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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.

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  • 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

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    Cited by:

    1. Wang, Yaoli & Kutadinata, Ronny & Winter, Stephan, 2019. "The evolutionary interaction between taxi-sharing behaviours and social networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 170-180.
    2. 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.
    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. Leng, Nuannuan & Corman, Francesco, 2020. "The role of information availability to passengers in public transport disruptions: An agent-based simulation approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 214-236.
    5. Paul M. Torrens, 2023. "Agent models of customer journeys on retail high streets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(1), pages 87-128, January.
    6. 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.
    7. 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.
    8. Masashi Okushima, 2015. "Simulating social influences on sustainable mobility shifts for heterogeneous agents," Transportation, Springer, vol. 42(5), pages 827-855, September.
    9. Zhang, Guijie & Wei, Fangfang & Jia, Ning & Ma, Shoufeng & Wu, Yi, 2019. "Information adoption in commuters’ route choice in the context of social interactions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 300-316.
    10. Kowald, Matthias & van den Berg, Pauline & Frei, Andreas & Carrasco, Juan-Antonio & Arentze, Theo & Axhausen, Kay & Mok, Diana & Timmermans, Harry & Wellman, Barry, 2013. "Distance patterns of personal networks in four countries: a comparative study," Journal of Transport Geography, Elsevier, vol. 31(C), pages 236-248.
    11. Tang, Yue & Gao, Song & Ben-Elia, Eran, 2017. "An exploratory study of instance-based learning for route choice with random travel times," Journal of choice modelling, Elsevier, vol. 24(C), pages 22-35.
    12. 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.
    13. Liu, Siyuan & Qu, Qiang, 2016. "Dynamic collective routing using crowdsourcing data," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 450-469.
    14. 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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