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Genetics of traffic assignment models for strategic transport planning

Author

Listed:
  • Michiel C. J. Bliemer
  • Mark P. H. Raadsen
  • Luuk J. N. Brederode
  • Michael G. H. Bell
  • Luc J. J. Wismans
  • Mike J. Smith

Abstract

This paper presents a review and classification of traffic assignment models for strategic transport planning purposes by using concepts analogous to genetics in biology. Traffic assignment models share the same theoretical framework (DNA), but differ in capability (genes). We argue that all traffic assignment models can be described by three genes. The first gene determines the spatial capability (unrestricted, capacity restrained, capacity constrained, and capacity and storage constrained) described by four spatial assumptions (shape of the fundamental diagram, capacity constraints, storage constraints, and turn flow restrictions). The second gene determines the temporal capability (static, semi-dynamic, and dynamic) described by three temporal assumptions (wave speeds, vehicle propagation speeds, and residual traffic transfer). The third gene determines the behavioural capability (all-or-nothing, one shot, and equilibrium) described by two behavioural assumptions (decision-making and travel time consideration). This classification provides a deeper understanding of the often implicit assumptions made in traffic assignment models described in the literature. It further allows for comparing different models in terms of functionality, and paves the way for developing novel traffic assignment models.

Suggested Citation

  • Michiel C. J. Bliemer & Mark P. H. Raadsen & Luuk J. N. Brederode & Michael G. H. Bell & Luc J. J. Wismans & Mike J. Smith, 2017. "Genetics of traffic assignment models for strategic transport planning," Transport Reviews, Taylor & Francis Journals, vol. 37(1), pages 56-78, January.
    • Handle: RePEc:taf:transr:v:37:y:2017:i:1:p:56-78
    DOI: 10.1080/01441647.2016.1207211
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    References listed on IDEAS

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    1. Guido Gentile, 2010. "The General Link Transmission Model for Dynamic Network Loading and a Comparison with the DUE Algorithm," Chapters, in: Chris M.J. Tampere & Francesco Viti & Lambertus H. (Ben) Immers (ed.), New Developments in Transport Planning, chapter 8, Edward Elgar Publishing.
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    Cited by:

    1. Zhang, Yachao & Xie, Shiwei & Shu, Shengwen, 2022. "Multi-stage robust optimization of a multi-energy coupled system considering multiple uncertainties," Energy, Elsevier, vol. 238(PC).
    2. Wei Huang & Guangming Xu & Hong K. Lo, 2020. "Pareto-Optimal Sustainable Transportation Network Design under Spatial Queuing," Networks and Spatial Economics, Springer, vol. 20(3), pages 637-673, September.
    3. Raadsen, Mark P.H. & Bliemer, Michiel C.J., 2019. "Steady-state link travel time methods: Formulation, derivation, classification, and unification," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 167-191.
    4. Brederode, Luuk & Pel, Adam & Wismans, Luc & Rijksen, Bernike & Hoogendoorn, Serge, 2023. "Travel demand matrix estimation for strategic road traffic assignment models with strict capacity constraints and residual queues," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 1-31.
    5. Raadsen, Mark P.H. & Bliemer, Michiel C.J., 2023. "General solution scheme for the static link transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 169(C), pages 108-135.
    6. Wang, Yi & Szeto, W.Y. & Han, Ke & Friesz, Terry L., 2018. "Dynamic traffic assignment: A review of the methodological advances for environmentally sustainable road transportation applications," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 370-394.
    7. Bliemer, Michiel C.J. & Raadsen, Mark P.H., 2020. "Static traffic assignment with residual queues and spillback," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 303-319.
    8. Ke Han & Gabriel Eve & Terry L. Friesz, 2019. "Computing Dynamic User Equilibria on Large-Scale Networks with Software Implementation," Networks and Spatial Economics, Springer, vol. 19(3), pages 869-902, September.
    9. 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.
    10. Zhou, Zhe & Zhang, Xuan & Guo, Qinglai & Sun, Hongbin, 2021. "Analyzing power and dynamic traffic flows in coupled power and transportation networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Babak Javani & Abbas Babazadeh, 2020. "Path-Based Dynamic User Equilibrium Model with Applications to Strategic Transportation Planning," Networks and Spatial Economics, Springer, vol. 20(2), pages 329-366, June.
    12. Raadsen, Mark P.H. & Bliemer, Michiel C.J. & Bell, Michael G.H., 2020. "Aggregation, disaggregation and decomposition methods in traffic assignment: historical perspectives and new trends," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 199-223.

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