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Transportation

, Volume 40, Issue 1, pp 151–171 | Cite as

Analysis of long-distance vacation travel demand in the United States: a multiple discrete–continuous choice framework

  • Caleb Van Nostrand
  • Vijayaraghavan Sivaraman
  • Abdul Rawoof PinjariEmail author
Article

Abstract

This study analyzes the annual vacation destination choices and related time allocation patterns of American households. More specifically, an annual vacation destination choice and time allocation model is formulated to simultaneously predict the different vacation destinations that a household visits in a year, and the time (no. of days) it allocates to each of the visited destinations. The model takes the form of a multiple discrete–continuous extreme value (MDCEV) structure. Further, a variant of the MDCEV model is proposed to reduce the prediction of unrealistically small amounts of vacation time allocation to the chosen destinations. To do so, the continuously non-linear utility functional form in the MDCEV framework is replaced with a combination of a linear and non-linear form. The empirical analysis was performed using the 1995 American Travel Survey data, with the United States divided into 210 alternative destinations. The model estimation results provide several insights into the determinants of households’ vacation destination choice and time allocation patterns. Results suggest that travel times and travel costs to the destinations, and lodging costs, leisure activity opportunities (measured by employment in the leisure industry), length of coastline, and weather conditions at the destinations influence households’ destination choices for vacations. The annual vacation destination choice model developed in this study can be incorporated into a larger national travel modeling framework for predicting the national-level, origin–destination flows for vacation travel.

Keywords

Long-distance travel Leisure travel demand National travel demand model Destination choice Kuhn-Tucker demand model systems Multiple discreteness 

Notes

Acknowledgments

The authors would like to thank three anonymous reviewers and the US Co-Editor David T. Hartgen for valuable comments on an earlier version of the paper. Jeff Hood raised (at a conference) thought provoking questions related to overprediction of very short time allocations.

