Supply-demand Diagrams and a New Framework for Analyzing the Inhomogeneous Lighthill-Whitham-Richards Model

  • W.L. Jin
  • L. Chen
  • Elbridge Gerry Puckett


Traditionally, the Lighthill-Whitham-Richards (LWR) models for homogeneous and inhomogeneous roads have been analyzed in flux-density space with the fundamental diagram of the flux-density relation. In this paper, we present a new framework for analyzing the LWR model, especially the Riemann problem at a linear boundary in which the upstream and downstream links are homogeneous and initially carry uniform traffic. We first review the definitions of local supply and demand functions and then introduce the so-called supply-demand diagram, on which a traffic state can be represented by its supply and demand, rather than as density and flux as on a fundamental diagram. It is well-known that the solutions to the Riemann problem at each link are self-similar with a stationary state, and that the wave on the link is determined by the stationary state and the initial state. In our new framework, there can also exist an interior state next to the linear boundary on each link, which takes infinitesimal space, and admissible conditions for the upstream and downstream stationary and interior states can be derived in supply-demand space. With an entropy condition consistent with a local supply-demand method in interior states, we show that the stationary states exist and are unique within the solution framework. We also develop a graphical scheme for solving the Riemann problem, and the results are shown to be consistent with those in the literature. We further discuss asymptotic stationary states on an inhomogeneous ring road with arbitrary initial conditions and demonstrate the existence of interior states with a numerical example. The framework developed in this study is simpler than existing ones and can be extended for analyzing the traffic dynamics in general road networks.


Rarefaction Wave Riemann Problem Entropy Condition Ring Road Transportation Research Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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The work of the first two authors was supported in part by the National Natural Science Foundation of China (No. 50708107), the Hi-Tech Research and Development Program of China (863 Project) (No. 2007AA11Z222), and the National Basic Research Program of China (973 Project) (No. 2006CB705506). The work of the third author was supported in part by the US Department of Energy (DOE) Mathematical, Information, and Computing Sciences Division under contract number DE-FG02-03ER25579.


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Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  • W.L. Jin
    • 1
  • L. Chen
    • 2
  • Elbridge Gerry Puckett
    • 1
  1. 1.University of CaliforniaCaliforniaU.S.A
  2. 2.University of Science and Technology of ChinaHong-KongChina

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