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Incorporating aggregate behavior in an individual's discrete choice: An application to analyzing illegal bicycle parking behavior

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  • Fukuda, Daisuke
  • Morichi, Shigeru

Abstract

Many types of travel behavior involve positive social interaction (conformity effect) and it sometimes induces undesirable results, such as chronic illegal bicycle parking and illegal car parking. In this study, the conformity effects among bicycle users in the choice problem of bicycle parking locations were modeled and estimated within a discrete choice framework. The proposed model combines discrete choice behavior of bicycle parking locations at an individual level (micro-level) with an average choice at an aggregate level (macro-level). The social equilibrium equation, which is derived from the individual-level choice model, entails multiple equilibria with regard to the choice proportion for each reference group of individuals. The model was econometrically identified by using the data collected in a survey conducted in Tokyo in 2001. The empirical results indicated that large variations in collective behavior occur across subgroups, which were defined by the stations the respondents visit often, since there was an intensive positive social interaction. Finally, the marginal frequency of police patrols required to drastically reduce the level of illegal bicycle parking was also calculated using the identified model.

Suggested Citation

  • Fukuda, Daisuke & Morichi, Shigeru, 2007. "Incorporating aggregate behavior in an individual's discrete choice: An application to analyzing illegal bicycle parking behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(4), pages 313-325, May.
  • Handle: RePEc:eee:transa:v:41:y:2007:i:4:p:313-325
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    References listed on IDEAS

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    Cited by:

    1. Walker, Joan L. & Ehlers, Emily & Banerjee, Ipsita & Dugundji, Elenna R., 2011. "Correcting for endogeneity in behavioral choice models with social influence variables," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(4), pages 362-374, May.
    2. Wang, Dianhai & Feng, Tianjun & Liang, Chunyan, 2008. "Research on bicycle conversion factors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(8), pages 1129-1139, October.
    3. Moshe Ben-Akiva & André Palma & Daniel McFadden & Maya Abou-Zeid & Pierre-André Chiappori & Matthieu Lapparent & Steven Durlauf & Mogens Fosgerau & Daisuke Fukuda & Stephane Hess & Charles Manski & Ar, 2012. "Process and context in choice models," Marketing Letters, Springer, vol. 23(2), pages 439-456, June.
    4. José I. Castillo-Manzano & Mercedes Castro-Nuño & Lourdes Lopez-Valpuesta, 2015. "The relationship between public and private bicycle use: the case of Seville," ERSA conference papers ersa15p206, European Regional Science Association.
    5. Kroesen, Maarten, 2015. "Do partners influence each other’s travel patterns? A new approach to study the role of social norms," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 489-505.
    6. 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.
    7. Maness, Michael & Cirillo, Cinzia, 2016. "An indirect latent informational conformity social influence choice model: Formulation and case study," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 75-101.
    8. Martens, Karel, 2007. "Promoting bike-and-ride: The Dutch experience," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(4), pages 326-338, May.
    9. Wilton, Robert D. & Páez, Antonio & Scott, Darren M., 2011. "Why do you care what other people think? A qualitative investigation of social influence and telecommuting," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(4), pages 269-282, May.
    10. 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.
    11. Jan-Dirk Schmöcker & Tsuyoshi Hatori & David Watling, 2014. "Dynamic process model of mass effects on travel demand," Transportation, Springer, vol. 41(2), pages 279-304, March.
    12. Maria Kamargianni & Moshe Ben-Akiva & Amalia Polydoropoulou, 2014. "Incorporating social interaction into hybrid choice models," Transportation, Springer, vol. 41(6), pages 1263-1285, November.
    13. Frank Goetzke & Regine Gerike & Antonio Páez & Elenna Dugundji, 2015. "Social interactions in transportation: analyzing groups and spatial networks," Transportation, Springer, vol. 42(5), pages 723-731, September.

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