References

  1. Ashiabor, S., Baik, H., Trani, A.: Logit models for forecasting nationwide intercity travel demand in the United States. Transp. Res. Rec.: J. Transp. Res. Board 1, 1–12 (2007)Google Scholar
  2. Baik, H., Trani, A., Hinze, N., Ashiabor, S., Seshadri, A.: Forecasting model for air taxi, commercial airline, and automobile demand in the United States. J. Transp. Res. Record 2052, 9–20 (2008)CrossRefGoogle Scholar
  3. Beser, M., Algers, S.: The SAMPERS models. In: Lundqvist, L., Mattsson, L.G. (eds.) National Transport Models: Recent Developments and Prospects. The Swedish Transport and Communications Research Board, Stockholm (1999)Google Scholar
  4. Bhat, C.: The multiple discrete-continuous extreme value (MDCEV) model: role of utility function parameters, identification considerations, and model extensions. Transp. Res. B 42(3), 274–303 (2008)CrossRefGoogle Scholar
  5. Bureau of Economic Analysis: Gross Domestic Product by State (1995). Retrieved January 10, 2011, from http://www.bea.gov/regional/gsp/
  6. Bureau of Labor Statistics: BLS Spotlight on Statistics Travel (2010)Google Scholar
  7. Bureau of Labor Statistics: Employment, Hours, and Earnings—State and Metro Area (1995). Retrieved December 2010, from http://www.bls.gov/data/#employment
  8. Bureau of Transportation Statistics: 1995 American Travel Survey Technical Documentation. Washington, DC, (1995a)Google Scholar
  9. Bureau of Transportation Statistics: 1995 American Travel Survey Profile (1997). http://www.bts.gov/publications/1995_american_travel_survey/us_profile/entire.pdf
  10. Bureau of Transportation Statistics: Airline Origin and Destination Survey (DB1B). Washington, DC (1995b)Google Scholar
  11. Bureau of Transportation Statistics: Transportation Statistics Annual Report 1998 Long-Distance Travel And Freight. Washington, DC (1998)Google Scholar
  12. Daly, A.: Estimating choice models containing attraction variables. Transp. Res. B 16(1), 5–15 (1982)CrossRefGoogle Scholar
  13. Energy Information Administration: Retail Gasoline Historical Prices (1995). Retrieved December 2010, from http://www.eia.doe.gov/oil_gas/petroleum/data_publications/wrgp/mogas_history.html
  14. Epstein, J.M., Parker, J., Cummings, D., Hammond, R.A.: Coupled contagion dynamics of fear and disease: mathematical and computational explorations. PLoS ONE 3(12), 3955 (2008)CrossRefGoogle Scholar
  15. Eugenio-Martin, J.: Modelling determinants of tourism demand as a five stage process: A discrete choice methodological approach. Tour. Hosp. Res. 4(4), 341 (2003)Google Scholar
  16. Eymann, A., Ronning, G.: Microeconometric models of tourists’ destination choice. Reg. Sci. Urb. Econ. 27(6), 735–761 (1997)CrossRefGoogle Scholar
  17. Fosgerau, M.: PETRA—an activity based approach to travel demand analysis. In: Lundqvist, L., Mattsson, L.G. (eds.) National Transport Models: Recent Developments and Prospects. The Swedish Transport and Communications Research Board, Stockholm (2001)Google Scholar
  18. Greenridge, K.: Forecasting Tourism Demand: An STM Approach. Ann. Tour. Res. 28(1), 98–112 (2001)CrossRefGoogle Scholar
  19. Grush, W.: Usage and Vehicle Miles of Travel (VMT) Per Capita. Highw. Inf. Q. 5(4) (1998)Google Scholar
  20. Hackney, J.: Discrete choice models for long-distance travel based on the DATELINE survey. Paper presented at the 4th Swiss Transport Research Conference, Monte Verita/Ascona, 2004Google Scholar
  21. Haliciolgu, F.: An econometric analysis of aggregate outbound tourism demand of Turkey. Paper presented at the 6th DeHaan tourism management conference proceedings, 2008Google Scholar
  22. HCG and TOI: A Model System to Predict Fuel Use and Emissions from Private Travel in Norway from 1985 to 2025: Norwegian Ministry of Transport (1990)Google Scholar
  23. Horowitz, A.: NCHRP Synthesis 358: Statewide Travel Forecasting Models. Transportation Research Board of the National Academies, Washington, DC (2006)Google Scholar
  24. Inter University Consortium for Political and Social Research (ICPSR): 1995 Consumer Expenditure Survey (2011). Retrieved 2011, from http://www.icpsr.umich.edu
  25. Iso-Ahola, S.: Towards a Social Psychology of Recreational Travel. Leis. Stud. 2, 45–56 (1983)CrossRefGoogle Scholar
  26. Koppelman, F., Sethi, V.: Incorporating variance and covariance heterogeneity in the generalized nested logit model: an application to modeling long distance travel choice behavior. Transp. Res. B 39(9), 825–853 (2005)CrossRefGoogle Scholar
  27. LaMondia, J., Snell, T., Bhat, C.: Traveler Behavior and Values Analysis in the Context of Vacation Destination and Travel Mode Choices: A European Union Case Study (2009)Google Scholar
