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Massively Parallel Computation of Dynamic Traffic Problems Modeled as Projected Dynamical Systems

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  • Anna Nagurney

    (Department of Finance and Operations Management, University of Massachusetts)

  • Ding Zhang

    (Department of Industrial Engineering and Operations Research, University of Massachusetts)

Abstract

Traffic congestion in the United States alone results in $n100 billion in lost productivity. In this paper we consider the modeling and solution of dynamic traffic models formulated as projected dynamical systems. The proposed discrete time algorithm, the Euler method, resolves the problem at each step into subproblems in path flow variables, all of which can be solved simultaneously and in closed form. Convergence results are also presented. Finally, the algorithm is implemented on the massively parallel architecture, the Thinking Machine's CM-5, and its performance compared to an implementation on the IBM SP2 on several traffic network examples.

Suggested Citation

  • Anna Nagurney & Ding Zhang, "undated". "Massively Parallel Computation of Dynamic Traffic Problems Modeled as Projected Dynamical Systems," Computing in Economics and Finance 1996 _039, Society for Computational Economics.
    • Handle: RePEc:sce:scecf6:_039
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    References listed on IDEAS

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    5. Michael J. Smith, 1984. "The Stability of a Dynamic Model of Traffic Assignment---An Application of a Method of Lyapunov," Transportation Science, INFORMS, vol. 18(3), pages 245-252, August.
    6. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1.
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    Cited by:

    1. Jurgen A. Doornik & Neil Shephard & David F. Hendry, 2004. "Parallel Computation in Econometrics: A Simplified Approach," Economics Papers 2004-W16, Economics Group, Nuffield College, University of Oxford.
    2. Morozov, Sergei & Mathur, Sudhanshu, 2009. "Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control," MPRA Paper 30298, University Library of Munich, Germany, revised 04 Apr 2011.
    3. Sergei Morozov & Sudhanshu Mathur, 2012. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 151-182, August.
    4. Lilia Maliar, 2015. "Assessing gains from parallel computation on a supercomputer," Economics Bulletin, AccessEcon, vol. 35(1), pages 159-167.
    5. Mathur, Sudhanshu & Morozov, Sergei, 2009. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," MPRA Paper 16721, University Library of Munich, Germany.

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