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Social Networks, Social Interactions, and Activity-Travel Behavior: A Framework for Microsimulation

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  • Theo Arentze
  • Harry Timmermans

Abstract

We argue that the social networks and activity-travel patterns of people interact and coevolve over time. Through social interaction, people exchange information about activity-travel choice alternatives and adapt their latent and overt preferences for alternatives to each other. At the same time, social networks are not static: new social links emerge and existing social links may dissolve in time, depending on activity-travel schedules and the attributes of persons. In this paper we propose a theoretical framework to incorporate these dynamics in microsimulations of activity-travel patterns. A core assumption of the proposed theory is that the utility that a person derives from social interaction is a function of dynamic social and information needs, on the one hand, and of similarity between the relevant characteristics of the persons involved, on the other. Furthermore, persons tend to adapt their preferences so as to increase the utility they derive from their social networks. We derive the theory and models from basic principles and discuss results of a first round of simulations conducted to examine the behavior of the model. We argue that the model is consistent with existing theories and findings in social network analysis.

Suggested Citation

  • Theo Arentze & Harry Timmermans, 2008. "Social Networks, Social Interactions, and Activity-Travel Behavior: A Framework for Microsimulation," Environment and Planning B, , vol. 35(6), pages 1012-1027, December.
    • Handle: RePEc:sae:envirb:v:35:y:2008:i:6:p:1012-1027
    DOI: 10.1068/b3319t
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    References listed on IDEAS

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    1. Masanao Aoki, 1995. "Economic Fluctuations With Interactive Agents: Dynamic And Stochastic Externalities," The Japanese Economic Review, Japanese Economic Association, vol. 46(2), pages 148-165, June.
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    Cited by:

    1. Ettema, Dick & Schwanen, Tim, 2012. "A relational approach to analysing leisure travel," Journal of Transport Geography, Elsevier, vol. 24(C), pages 173-181.
    2. Jones, Peter & Lucas, Karen, 2012. "The social consequences of transport decision-making: clarifying concepts, synthesising knowledge and assessing implications," Journal of Transport Geography, Elsevier, vol. 21(C), pages 4-16.
    3. Ryley, Tim J. & Zanni, Alberto M., 2013. "An examination of the relationship between social interactions and travel uncertainty," Journal of Transport Geography, Elsevier, vol. 31(C), pages 249-257.
    4. Zhao, Zhan & Koutsopoulos, Haris N. & Zhao, Jinhua, 2018. "Detecting pattern changes in individual travel behavior: A Bayesian approach," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 73-88.
    5. Matous, Petr, 2017. "Complementarity and substitution between physical and virtual travel for instrumental information sharing in remote rural regions: A social network approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 61-79.
    6. Lin, Tao & Wang, Donggen & Zhou, Meng, 2018. "Residential relocation and changes in travel behavior: what is the role of social context change?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 360-374.
    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. Vinayak, Pragun & Dias, Felipe F. & Astroza, Sebastian & Bhat, Chandra R. & Pendyala, Ram M. & Garikapati, Venu M., 2018. "Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels," Transport Policy, Elsevier, vol. 72(C), pages 129-137.
    9. Avineri, Erel, 2012. "On the use and potential of behavioural economics from the perspective of transport and climate change," Journal of Transport Geography, Elsevier, vol. 24(C), pages 512-521.
    10. Maness, Michael & Cirillo, Cinzia & Dugundji, Elenna R., 2015. "Generalized behavioral framework for choice models of social influence: Behavioral and data concerns in travel behavior," Journal of Transport Geography, Elsevier, vol. 46(C), pages 137-150.
    11. Lee, Jae Hyun & Goulias, Konstadinos G., 2018. "Companionship and time investment in social fields at different life cycle stages: Implications for activity and travel modeling and simulation," Research in Transportation Economics, Elsevier, vol. 68(C), pages 18-28.
    12. Li Shi & Lun Wu & Guanghua Chi & Yu Liu, 2016. "Geographical impacts on social networks from perspectives of space and place: an empirical study using mobile phone data," Journal of Geographical Systems, Springer, vol. 18(4), pages 359-376, October.
    13. Roy, P. & Martínez, A.J. & Miscione, G. & Zuidgeest, M.H.P. & van Maarseveen, M.F.A.M., 2012. "Using Social Network Analysis to profile people based on their e-communication and travel balance," Journal of Transport Geography, Elsevier, vol. 24(C), pages 111-122.
    14. Konstadinos G. Goulias & Ram M. Pendyala, 2014. "Choice context," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 5, pages 101-130, Edward Elgar Publishing.
    15. van Wee, Bert & De Vos, Jonas & Maat, Kees, 2019. "Impacts of the built environment and travel behaviour on attitudes: Theories underpinning the reverse causality hypothesis," Journal of Transport Geography, Elsevier, vol. 80(C).

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