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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))

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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Bibliographic Info

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

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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.
  2. Mahmassani, Hani S. & Jou, Rong-Chang, 2000. "Transferring insights into commuter behavior dynamics from laboratory experiments to field surveys," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(4), pages 243-260, May.
  3. 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.
  4. Richard Arnott & Andre de Palma & Robin Lindsey, 1985. "Economics of a Bottleneck," Working Papers 636, Queen's University, Department of Economics.
  5. Arentze, T.A. & Timmermans, H.J.P., 2005. "Information gain, novelty seeking and travel: a model of dynamic activity-travel behavior under conditions of uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(2-3), pages 125-145.
  6. David Levinson, 2003. "The Value of Advanced Traveler Information Systems for Route Choice," Working Papers 200307, University of Minnesota: Nexus Research Group.
  7. Drew Fudenberg & David K. Levine, 1998. "Learning in Games," Levine's Working Paper Archive 2222, David K. Levine.
  8. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-60, May.
  9. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-79, June.
  10. Denant-Boèmont, L. & Petiot, R., 2003. "Information value and sequential decision-making in a transport setting: an experimental study," Transportation Research Part B: Methodological, Elsevier, vol. 37(4), pages 365-386, May.
  11. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
  12. 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.
  13. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
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Cited by:
  1. 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.
  2. Laurent Denant-Boemont & Sabrina Hammiche, 2009. "Public Transit Capacity and User’s Choice: AnExperiment on Downs-Thomson Paradox," Working Papers halshs-00405501, HAL.
  3. 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, London School of Economics and University of Bath, vol. 46(1), pages 139-155, January.
  4. 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.
  5. 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 3.
  6. 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.

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