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Dynamic model of peak period congestion


  • Ben-Akiva, Moshe
  • Cyna, Michèle
  • de Palma, André


This paper examines the problems of peak period traffic congestion and the analysis of alternative congestion relief methods. It presents a dynamic model of the queues and delays at a single point of traffic congestion because there is ample evidence to suggest that the major delays to users occur at bottlenecks. The model consists of a deterministic queueing model and a model of arrival rate as a function of travel time and schedule delay. A dynamic simulation model also describes the evolution of queues from day to day. The model is used to study the impacts of changes in capacity, total demand, flexibility of work start time and traffic control. Among the numerical results is a demonstration that additional capacity always significantly reduces the duration of the congestion period, but may result in a less significant improvement in maximum delays.

Suggested Citation

  • Ben-Akiva, Moshe & Cyna, Michèle & de Palma, André, 1984. "Dynamic model of peak period congestion," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 339-355.
  • Handle: RePEc:eee:transb:v:18:y:1984:i:4-5:p:339-355

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    References listed on IDEAS

    1. S. Selvanathan, 1987. "Do OECD Consumers Obey Demand Theory?," Economics Discussion / Working Papers 87-04, The University of Western Australia, Department of Economics.
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    Cited by:

    1. Wuping Xin & David Levinson, 2015. "Stochastic Congestion and Pricing Model with Endogenous Departure Time Selection and Heterogeneous Travelers," Mathematical Population Studies, Taylor & Francis Journals, vol. 22(1), pages 37-52, March.
    2. Daniel, Joseph I, 1995. "Congestion Pricing and Capacity of Large Hub Airports: A Bottleneck Model with Stochastic Queues," Econometrica, Econometric Society, vol. 63(2), pages 327-370, March.
    3. Small, Kenneth A., 2015. "The bottleneck model: An assessment and interpretation," Economics of Transportation, Elsevier, vol. 4(1), pages 110-117.
    4. Zhang, Xiaoning & Yang, Hai & Huang, Hai-Jun & Zhang, H. Michael, 2005. "Integrated scheduling of daily work activities and morning-evening commutes with bottleneck congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(1), pages 41-60, January.
    5. A. de Palma & F. Marchal, 2000. "Dynamic traffic analysis with static data: some guidelines with an application to Paris," THEMA Working Papers 2000-55, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    6. Gonzales, Eric J., 2015. "Coordinated pricing for cars and transit in cities with hypercongestion," Economics of Transportation, Elsevier, vol. 4(1), pages 64-81.
    7. Ran, Bin & Hall, Randolph W. & Boyce, David E., 1996. "A link-based variational inequality model for dynamic departure time/route choice," Transportation Research Part B: Methodological, Elsevier, vol. 30(1), pages 31-46, February.
    8. Canca, David & Zarzo, Alejandro & Algaba, Encarnación & Barrena, Eva, 2013. "Macroscopic attraction-based simulation of pedestrian mobility: A dynamic individual route-choice approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 428-442.
    9. Richard J. Arnott & Anatolii Kokoza & Mehdi Naji, 2015. "A Model of Rush-Hour Traffic in an Isotropic Downtown Area," CESifo Working Paper Series 5465, CESifo Group Munich.
    10. Kenneth Small, 2015. "The Bottleneck Model: An Assessment and Interpretation," Working Papers 141506, University of California-Irvine, Department of Economics.
    11. Donald K. Richter & John Griffin & Richard Arnott, 1990. "Computation of Dynamic User Equilibria in a Model of Peak Period Traffic Congestion with Heterogenous Commuters," Boston College Working Papers in Economics 198, Boston College Department of Economics.

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