, Volume 37, Issue 4, pp 677–688 | Cite as

Hierarchical ordering of nests in a joint mode and destination choice model



This paper seeks to explore the relationship between mode and destination choice in an integrated nested choice model. A fundamental argument can be made that in certain circumstances, the ordering of choices should be reversed from the usual sequence of destination choice preceding mode choice. This results in a travel demand model where travelers are more likely to change destinations than to change transportation modes. For small and medium size urban areas, particularly in the United States, with less well developed public transit systems that draw few choice riders, this assumption makes much more sense than the traditional modeling assumptions. The models used in the new travel modeling system developed for Knoxville, Tennessee utilize this reversed ordering, with generally good results, which required no external tinkering in the logsum parameters.


Discrete choice models Mode choice Destination choice Demand Elasticity Hierarchical nesting Integrated models 


  1. Abrahamsson, T., Lundqvist, L.: Formulation and estimation of combined network equilibrium models with applications to Stockholm. Transp. Sci. 33(1), 80–100 (1999)CrossRefGoogle Scholar
  2. Ben-Akiva, M.: Structure of passenger travel demand models. Transp. Res. Rec. 526, 26–42 (1974)Google Scholar
  3. Bernardin, V.L.: A trip-based travel demand framework consistent with tours and stop interaction. Ph.D. Dissertation, Northwestern University (2008)Google Scholar
  4. Bernardin, V.S., Koppelman, F.S., Boyce, D.: Enhanced destination choice models incorporating agglomeration related to trip chaining while controlling for spatial competition. Transp. Res. Rec. 2132, 143–151 (2009)CrossRefGoogle Scholar
  5. Bowman, J.L., Ben-Akiva, M.E.: Activity-based disaggregate travel demand model system with activity schedules. Transp. Res. A 35(1), 1–28 (2001)CrossRefGoogle Scholar
  6. Boyce, D.: Is the Sequential Travel Forecasting Paradigm Counterproductive? J Urban Plan. Dev. 128(4), 169–183 (2002)CrossRefGoogle Scholar
  7. Bradley, M., Bowman, J.L.: A summary of design features of activity-based microsimulation models for US MPOs. In: Conference on innovations in travel demand modeling, Austin, TX, 2006Google Scholar
  8. Debrezion, G., Pels, E., Rietveld, P.: Modelling the joint access mode and railway station choice. Transp. Res. E 45(1), 270–283 (2009)CrossRefGoogle Scholar
  9. ELM-Works LLC: ELM version 1.0, 2009Google Scholar
  10. Fotheringham, A.S.: Some theoretical aspects of destination choice and their relevance to production-constrained gravity models. Environ. Plan. A 15, 1121–1132 (1983)CrossRefGoogle Scholar
  11. Fotheringham, A.S.: Modelling hierarchical destination choice. Environ. Plan. A 18, 401–418 (1986)CrossRefGoogle Scholar
  12. Hensher, D.A.: Sequential and full information maximum likelihood estimation of a nested logit model. Rev. Econ. Stat. 68(4), 657–667 (1986)CrossRefGoogle Scholar
  13. Jonnalagadda, N., Freedman, J., Davidson, W.A., Hunt, J.D.: Development of microsimulation activity-based model for San Francisco: destination and mode choice models. Transp. Res. Rec. 1777, 25–35 (2001)CrossRefGoogle Scholar
  14. Kitamura, R., Kermanshah, M.: Sequential model of interdependent activity and destination choices. Transp. Res. Rec. 987, 81–89 (1984)Google Scholar
  15. Koppelman, F.S., Wen, C.-H.: Alternative nested logit models: structure, properties and estimation. Transp. Res. B 32(5), 289–298 (1998)CrossRefGoogle Scholar
  16. Lerman, S.R.: Location, housing, automobile ownership, and mode to work: a joint choice model. Transp. Res. Rec. 610, 6–11 (1976)Google Scholar
  17. Miller, E.J., Roorda, M.J., Carrasco, J.A.: A tour-based model of travel mode choice. Transportation 32(4), 399–422 (2005)CrossRefGoogle Scholar
  18. Nerella, S., Bhat, C.R.: Numerical analysis of effect of sampling of alternatives in discrete choice models. Transp. Res. Rec. 1894, 11–19 (2004)CrossRefGoogle Scholar
  19. NuStats: 2000 Knoxville urban area household travel behavior study. Final Report. Knoxville Regional Transportation Planning Organization, Knoxville, TN (2001)Google Scholar
  20. NuStats: 2008 East Tennessee household travel survey. Final Report. Knoxville Regional Transportation Planning Organization, Knoxville, TN (2008)Google Scholar
  21. Richards, M.G., Ben-Akiva, M.E.: A simultaneous destination and mode choice model for shopping trips. Transportation 3(4), 343–356 (1974)CrossRefGoogle Scholar
  22. Siegel, J.D., De Cea, J., Fernandez, J.E., Rodriguez, R.E., Boyce, D.: Comparisons of urban travel forecasts prepared with the sequential procedure and a combined model. Netw. Spat. Econ. 6(2), 135–148 (2006)CrossRefGoogle Scholar
  23. Vrtic, M., Axhausen, K.W.: The impact of tilting trains in Switzerland: a route choice model of regional- and long distance public transport trips. In: 82nd annual meeting of the transportation research board, Washington, DC, 2008Google Scholar
  24. Yagi, S., Mohammadian, A.K.: Joint models of home-based tour mode and destination choices: applications to a developing country. Transp. Res. Rec. 2076, 29–40 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  1. 1.École Polytechnique Fédérale de LausanneENAC TRANSP-ORLausanneSwitzerland
  2. 2.Bernardin, Lochmueller & Associates, Inc.EvansvilleUSA

Personalised recommendations