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The effects of social networks on choice set dynamics: Results of numerical simulations using an agent-based approach

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  • Han, Qi
  • Arentze, Theo
  • Timmermans, Harry
  • Janssens, Davy
  • Wets, Geert
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    Abstract

    Activity-based analysis has slowly shifted gear from the analysis of daily activity patterns to the analysis and modeling of dynamic activity-travel patterns. In this paper, we address one type of dynamics: the formation and adaptation of location choice sets under influence of dyad relationships within social networks. It extends the dynamic model developed in earlier work, which simulates habitual behavior versus exploitation and exploration as a function of discrepancies between dynamic, context-dependent aspiration levels and expected outcomes. Principles of social comparison and knowledge transfer are used in modeling the impact of social networks through information exchange, adaptations of spatial choice sets and formation of common aspiration levels. We demonstrate model properties using numerical simulation with a case study of shopping activities.

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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: 310-322

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    Handle: RePEc:eee:transa:v:45:y:2011:i:4:p:310-322

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    Related research

    Keywords: Social learning Context dependent Dynamic behavior Information exchange Choice set formation;

    References

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    1. Juan Carrasco & Eric Miller, 2006. "Exploring the propensity to perform social activities: a social network approach," Transportation, Springer, Springer, vol. 33(5), pages 463-480, 09.
    2. Swait, Joffre & Ben-Akiva, Moshe, 1987. "Incorporating random constraints in discrete models of choice set generation," Transportation Research Part B: Methodological, Elsevier, Elsevier, vol. 21(2), pages 91-102, April.
    3. Theo Arentze & Harry Timmermans, 2003. "Modeling learning and adaptation processes in activity-travel choice A framework and numerical experiment," Transportation, Springer, Springer, vol. 30(1), pages 37-62, February.
    4. T. Arentze & H. Timmermans, 2005. "Representing mental maps and cognitive learning in micro-simulation models of activity-travel choice dynamics," Transportation, Springer, Springer, vol. 32(4), pages 321-340, 07.
    5. Arentze, Theo A. & Ettema, Dick & Timmermans, Harry J.P., 2011. "Estimating a model of dynamic activity generation based on one-day observations: Method and results," Transportation Research Part B: Methodological, Elsevier, Elsevier, vol. 45(2), pages 447-460, February.
    6. Swait, Joffre, 2001. "Choice set generation within the generalized extreme value family of discrete choice models," Transportation Research Part B: Methodological, Elsevier, Elsevier, vol. 35(7), pages 643-666, August.
    7. Antonio P�ez & Darren M Scott, 2007. "Social influence on travel behavior: a simulation example of the decision to telecommute," Environment and Planning A, Pion Ltd, London, vol. 39(3), pages 647-665, March.
    8. Theo Arentze & Harry Timmermans, 2008. "Social networks, social interactions, and activity-travel behavior: a framework for microsimulation," Environment and Planning B: Planning and Design, Pion Ltd, London, Pion Ltd, London, vol. 35(6), pages 1012-1027, November.
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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, Elsevier, vol. 59(C), pages 159-171.
    2. Habib, Khandker Nurul & Sasic, Ana & Weis, Claude & Axhausen, Kay, 2013. "Investigating the nonlinear relationship between transportation system performance and daily activity–travel scheduling behaviour," Transportation Research Part A: Policy and Practice, Elsevier, Elsevier, vol. 49(C), pages 342-357.

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