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Road Traffic Congestion and Public Information: An Experimental Investigation

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

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. © 2008 LSE and the University of Bath

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Bibliographic Info

Article provided by London School of Economics and University of Bath in its journal Journal of Transport Economics and Policy.

Volume (Year): 42 (2008)
Issue (Month): 1 (January)
Pages: 43-82
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Handle: RePEc:tpe:jtecpo:v:42:y:2008:i:1:p:43-82

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Web page: http://www.bath.ac.uk/e-journals/jtep

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  1. 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.
  2. 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.
  3. Drew Fudenberg & David K. Levine, 1998. "Learning in Games," Levine's Working Paper Archive 2222, David K. Levine.
  4. Richard Arnott & Andre de Palma & Robin Lindsey, 1985. "Economics of a Bottleneck," Working Papers 636, Queen's University, Department of Economics.
  5. 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.
  6. David Levinson, 2003. "The Value of Advanced Traveler Information Systems for Route Choice," Working Papers 200307, University of Minnesota: Nexus Research Group.
  7. 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.
  8. 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.
  9. 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.
  10. Vickrey, William S, 1969. "Congestion Theory and Transport Investment," American Economic Review, American Economic Association, vol. 59(2), pages 251-60, 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. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-79, June.
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Cited by:
  1. 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.
  2. Laurent Denant-Boemont & Sabrina Hammiche, 2009. "Public Transit Capacity and User’s Choice: AnExperiment on Downs-Thomson Paradox," Working Papers halshs-00405501, HAL.

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