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Auction-Based Congestion Pricing

Author

Listed:
  • Dušan Teodorović
  • Konstantinos Triantis
  • Praveen Edara
  • Yueqin Zhao
  • Snežana Mladenović

Abstract

Planners, engineers and economists have introduced various demand management methods in an attempt to reduce the fast growing traffic congestion. The basic idea behind various demand management strategies is to force drivers to travel and use transportation facilities more during off-peak hours and less during peak hours, as well as to increase the usage of underutilized routes. In this paper, a new demand management concept -- Auction-based Congestion Pricing -- is proposed and modeled.

Suggested Citation

  • Dušan Teodorović & Konstantinos Triantis & Praveen Edara & Yueqin Zhao & Snežana Mladenović, 2008. "Auction-Based Congestion Pricing," Transportation Planning and Technology, Taylor & Francis Journals, vol. 31(4), pages 399-416, March.
    • Handle: RePEc:taf:transp:v:31:y:2008:i:4:p:399-416
    DOI: 10.1080/03081060802335042
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    References listed on IDEAS

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    1. Sven de Vries & Rakesh V. Vohra, 2003. "Combinatorial Auctions: A Survey," INFORMS Journal on Computing, INFORMS, vol. 15(3), pages 284-309, August.
    2. Vikrey W., 1994. "Statement to the Joint Committee on Washington, DC, Metropolitan Problems (with a foreword by Richard Arnott and Marvin Kraus)," Journal of Urban Economics, Elsevier, vol. 36(1), pages 42-65, July.
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    Cited by:

    1. Olarte, Rafael & Haghani, Ali, 2018. "Introducing and testing a game-theoretic model for a lottery-based metering system in Minneapolis, United States," Transport Policy, Elsevier, vol. 62(C), pages 63-78.
    2. Nie, Yu (Marco) & Yin, Yafeng, 2013. "Managing rush hour travel choices with tradable credit scheme," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 1-19.
    3. Wada, Kentaro & Akamatsu, Takashi, 2013. "A hybrid implementation mechanism of tradable network permits system which obviates path enumeration: An auction mechanism with day-to-day capacity control," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 60(C), pages 94-112.
    4. Fan, Wenbo & Xiao, Feng & Nie, Yu (Macro), 2022. "Managing bottleneck congestion with tradable credits under asymmetric transaction cost," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    5. Sabounchi, Nasim S. & Triantis, Konstantinos P. & Sarangi, Sudipta & Liu, Shiyong, 2014. "Dynamic simulation modeling and policy analysis of an area-based congestion pricing scheme for a transportation socioeconomic system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 357-383.
    6. Han, Linghui & Zhu, Chengjuan & Wang, David Z.W. & Sun, Huijun & Tan, Zhijia & Meng, Meng, 2019. "Discrete-time dynamic road congestion pricing under stochastic user optimal principle," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 24-36.
    7. Yang, Hai & Wang, Xiaolei, 2011. "Managing network mobility with tradable credits," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 580-594, March.
    8. Yang, Kaidi & Roca-Riu, Mireia & Menéndez, Mónica, 2019. "An auction-based approach for prebooked urban logistics facilities," Omega, Elsevier, vol. 89(C), pages 193-211.
    9. Ren-Yong Guo & Hai Yang & Hai-Jun Huang & Zhijia Tan, 2016. "Day-to-Day Flow Dynamics and Congestion Control," Transportation Science, INFORMS, vol. 50(3), pages 982-997, August.

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