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Solving multi-objective traffic assignment

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  • Andrea Raith
  • Judith Wang
  • Matthias Ehrgott
  • Stuart Mitchell

Abstract

Traffic assignment is a key component in transport planning models. It models travel behaviour in terms of route choice. This is essential to accurately forecast travel demand and most importantly to enable the correct assessment of the benefits of changes in transport policies and infrastructure developments. The route choice of travellers may be influenced by multiple objectives, for example travel time but also travel associated toll costs. Here, travellers may avoid a fast route because of toll costs associated with it. We explicitly distinguish those functions as separate route choice objectives. This leads to the concept of multi-objective traffic assignment (MTA). We discuss the concept of MTA, and develop heuristic solution methods to obtain equilibrium solutions of MTA and present some computational results. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Andrea Raith & Judith Wang & Matthias Ehrgott & Stuart Mitchell, 2014. "Solving multi-objective traffic assignment," Annals of Operations Research, Springer, vol. 222(1), pages 483-516, November.
    • Handle: RePEc:spr:annopr:v:222:y:2014:i:1:p:483-516:10.1007/s10479-012-1284-1
    DOI: 10.1007/s10479-012-1284-1
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    References listed on IDEAS

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    Cited by:

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    2. Svetla Stoilova, 2020. "An Integrated Multi-Criteria and Multi-Objective Optimization Approach for Establishing the Transport Plan of Intercity Trains," Sustainability, MDPI, vol. 12(2), pages 1-24, January.
    3. Chi Xie & Xing Wu & Stephen Boyles, 2019. "Traffic equilibrium with a continuously distributed bound on travel weights: the rise of range anxiety and mental account," Annals of Operations Research, Springer, vol. 273(1), pages 279-310, February.
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    5. Hongli Xu & Hai Yang & Jing Zhou & Yafeng Yin, 2017. "A Route Choice Model with Context-Dependent Value of Time," Transportation Science, INFORMS, vol. 51(2), pages 536-548, May.

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