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Experiments and Simulations on Day-to-Day Route Choice-Behaviour

Author

Listed:
  • Reinhard Selten
  • M. Schreckenberg
  • Thomas Pitz
  • T. Chmura
  • S. Kube

Abstract

The paper reports laboratory experiments on a day-to-day route choice game with two routes. Subjects had to choose between a main road M and a side road S. The capacity was greater for the main road. 18 subjects participated in each session. In equilibrium the number of subjects is 12 on M and 6 on S. Two treatments with 6 sessions each were run at the Laboratory of Experimental Economics at Bonn University using RatImage. Feedback was given in treatment I only about own travel time and in treatment II on travel time for M and S. Money payoffs increase with decreasing time. The main results are as follows. 1. Mean numbers on M and S are very near to the equilibrium. 2. Fluctuations persist until the end of the sessions in both treatments. 3. Fluctuations are smaller under treatment II .The effect is small but significant. 4. The total number of changes is significantly greater in treatment I. 5. Subjects’ road changes and payoffs are negatively correlated in all sessions. 6. A direct response mode reacts with more changes for bad payoffs whereas a contrary response mode shows opposite reactions. Both response modes can be observed. 7. The simulation of an extended payoff sum learning model closely fits the main results of the statistical evaluation of the data.

Suggested Citation

  • Reinhard Selten & M. Schreckenberg & Thomas Pitz & T. Chmura & S. Kube, 2003. "Experiments and Simulations on Day-to-Day Route Choice-Behaviour," CESifo Working Paper Series 900, CESifo.
    • Handle: RePEc:ces:ceswps:_900
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    References listed on IDEAS

    as
    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. Arthur, W Brian, 1991. "Designing Economic Agents that Act Like Human Agents: A Behavioral Approach to Bounded Rationality," American Economic Review, American Economic Association, vol. 81(2), pages 353-359, 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 848-881, September.
    4. Wahle, Joachim & Bazzan, Ana Lúcia C & Klügl, Franziska & Schreckenberg, Michael, 2000. "Decision dynamics in a traffic scenario," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 669-681.
    5. Iida, Yasunori & Akiyama, Takamasa & Uchida, Takashi, 1992. "Experimental analysis of dynamic route choice behavior," Transportation Research Part B: Methodological, Elsevier, vol. 26(1), pages 17-32, February.
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    Cited by:

    1. Giovanna Devetag & Francesca Pancotto & Thomas Brenner, 2011. "The Minority Game Unpacked: Coordination and Competition in a Team-based Experiment," LEM Papers Series 2011/18, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Giovanna Devetag & Francesca Pancotto & Thomas Brenner, 2014. "The minority game unpacked:," Journal of Evolutionary Economics, Springer, vol. 24(4), pages 761-797, September.
    3. Siegfried Berninghaus & Karl-Martin Ehrhart & Marion Ott, 2006. "A network experiment in continuous time: The influence of link costs," Experimental Economics, Springer;Economic Science Association, vol. 9(3), pages 237-251, September.
    4. Giulio Bottazzi & Giovanna Devetag, 2007. "Competition and coordination in experimental minority games," Journal of Evolutionary Economics, Springer, vol. 17(3), pages 241-275, June.
    5. Thorsten Chmura & Thomas Pitz, 2007. "An Extended Reinforcement Algorithm for Estimation of Human Behaviour in Experimental Congestion Games," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-1.

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    More about this item

    Keywords

    travel behaviour research; information in intelligent transportation systems; day-to-day route choice; laboratory experiments; payoff sum model;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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