategies for reducing the inherent problem of overlapping route flows in logit-based traffic models.

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  • 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.
    • Handle: RePEc:eee:transb:v:30:y:1996:i:2:p:115-131
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    References listed on IDEAS

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    Cited by:

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    3. Guido Gentile, 2018. "New Formulations of the Stochastic User Equilibrium with Logit Route Choice as an Extension of the Deterministic Model," Service Science, INFORMS, vol. 52(6), pages 1531-1547, December.
    4. Chen, Huey-Kuo & Feng, Ginny, 2000. "Heuristics for the stochastic/dynamic user-optimal route choice problem," European Journal of Operational Research, Elsevier, vol. 126(1), pages 13-30, October.
    5. Garcia-Rodenas, Ricardo & Verastegui-Rayo, Doroteo, 2008. "A column generation algorithm for the estimation of origin-destination matrices in congested traffic networks," European Journal of Operational Research, Elsevier, vol. 184(3), pages 860-878, February.
    6. 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.
    7. Seungkyu Ryu & Anthony Chen & Xiangdong Xu & Keechoo Choi, 2014. "A Dual Approach for Solving the Combined Distribution and Assignment Problem with Link Capacity Constraints," Networks and Spatial Economics, Springer, vol. 14(2), pages 245-270, June.
    8. Jian Wang & Muqing Du & Lili Lu & Xiaozheng He, 2018. "Maximizing Network Throughput under Stochastic User Equilibrium with Elastic Demand," Networks and Spatial Economics, Springer, vol. 18(1), pages 115-143, March.
    9. Chen, Anthony & Pravinvongvuth, Surachet & Xu, Xiangdong & Ryu, Seungkyu & Chootinan, Piya, 2012. "Examining the scaling effect and overlapping problem in logit-based stochastic user equilibrium models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1343-1358.
    10. Huang, Hai-Jun & Li, Zhi-Chun, 2007. "A multiclass, multicriteria logit-based traffic equilibrium assignment model under ATIS," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1464-1477, February.
    11. Huang, Hai-Jun & Bell, Michael G. H., 1998. "A study on logit assignment which excludes all cyclic flows," Transportation Research Part B: Methodological, Elsevier, vol. 32(6), pages 401-412, August.
    12. 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.
    13. 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.
    14. Rasmussen, Thomas Kjær & Watling, David Paul & Prato, Carlo Giacomo & Nielsen, Otto Anker, 2015. "Stochastic user equilibrium with equilibrated choice sets: Part II – Solving the restricted SUE for the logit family," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 146-165.
    15. Wang, Guangchao & Chen, Anthony & Kitthamkesorn, Songyot & Ryu, Seungkyu & Qi, Hang & Song, Ziqi & Song, Jianguo, 2020. "A multi-modal network equilibrium model with captive mode choice and path size logit route choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 293-317.
    16. Du, Muqing & Tan, Heqing & Chen, Anthony, 2021. "A faster path-based algorithm with Barzilai-Borwein step size for solving stochastic traffic equilibrium models," European Journal of Operational Research, Elsevier, vol. 290(3), pages 982-999.
    17. Ghatee, Mehdi & Hashemi, S. Mehdi, 2009. "Traffic assignment model with fuzzy level of travel demand: An efficient algorithm based on quasi-Logit formulas," European Journal of Operational Research, Elsevier, vol. 194(2), pages 432-451, April.
    18. Ampol Karoonsoontawong & Dung-Ying Lin, 2015. "Combined Gravity Model Trip Distribution and Paired Combinatorial Logit Stochastic User Equilibrium Problem," Networks and Spatial Economics, Springer, vol. 15(4), pages 1011-1048, December.
    19. Li, Xinyan & Xie, Chi & Bao, Zhaoyao, 2022. "A multimodal multicommodity network equilibrium model with service capacity and bottleneck congestion for China-Europe containerized freight flows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).

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