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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

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.

Suggested Citation

  • Han, Qi & Arentze, Theo & Timmermans, Harry & Janssens, Davy & Wets, Geert, 2011. "The effects of social networks on choice set dynamics: Results of numerical simulations using an agent-based approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(4), pages 310-322, May.
  • Handle: RePEc:eee:transa:v:45:y:2011:i:4:p:310-322
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    References listed on IDEAS

    as
    1. Theo Arentze & Harry Timmermans, 2003. "Modeling learning and adaptation processes in activity-travel choice A framework and numerical experiment," Transportation, Springer, vol. 30(1), pages 37-62, February.
    2. Antonio Páez & 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.
    3. Juan Carrasco & Eric Miller, 2006. "Exploring the propensity to perform social activities: a social network approach," Transportation, Springer, vol. 33(5), pages 463-480, September.
    4. 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, vol. 45(2), pages 447-460, February.
    5. Swait, Joffre, 2001. "Choice set generation within the generalized extreme value family of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 643-666, August.
    6. T. Arentze & H. Timmermans, 2005. "Representing mental maps and cognitive learning in micro-simulation models of activity-travel choice dynamics," Transportation, Springer, vol. 32(4), pages 321-340, July.
    7. Swait, Joffre & Ben-Akiva, Moshe, 1987. "Incorporating random constraints in discrete models of choice set generation," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 91-102, April.
    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, vol. 35(6), pages 1012-1027, November.
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    Citations

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    Cited by:

    1. Kim, Jinhee & Rasouli, Soora & Timmermans, Harry, 2014. "Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 71-85.
    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, vol. 49(C), pages 342-357.
    3. Thibaut Dubernet & Kay Axhausen, 2015. "Implementing a household joint activity-travel multi- agent simulation tool: first results," Transportation, Springer, vol. 42(5), pages 753-769, September.
    4. Wei, Fangfang & Jia, Ning & Ma, Shoufeng, 2016. "Day-to-day traffic dynamics considering social interaction: From individual route choice behavior to a network flow model," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 335-354.
    5. Masashi Okushima, 2015. "Simulating social influences on sustainable mobility shifts for heterogeneous agents," Transportation, Springer, vol. 42(5), pages 827-855, September.
    6. repec:eee:eejocm:v:24:y:2017:i:c:p:22-35 is not listed on IDEAS
    7. 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.
    8. Liu, Siyuan & Qu, Qiang, 2016. "Dynamic collective routing using crowdsourcing data," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 450-469.
    9. Xiao, Yu & Lo, Hong K., 2016. "Day-to-day departure time modeling under social network influence," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 54-72.

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