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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.

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Bibliographic Info

Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

Volume (Year): 41 (2007)
Issue (Month): 4 (May)
Pages: 313-325

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Handle: RePEc:eee:transa:v:41:y:2007:i:4:p:313-325

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  1. William Brock & Steven N. Durlauf, 2000. "Interactions-Based Models," NBER Technical Working Papers 0258, National Bureau of Economic Research, Inc.
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Cited by:
  1. 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 & , 2012. "Process and context in choice models," Marketing Letters, Springer, vol. 23(2), pages 439-456, June.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.

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