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Dynamic traffic assignment approximating the kinematic wave model: System optimum, marginal costs, externalities and tolls

Listed author(s):
  • Carey, Malachy
  • Watling, David
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    System marginal costs, externalities and optimal congestion tolls for traffic networks are generally derived from system optimising (SO) traffic assignment models and when they are treated as varying over time they are referred to as dynamic. In dynamic system optimum (DSO) models the link flows and travel times or costs are generally modelled using so-called ‘whole link’ models. Here we instead develop an SO model that more closely reflects traffic flow theory and derive the marginal costs and externalities from that. The most widely accepted traffic flow model appears to be the LWR (Lighthill, Whitham and Richards) model and a tractable discrete implementation or approximation to that is provided by the cell transmission model (CTM) or a finite difference approximation (FDA). These handle spillbacks, traffic controls and moving queues in a way that is consistent with the LWR model and hence with the kinematic wave model and fluid flow model. An SO formulation using the CTM is already available, assuming a single destination and a trapezoidal flow-density function. We extend the formulation to allow more general nonlinear flow density functions and derive and interpret system marginal costs and externalities. We show that if tolls computed from the DSO solution are imposed on users then the DSO solution would also satisfy the criteria for a dynamic user equilibrium (DUE). We extend the analysis to allow for physical or behavioural constraints on the link outflow proportions at merges and inflow proportions at diverges. We also extend the model to elastic demands and establish connections between the present DSO model and earlier DSO models.

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    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 46 (2012)
    Issue (Month): 5 ()
    Pages: 634-648

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    Handle: RePEc:eee:transb:v:46:y:2012:i:5:p:634-648
    DOI: 10.1016/j.trb.2012.01.008
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    1. Lo, Hong K. & Szeto, W. Y., 2002. "A cell-based variational inequality formulation of the dynamic user optimal assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 36(5), pages 421-443, June.
    2. Cayford, Randall & Lin, Wei-Hua & Daganzo, Carlos F., 1997. "The Netcell Simulation Package: Technical Description," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt4j27j106, Institute of Transportation Studies, UC Berkeley.
    3. Satish Ukkusuri & S. Waller, 2008. "Linear Programming Models for the User and System Optimal Dynamic Network Design Problem: Formulations, Comparisons and Extensions," Networks and Spatial Economics, Springer, vol. 8(4), pages 383-406, December.
    4. Tong, C. O. & Wong, S. C., 2000. "A predictive dynamic traffic assignment model in congested capacity-constrained road networks," Transportation Research Part B: Methodological, Elsevier, vol. 34(8), pages 625-644, November.
    5. Daganzo, Carlos F., 1995. "The cell transmission model, part II: Network traffic," Transportation Research Part B: Methodological, Elsevier, vol. 29(2), pages 79-93, April.
    6. Boel, René & Mihaylova, Lyudmila, 2006. "A compositional stochastic model for real time freeway traffic simulation," Transportation Research Part B: Methodological, Elsevier, vol. 40(4), pages 319-334, May.
    7. Szeto, W. Y. & Lo, Hong K., 2004. "A cell-based simultaneous route and departure time choice model with elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 593-612, August.
    8. Sumalee, A. & Zhong, R.X. & Pan, T.L. & Szeto, W.Y., 2011. "Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 507-533, March.
    9. Daganzo, Carlos F., 1995. "A finite difference approximation of the kinematic wave model of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 29(4), pages 261-276, August.
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