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Road Traffic Congestion and Public Information: An Experimental Investigation

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Author Info
Anthony Ziegelmeyer (Max Planck Institute of Economics, Strategic Interaction Group, Jena (Germany))
Frédéric Koessler (THEMA, University of Cergy-Pontoise (France))
Kene Boun My (BETA-Theme, CNRS, Louis Pasteur University, Strasbourg (France))
Laurent Denant-Boèmont () (CREM, University of Rennes 1 (France))

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Abstract

This paper reports two laboratory studies designed to study the impact of public information about past departure rates on congestion levels and travel costs. Our experimental design is based on a discrete version of Arnott, de Palma, and Lindsey’s (1990) bottleneck model where subjects have to choose their departure time in order to reach a common destination. Experimental treatments in our first study differ in terms of the level of public information on past departure rates and the relative cost of delay. In all treatments, congestion occurs and the observed travel costs are quite similar to the predicted ones. In other words, subjects’ capacity to coordinate does not seem to be affected by the availability of public information on past departure rates 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 characterizes individual behavior. The number of experimental subjects taking the role of drivers is four times larger in our second study than in our first study. We observe that coordination failures in our congestion situation do not become more severe when the number of drivers increases.

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Paper provided by THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise in its series THEMA Working Papers with number 2007-05.

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Date of creation: 2007
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Handle: RePEc:ema:worpap:2007-05

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Related research
Keywords: Travel behavior Congestion Information in intelligent transportation systems Laboratory experiments.

Other versions of this item:

Find related papers by 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, and Information
R40 - Urban, Rural, and Regional Economics - - Transportation Systems - - - General
R41 - Urban, Rural, and Regional Economics - - Transportation Systems - - - Transportation: Demand, Supply, and Congestion

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References listed on IDEAS
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  1. Selten, R. & Chmura, T. & Pitz, T. & Kube, S. & Schreckenberg, M., 2007. "Commuters route choice behaviour," Games and Economic Behavior, Elsevier, vol. 58(2), pages 394-406, February. [Downloadable!] (restricted)
  2. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September. [Downloadable!] (restricted)
  3. Arnott, Richard & de Palma, Andre & Lindsey, Robin, 1990. "Economics of a bottleneck," Journal of Urban Economics, Elsevier, vol. 27(1), pages 111-130, January. [Downloadable!] (restricted)
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  4. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-60, May. [Downloadable!] (restricted)
  5. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
  6. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-79, June. [Downloadable!] (restricted)
  7. David Levinson, 2003. "The Value of Advanced Traveler Information Systems for Route Choice," Working Papers 200307, University of Minnesota: Nexus Research Group. [Downloadable!]
  8. Moshe Ben-Akiva & Andre de Palma & Pavlos Kanaroglou, 1984. "Dynamic Model of Peak Period Traffic Congestion with Elastic Arrival Rates," Working Papers 588, Queen's University, Department of Economics.
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