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Modeling space–time inhomogeneities with the kinematic wave theory

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  • Li, Jia
  • Zhang, H.M.

Abstract

In this paper, we are concerned with modeling space–time inhomogeneities with the kinematic wave (LWR) model. The notion of space–time inhomogeneity refers to the fact that governing laws of traffic, essentially dictated by fundamental diagrams (FD), differ from each other in distinct space–time regions. Such a scenario is common when exogenous inputs, e.g. a group of slowly moving vehicles, emerge in the modeling process. We will prove the well-posedness of this class of problems. More importantly, we show that if the boundary delineating two neighboring regions is continuous and has bounded speed, this problem can be greatly simplified by introducing a piecewise linear approximation to the boundary. In particular, we utilize the variational formulation of the kinematic wave model and prove that this approximation results in uniformly bounded errors in cumulative flow N which are proportional to the L∞ deviation of the approximation. The numerical solution of this simplified problem is well understood, and this result means that a kinematic wave model with space–time inhomogeneity can be solved accurately with any existing Godunov type scheme. Finally, using the inhomogeneous LWR model, we explain the capacity drop as a natural result of space–time inhomogeneity.

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  • Li, Jia & Zhang, H.M., 2013. "Modeling space–time inhomogeneities with the kinematic wave theory," Transportation Research Part B: Methodological, Elsevier, vol. 54(C), pages 113-125.
    • Handle: RePEc:eee:transb:v:54:y:2013:i:c:p:113-125
    DOI: 10.1016/j.trb.2013.03.005
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    References listed on IDEAS

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    Cited by:

    1. Wang, Jiawen & Zou, Linzhi & Zhao, Jing & Wang, Xinwei, 2024. "Dynamic capacity drop propagation in incident-affected networks: Traffic state modeling with SIS-CTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    2. Jin, Wen-Long, 2017. "A first-order behavioral model of capacity drop," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 438-457.
    3. (Sean) Qian, Zhen & Li, Jia & Li, Xiaopeng & Zhang, Michael & Wang, Haizhong, 2017. "Modeling heterogeneous traffic flow: A pragmatic approach," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 183-204.
    4. Laval, Jorge A. & Costeseque, Guillaume & Chilukuri, Bargavarama, 2016. "The impact of source terms in the variational representation of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 204-216.
    5. Jin, Wen-Long & Gan, Qi-Jian & Lebacque, Jean-Patrick, 2015. "A kinematic wave theory of capacity drop," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 316-329.

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