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


  • Hackney, Jeremy
  • Marchal, Fabrice


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.

Suggested Citation

  • Hackney, Jeremy & Marchal, Fabrice, 2011. "A coupled multi-agent microsimulation of social interactions and transportation behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(4), pages 296-309, May.
  • Handle: RePEc:eee:transa:v:45:y:2011:i:4:p:296-309

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    References listed on IDEAS

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    Cited by:

    1. Kii, Masanobu & Nakanishi, Hitomi & Nakamura, Kazuki & Doi, Kenji, 2016. "Transportation and spatial development: An overview and a future direction," Transport Policy, Elsevier, vol. 49(C), pages 148-158.
    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. Miguel Picornell & Tomás Ruiz & Maxime Lenormand & José Ramasco & Thibaut Dubernet & Enrique Frías-Martínez, 2015. "Exploring the potential of phone call data to characterize the relationship between social network and travel behavior," Transportation, Springer, vol. 42(4), pages 647-668, July.
    4. Fariya Sharmeen & Theo Arentze & Harry Timmermans, 2015. "Predicting the evolution of social networks with life cycle events," Transportation, Springer, vol. 42(5), pages 733-751, September.
    5. 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.
    6. Masashi Okushima, 2015. "Simulating social influences on sustainable mobility shifts for heterogeneous agents," Transportation, Springer, vol. 42(5), pages 827-855, September.
    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.


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