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A coupled multi-agent microsimulation of social interactions and transportation behavior

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  • Hackney, Jeremy
  • Marchal, Fabrice
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    Abstract

    Choice set formation, location and mode preferences, coordinated scheduling, alternative utility valuations, and shared mobility resources are among the many activity-travel issues hypothesized to be significantly influenced by traveler interdependencies. Empirical evidence lags theory, particularly about the geography of social networks. A simulation tool is presented to let the experimenter construct and test hypothetical interdependencies between geography, socially-linked travelers, and activity-travel choices. The exploratory tool is integrated in the Multi-Agent Transportation Simulation Toolbox (MatSim-T). Initially, any social network can be constructed and embedded in geography. It can remain static, or be adapted to the travel patterns of the agents. The interactions and exchanges between agents influencing socializing and/or travel behavior can be defined in substance and in time/space. The reward for socializing or being socially linked can be varied. Finally, the co-dependence of social factors and travel behavior can be studied. This paper introduces the model and presents verification results which illustrate the coupling of extremely simplified socializing assumptions and travel behavior.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 45 (2011)
    Issue (Month): 4 (May)
    Pages: 296-309

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    Handle: RePEc:eee:transa:v:45:y:2011:i:4:p:296-309

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    Related research

    Keywords: Behavior microsimulation Social networks Activity-based planning Travel behavior;

    References

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    1. Elenna R Dugundji & L�szl� Guly�s, 2008. "Sociodynamic discrete choice on networks in space: impacts of agent heterogeneity on emergent outcomes," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 35(6), pages 1028-1054, November.
    2. repec:att:wimass:9127 is not listed on IDEAS
    3. Manski, Charles F, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Wiley Blackwell, vol. 60(3), pages 531-42, July.
    4. Juan Carrasco & Eric Miller, 2006. "Exploring the propensity to perform social activities: a social network approach," Transportation, Springer, vol. 33(5), pages 463-480, 09.
    5. Shy,Oz, 2001. "The Economics of Network Industries," Cambridge Books, Cambridge University Press, number 9780521800952, November.
    6. Charles F. Manski, 2000. "Economic Analysis of Social Interactions," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 115-136, Summer.
    7. Juan Antonio Carrasco & Bernie Hogan & Barry Wellman & Eric J. Miller, 2008. "Agency In Social Activity Interactions: The Role Of Social Networks In Time And Space," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 99(5), pages 562-583, December.
    8. David Charypar & Kai Nagel, 2005. "Generating complete all-day activity plans with genetic algorithms," Transportation, Springer, vol. 32(4), pages 369-397, 07.
    9. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    10. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2007. "Identification of Peer Effects through Social Networks," IZA Discussion Papers 2652, Institute for the Study of Labor (IZA).
    11. Wong, Ling Heng & Pattison, Philippa & Robins, Garry, 2006. "A spatial model for social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(1), pages 99-120.
    12. Emily M. Jin & Michelle Girvan & M. E. J. Newman, 2001. "The Structure of Growing Social Networks," Working Papers 01-06-032, Santa Fe Institute.
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    Cited by:
    1. Sharmeen, Fariya & Arentze, Theo & Timmermans, Harry, 2014. "An analysis of the dynamics of activity and travel needs in response to social network evolution and life-cycle events: A structural equation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 159-171.

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