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An Iterative Algorithm to Determine the Dynamic User Equilibrium in a Traffic Simulation Model

Author

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  • C. Gawron

    (Center for Parallel Computing, Cologne University, D-50931 Köln, Germany)

Abstract

An iterative algorithm to determine the dynamic user equilibrium with respect to link costs defined by a traffic simulation model is presented. Each driver's route choice is modeled by a discrete probability distribution which is used to select a route in the simulation. After each simulation run, the probability distribution is adapted to minimize the travel costs. Although the algorithm does not depend on the simulation model, a queuing model is used for performance reasons. The stability of the algorithm is analyzed for a simple example network. As an application example, a dynamic version of Braess's paradox is studied.

Suggested Citation

  • C. Gawron, 1998. "An Iterative Algorithm to Determine the Dynamic User Equilibrium in a Traffic Simulation Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 393-407.
    • Handle: RePEc:wsi:ijmpcx:v:09:y:1998:i:03:n:s0129183198000303
    DOI: 10.1142/S0129183198000303
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    Citations

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    Cited by:

    1. Ihab Kaddoura & Kai Nagel, 2018. "Simultaneous internalization of traffic congestion and noise exposure costs," Transportation, Springer, vol. 45(5), pages 1579-1600, September.
    2. Kevin D. Henry & Nathan J. Wood & Tim G. Frazier, 2017. "Influence of road network and population demand assumptions in evacuation modeling for distant tsunamis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(3), pages 1665-1687, February.
    3. Albert Solé-Ribalta & Sergio Gómez & Alex Arenas, 2018. "Decongestion of Urban Areas with Hotspot Pricing," Networks and Spatial Economics, Springer, vol. 18(1), pages 33-50, March.
    4. Ihab Kaddoura & Benjamin Kickhöfer & Andreas Neumann & Alejandro Tirachini, 2015. "Agent-based optimisation of public transport supply and pricing: impacts of activity scheduling decisions and simulation randomness," Transportation, Springer, vol. 42(6), pages 1039-1061, November.
    5. Chiu, Yi-Chang & Zhou, Liang & Song, Houbing, 2010. "Development and calibration of the Anisotropic Mesoscopic Simulation model for uninterrupted flow facilities," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 152-174, January.
    6. Kaddoura, Ihab & Nagel, Kai, 2019. "Congestion pricing in a real-world oriented agent-based simulation context," Research in Transportation Economics, Elsevier, vol. 74(C), pages 40-51.
    7. Agarwal, Amit & Ziemke, Dominik & Nagel, Kai, 2020. "Bicycle superhighway: An environmentally sustainable policy for urban transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 519-540.
    8. Benjamin Kickhöfer & Friederike Hülsmann & Regine Gerike & Kai Nagel, 2013. "Rising car user costs: comparing aggregated and geo-spatial impacts on travel demand and air pollutant emissions," Chapters, in: Thomas Vanoutrive & Ann Verhetsel (ed.), Smart Transport Networks, chapter 9, pages 180-207, Edward Elgar Publishing.
    9. Mads Paulsen & Thomas Kjær Rasmussen & Otto Anker Nielsen, 2022. "Including Right-of-Way in a Joint Large-Scale Agent-Based Dynamic Traffic Assignment Model for Cars and Bicycles," Networks and Spatial Economics, Springer, vol. 22(4), pages 915-957, December.
    10. Kai Nagel & Peter Wagner & Richard Woesler, 2003. "Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling," Operations Research, INFORMS, vol. 51(5), pages 681-710, October.

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