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Road traffic congestion and public information : an experimental investigation

Author

Listed:
  • Anthony Ziegelmeyer

    (Max Planck Institute of Economics - Max-Planck-Gesellschaft)

  • Frédéric Koessler

    (THEMA - Théorie économique, modélisation et applications - UCP - Université de Cergy Pontoise - Université Paris-Seine - CNRS - Centre National de la Recherche Scientifique)

  • Kene Boun My

    (BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)

  • Laurent Denant-Boèmont

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper reports laboratory experiments designed to study the impact of public information about past departure rates on congestion levels and travel costs. Our design is based on a discrete version of Arnott et al.'s (1990) bottleneck model. In all treatments, congestion occurs and the observed travel costs are quite similar to the predicted ones. Subjects' capacity to coordinate is not affected by the availability of public information on past departure rates, by the number of drivers or by the relative cost of delay. This seemingly absence of treatment effects is confirmed by our finding that a parameter-free reinforcement learning model best characterises individual behaviour.

Suggested Citation

  • Anthony Ziegelmeyer & Frédéric Koessler & Kene Boun My & Laurent Denant-Boèmont, 2008. "Road traffic congestion and public information : an experimental investigation," Post-Print halshs-00333755, HAL.
    • Handle: RePEc:hal:journl:halshs-00333755
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    Citations

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    Cited by:

    1. Levy, Nadav & Klein, Ido & Ben-Elia, Eran, 2018. "Emergence of cooperation and a fair system optimum in road networks: A game-theoretic and agent-based modelling approach," Research in Transportation Economics, Elsevier, vol. 68(C), pages 46-55.
    2. Morgan, John & Orzen, Henrik & Sefton, Martin, 2009. "Network architecture and traffic flows: Experiments on the Pigou-Knight-Downs and Braess Paradoxes," Games and Economic Behavior, Elsevier, vol. 66(1), pages 348-372, May.
    3. Rapoport, Amnon & Stein, William E. & Mak, Vincent & Zwick, Rami & Seale, Darryl A., 2010. "Endogenous arrivals in batch queues with constant or variable capacity," Transportation Research Part B: Methodological, Elsevier, vol. 44(10), pages 1166-1185, December.
    4. de Jong, Gerard, 2012. "Application of experimental economics in transport and logistics," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 50, pages 1-3.
    5. Sun, Xiaoyan & Han, Xiao & Bao, Jian-Zhang & Jiang, Rui & Jia, Bin & Yan, Xiaoyong & Zhang, Boyu & Wang, Wen-Xu & Gao, Zi-You, 2017. "Decision dynamics of departure times: Experiments and modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 74-82.
    6. Rey, David & Dixit, Vinayak V. & Ygnace, Jean-Luc & Waller, S. Travis, 2016. "An endogenous lottery-based incentive mechanism to promote off-peak usage in congested transit systems," Transport Policy, Elsevier, vol. 46(C), pages 46-55.
    7. Laurent Denant-Boèmont & Sabrina Hammiche, 2009. "Public Transit Capacity and Users' Choice: AnExperiment on Downs-Thomson Paradox," Post-Print halshs-00406223, HAL.
    8. Hamed Alibabai & Hani S. Mahmassani, 2016. "Foxes and sheep: effect of predictive logic in day-to-day dynamics of route choice behavior," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 53-67, March.
    9. 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.
    10. Xiao Han & Yun Yu & Bin Jia & Zi‐You Gao & Rui Jiang & H. Michael Zhang, 2021. "Coordination Behavior in Mode Choice: Laboratory Study of Equilibrium Transformation and Selection," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3635-3656, October.
    11. Ramadurai, Gitakrishnan & Ukkusuri, Satish V. & Zhao, Jinye & Pang, Jong-Shi, 2010. "Linear complementarity formulation for single bottleneck model with heterogeneous commuters," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 193-214, February.
    12. Philippe Gagnepain & Sébastien Massoni & Alexandre Mayol & Carine Staropoli, 2024. "The Effect of Public Transport Pricing Policy: Experimental Evidence," Working Papers halshs-04607716, HAL.
    13. Nicholas Janusch & Stephan Kroll & Christopher Goemans & Todd L. Cherry & Steffen Kallbekken, 2021. "Learning to accept welfare-enhancing policies: an experimental investigation of congestion pricing," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 59-86, March.
    14. Vinayak V Dixit & Laurent Denant-Boemont, 2014. "Is Equilibrium in Transport Pure Nash, Mixed or Stochastic? Evidence from Laboratory Experiments," Post-Print halshs-01103472, HAL.
    15. 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.
    16. 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.
    17. Ruihua Lu & Caspar Chorus & Bert van Wee, 2014. "Travelers' Use of ICT under Conditions of Risk and Constraints: An Empirical Study Based on Stated and Induced Preferences," Environment and Planning B, , vol. 41(5), pages 928-944, October.
    18. Han, Xiao & Yang, Yong & Jiang, Rui & Gao, Zi-You & Zhang, H. Michael, 2025. "Commuting departure time choice under stochastic demand: Departure preferences and the value of information," Transportation Research Part A: Policy and Practice, Elsevier, vol. 197(C).
    19. Caspar G. Chorus & Benedict G. C. Dellaert, 2012. "Travel Choice Inertia: The Joint Role of Risk Aversion and Learning," Journal of Transport Economics and Policy, University of Bath, vol. 46(1), pages 139-155, January.
    20. Satsukawa, Koki & Wada, Kentaro & Iryo, Takamasa, 2024. "Stability analysis of a departure time choice problem with atomic vehicle models," Transportation Research Part B: Methodological, Elsevier, vol. 189(C).
    21. Chidambaram, Bhuvanachithra & Janssen, Marco A. & Rommel, Jens & Zikos, Dimitrios, 2014. "Commuters’ mode choice as a coordination problem: A framed field experiment on traffic policy in Hyderabad, India," Transportation Research Part A: Policy and Practice, Elsevier, vol. 65(C), pages 9-22.
    22. Emmanuel Dechenaux & Shakun Mago & Laura Razzolini, 2014. "Traffic congestion: an experimental study of the Downs-Thomson paradox," Experimental Economics, Springer;Economic Science Association, vol. 17(3), pages 461-487, September.
    23. Wijayaratna, Kasun P. & Dixit, Vinayak V., 2016. "Impact of information on risk attitudes: Implications on valuation of reliability and information," Journal of choice modelling, Elsevier, vol. 20(C), pages 16-34.
    24. Rapoport, Amnon & Gisches, Eyran J. & Daniel, Terry & Lindsey, Robin, 2014. "Pre-trip information and route-choice decisions with stochastic travel conditions: Experiment," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 154-172.
    25. Otsubo, Hironori & Rapoport, Amnon, 2008. "Vickrey's model of traffic congestion discretized," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 873-889, December.

    More about this item

    Keywords

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    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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