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Road Traffic Congestion and Public Information: An Experimental Investigation

  • Kene Boun My
  • Laurent Denant-Boèmont
  • Frédéric Koessler
  • Marc Willinger
  • Anthony Ziegelmeyer

    ()

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 total travel costs match the predicted ones. In other words, subjects' capacity to coordinate is neither affected by the availability of public information on past departure rates nor by the relative cost of delay. This 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 subjects’ capacity to coordinate is not affected by the size of the population.

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Paper provided by Max Planck Institute of Economics, Strategic Interaction Group in its series Papers on Strategic Interaction with number 2006-20.

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Length: 32 pages
Date of creation: Dec 2006
Date of revision:
Handle: RePEc:esi:discus:2006-20
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  1. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-60, May.
  2. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
  3. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
  4. 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.
  5. 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.
  6. 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.
  7. Arnott, Richard & de Palma, Andre & Lindsey, Robin, 1990. "Economics of a bottleneck," Journal of Urban Economics, Elsevier, vol. 27(1), pages 111-130, January.
  8. David Levinson, 2003. "The Value of Advanced Traveler Information Systems for Route Choice," Working Papers 200307, University of Minnesota: Nexus Research Group.
  9. 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.
  10. 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.
  11. 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.
  12. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-79, June.
  13. 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.
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