  28. LaMondia, J., Bhat, C., Hensher, D.: An annual time use model for domestic vacation travel. J. Choice Model. 1(1), 70 (2008)Google Scholar
  29. Lue, C., Crompton, J., Fesenmaier, D.: Conceptualization of multi-destination pleasure trips. Ann. Tour. Res. 20(2), 289–301 (1993)CrossRefGoogle Scholar
  30. Lundqvist, L., Mattsson, L.G.: National Transport Models: Recent Developments and Prospects. Springer, New York (2002)Google Scholar
  31. Microsoft: MapPoint North America (2009)Google Scholar
  32. Moeckel, R., Donnelly, R.: nationwide estimate for long distance travel (NELDT) Paper presented at the third international conference on innovations in travel modeling (ITM) of the transportation research board, Tempe, AZ, 2010Google Scholar
  33. Morley, C.L.: A microeconomic theory of international tourism demand. Ann. Tour. Res. 19, 250–267 (1992)CrossRefGoogle Scholar
  34. National Oceanic and Atmospheric Administration: Ocean and Coastal Resource Management (2011). Retrieved January 10, 2011, from National Oceanic and Atmospheric Administration http://coastalmanagement.noaa.gov/
  35. Outwater, M.L., Tierney, K., Bradley, M., Sall, E., Kuppam, A., Modugula, V.: California statewide model for high-speed rail. J. Choice Model. 3(1), 58 (2010)Google Scholar
  36. Phaneuf, D.J., Smith, V.K.: Recreation Demand Models. In: Vincent, J.R. (ed.) Handbook of Enviornmental Economics. North Holland, Amsterdam (2005)Google Scholar
  37. Pinjari, A., Bhat, C.: An efficient forecasting procedure for Kuhn-Tucker consumer demand model systems. Technical paper. Department of Civil & Environmental Engineering, University of South Florida (2010)Google Scholar
  38. Pollak, R.A., Wales, T.J.: Demand System Specification and Estimation. Oxford University Press, Oxford (1992)Google Scholar
  39. Rich, J., Brocker, J., Hanson, C., Korchenewych, A., Nielsen, O., Vuk, G.: Report on Scenario, Traffic Forecast and Analysis of Traffic on the TEN-T, Taking into Consideration the External Dimension of the Union-Trans Tools version 2 (2009)Google Scholar
  40. Rugg, D.: The choice of journey destination: A theoretical and empirical analysis. Rev. Econ. Stat. 55(1), 64–72 (1973)CrossRefGoogle Scholar
  41. Savageau, D., Loftus, G.: Places Rated Almanac: Macmillan General Reference (1997)Google Scholar
  42. Seddighi, H., Theocharous, A.: A model of tourism destination choice. A theoretical and empirical analysis. Tourism Management 23, 475–487 (2002)CrossRefGoogle Scholar
  43. Simma, A., Schlich, R., Axhausen, K.: Destination Choice Modelling of Leisure Trips: The Case of Switzerland. Arbeitsberichte Verkehrs-und Raumplanung, 99 (2001)Google Scholar
  44. U.S. Census Bureau: Demographic profiles: 100-Percent and Sample Data . (2000). http://www.census.gov/census2000/demoprofiles.html
  45. van Middelkoop, M., Borgers, A., Timmermans, H.: Merlin: microsimulation system for predicting leisure activity travel patterns. Transp. Res. Rec. 1894, 20–27 (2004)CrossRefGoogle Scholar
  46. Van Nostrand, C.: A Discrete-Continuous Modeling Framework for Long-Distance, Leisure Travel Demand Analysis. Master’s Degree in Civil Engineering, University of South Florida, Tampa, 2011Google Scholar
  47. VisitUSA.com: State Hotel Guide (2011) http://www.visitusa.com/state-hotels/index.htm
  48. Vortish, P., Wabmuth, V.: VALIDATE—A nationwide dynamic travel demand model for Germany. Paper Presented at the Transportation Research Board Planning Applications Conference, Daytona, FL, 2007Google Scholar
  49. Winwaed Software Technology: Mile Charter (Version 2.12) (2009)Google Scholar
  50. Woodside, A., Lysonski, S.: A general model of traveler destination choice. J. Trav. Res. 27(4), 8–14 (1989)CrossRefGoogle Scholar
  51. Yao, E., Morikawa, T.: A Study on Intercity Travel Demand Model. Paper Presented at the 10th conference on travel behavior research Lucerne (2003)Google Scholar
  52. Zhang, L.: Multimodal inter-regional origin-destination demand estimation: A review of methodologies and their applicability to national-level passenger travel analysis in the U.S. Paper Presented at the 2010 World Conference on Transport Research, Lisbon (2010)Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2012

Authors and Affiliations

  • Caleb Van Nostrand
    • 1
  • Vijayaraghavan Sivaraman
    • 2
  • Abdul Rawoof Pinjari
    • 2
    Email author
  1. 1.Department of Civil and Environmental EngineeringUniversity of South FloridaTampaUSA
  2. 2.Department of Civil and Environmental EngineeringUniversity of South FloridaTampaUSA

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