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Dynamic model of peak period congestion

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

Abstract

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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    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. Agostino Nuzzolo & Francesco Russo & Umberto Crisalli, 2001. "A Doubly Dynamic Schedule-based Assignment Model for Transit Networks," Transportation Science, INFORMS, vol. 35(3), pages 268-285, August.
    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. Cantelmo, Guido & Viti, Francesco, 2019. "Incorporating activity duration and scheduling utility into equilibrium-based Dynamic Traffic Assignment," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 365-390.
    6. 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.
    7. Liu, Wei & Szeto, Wai Yuen, 2020. "Learning and managing stochastic network traffic dynamics with an aggregate traffic representation," Transportation Research Part B: Methodological, Elsevier, vol. 137(C), pages 19-46.
    8. 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.
    9. Kenneth Small, 2015. "The Bottleneck Model: An Assessment and Interpretation," Working Papers 141506, University of California-Irvine, Department of Economics.
    10. 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.
    11. Ren-Yong Guo & Hai Yang & Hai-Jun Huang, 2018. "Are We Really Solving the Dynamic Traffic Equilibrium Problem with a Departure Time Choice?," Transportation Science, INFORMS, vol. 52(3), pages 603-620, June.
    12. 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.
    13. 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.
    14. Siyu Chen & Ravi Seshadri & Carlos Lima Azevedo & Arun P. Akkinepally & Renming Liu & Andrea Araldo & Yu Jiang & Moshe E. Ben-Akiva, 2021. "Market Design for Tradable Mobility Credits," Papers 2101.00669, arXiv.org, revised Sep 2022.
    15. Gonzales, Eric J., 2015. "Coordinated pricing for cars and transit in cities with hypercongestion," Economics of Transportation, Elsevier, vol. 4(1), pages 64-81.
    16. Li, Zhi-Chun & Huang, Hai-Jun & Yang, Hai, 2020. "Fifty years of the bottleneck model: A bibliometric review and future research directions," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 311-342.
    17. Guo, Ren-Yong & Yang, Hai & Huang, Hai-Jun & Li, Xinwei, 2018. "Day-to-day departure time choice under bounded rationality in the bottleneck model," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 832-849.
    18. 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.
    19. Wen-Long Jin, 2020. "Stable Day-to-Day Dynamics for Departure Time Choice," Transportation Science, INFORMS, vol. 54(1), pages 42-61, January.

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