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Macroscopic Models

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Traffic Flow Modelling

Part of the book series: EURO Advanced Tutorials on Operational Research ((EUROATOR))

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Abstract

Macroscopic traffic flow models forms arguably the largest family in the model tree, see page 15. They describe traffic flow as if it were a continuum flow and are often compared to, or derived in analogy with, continuum models for fluids. Individual vehicles are not modeled, however aggregated variables such as (average) density and (average) flow are used.

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Notes

  1. 1.

    This assumption is related to the continuum assumption, stating that the flow can be described as if it were a continuum instead of individual particles. The validity of this assumption is discussed in more detail in Sect. 7.1.1.

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Further Reading

  • Aw A, Klar A, Rascle M, Materne T (2002) Derivation of continuum traffic flow models from microscopic follow-the-leader models. SIAM J Appl Math 63(1):259–278

    Google Scholar 

  • Lebacque JP, Mammar S, Haj Salem H (2007) Generic second order traffic flow modelling. In: Allsop RE, Bell MGH, Heydecker BG (eds) Transportation and traffic theory 2007. Elsevier, Oxford, pp 755–776

    Google Scholar 

  • Leclercq L, Laval J, Chevallier E (2007) The Lagrangian coordinates and what it means for first order traffic flow models. In: Allsop RE, Bell MGH, Heydecker BG (eds) Transportation and traffic theory 2007. Elsevier, Oxford, pp 735–753

    Google Scholar 

  • Van Wageningen-Kessels FLM (2016) Framework to assess multi-class continuum traffic flow models. Transp Res Rec J Transp Res Board 2553:150–160

    Google Scholar 

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Authors and Affiliations

  1. Department of Transport & Planning, Delft University of Technology, Delft, The Netherlands

    Femke Kessels

  2. Logistics, Tourism and Service Management, German University of Technology, Muscat, Sultanate of Oman

    Femke Kessels

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  1. Femke Kessels
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Kessels, F. (2019). Macroscopic Models. In: Traffic Flow Modelling. EURO Advanced Tutorials on Operational Research. Springer, Cham. https://doi.org/10.1007/978-3-319-78695-7_4

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