Transportation

, Volume 38, Issue 1, pp 101–122

Discretionary activity location choice: in-home or out-of-home?

Article

Abstract

This paper examines the location choice associated with discretionary activities (in-home vs. out-of-home). These substitution patterns are important in terms of travel demand as in-home activities do not necessitate travel while out-of-home activities incur travel. Mixed logit models are estimated using an activity dataset (2003 CHASE data) to analyze the factors associated with this choice at the individual activity-level. Results suggest that the attributes of an activity significantly contribute to understanding the likelihood of engaging in out-of-home activities. Activity type interaction terms reveal the varying influence that socio-demographics, activity attributes and travel have over four different activity types modeled. The results reveal that the location choice (in-home vs. out-of-home) is sensitive to travel characteristics. As the travel time and cost increases, an individual is less likely to engage in an activity out-of-home. Compared to passive and social activities, the location of active activities is more sensitive to changes in travel attributes.

Keywords

Activity location choice In-home activities Out-of-home activities Mixed logit Activity attributes 

References

  1. Akar, G., Clifton, J.K., Doherty, S.T.: Discretionary Activity Location Choice: An Examination of Temporal Dimensions and Activity Attributes of In-Home or Out-of-Home Location Choice. World Conference on Transportation Research (WCTR), UC-Berkeley (2007)Google Scholar
  2. Bastin, F., Cirillo, C., Toint, P.L.: Application of an adaptive Monte Carlo algorithm to mixed logit estimation. Transp. Res. Part B 40(7), 577–593 (2006)CrossRefGoogle Scholar
  3. Bhat, C., Lockwood, A.: On distinguishing between physically active and physically passive episodes and between travel and activity episodes: an analysis of weekend recreational participation in the San Francisco Bay area. Transp. Res. Part A Policy Pract. 38(8), 573 (2004)Google Scholar
  4. Bhat, C.R., Gossen, R.: A mixed multinomial logit model analysis of weekend recreational episode type choice. Transp. Res. Part B Methodol. 38(9), 767 (2004)Google Scholar
  5. Bhat, C.R., Koppelman, F.S.: A retrospective and prospective survey of time-use research. Transportation 26, 119–139 (1999)CrossRefGoogle Scholar
  6. Bhat, C.R., Misra, R.: Discretionary activity time allocation of individuals between in-home and out-of-home and between weekdays and weekends. Transportation 26, 193–209 (1999)CrossRefGoogle Scholar
  7. Bhat, C.R., Srinivasan, S., Sen, S.: A joint model for the perfect and imperfect substitute goods case: application to activity time-use decisions. Transp. Res. Part B Methodol. 40(10), 827–850 (2006)CrossRefGoogle Scholar
  8. Bowman, J.L., Ben-Akiva, M.E.: Activity-based disaggregate travel demand model system with activity schedules. Transp. Res. Part A. Policy Pract. 35(1), 1–28 (2001)CrossRefGoogle Scholar
  9. Cirillo, C., Axhausen, K.W.: Evidence on the distribution of values of travel time savings from a six-week diary. Transp. Res. Part A 40, 444–457 (2006)Google Scholar
  10. Cirillo, C., Axhausen, K.: Dynamic model of activity-type choice and scheduling. Transportation. doi: 10.1007/s11116-009-9218-8, available at http://www.springerlink.com/content/55j422640140424u (2009)
  11. Cirillo, C., Toint, P.L.: One Activity-Based Framework for Europe. ETC. European Transport Conference, Cambridge, UK (2002)Google Scholar
  12. Doherty, S.T.: Should we abandon activity type analysis? Redefining activities by their salient attributes. Transportation 33(6), 517–536 (2006)CrossRefGoogle Scholar
  13. Doherty, S.T., Miller, E.J., Axhausen, K.W., Gärling, T.: A conceptual model of the weekly household activity-travel scheduling process. In: Stern, E., Salomon, I., Bovy, P. (eds.) Travel Behaviour: Patterns, Implications and Modelling (2002)Google Scholar
  14. Doherty, S.T., Nemeth, E., Roorda, M., Miller, E.J.: Design and assessment of the Toronto area computerized household activity scheduling survey. J. Transp. Res. Board 1894, 140–149 (2004)CrossRefGoogle Scholar
  15. Ettema, D., Timmermans, H.J.P.: Theories and models of activity patterns. In: Dick, E., Timmermans, H.J.P. (eds.) Activity-Based Approaches to Travel Analysis. Pergamon, Oxford, UK, pp. 1–36 (1997)Google Scholar
  16. Golob, T.F., McNally, M.G.: A model of activity participation and travel interactions between household heads. Transp. Res. Part B Methodol. 31(3), 177–194 (1997)CrossRefGoogle Scholar
  17. Greene, W.H.: Econometric analysis. Pearson Education Inc., India (2003)Google Scholar
  18. Handy, S., Yantis, T.: The impacts of telecommunications technologies on nonwork travel behavior. SWUTC/97/721927–1F, Southwest Region University Transportation Center, Center for Transportation Research, The University of Texas at Austin (1997)Google Scholar
  19. Kemperman, A., Arentze, T., Timmermans, H.: Social commitments and activity-travel scheduling decisions. Transp. Res. Rec. 1977(2006), 242–249 (2006)CrossRefGoogle Scholar
  20. Kitamura, R.: An evaluation of activity-based travel analysis. Transportation 15(1/2), 9–34 (1988)Google Scholar
  21. Lu, X., Pas, E.I.: Socio-demographics, activity participation and travel behavior. Transp. Res. Part A (Policy and Practice) 33A(1), 1–18 (1999)CrossRefGoogle Scholar
  22. McFadden, D., Train, K.: Mixed multinomial logit models of discrete response. J. Appl. Econom. 15, 447–470 (2000)CrossRefGoogle Scholar
  23. McNally, M.G.: The activity-based Approach. Paper UCI-ITS-AS-WP-00-4, Institute of Transportation Studies Center for Activity Systems Analysis, University of California, Irvine (2000)Google Scholar
  24. Miller, E.J., Roorda, M.J.: A prototype model of 24-hour household activity scheduling for the Toronto area. Transp. Res. Rec. 1831, 114–121 (2003)CrossRefGoogle Scholar
  25. Mokhtarian, P.L., Salomon, I.: How derived is the demand for travel? Some conceptual and measurement considerations. Transp. Res. A 35(8), 695–719 (2001)Google Scholar
  26. Mokhtarian, P.L., Salomon, I.: Emerging travel patterns: do telecommunications make a difference?. In: Mahmassani, H.S. (ed.) In Perpetual Motion: Travel Behaviour Research Opportunities and Application Challenges. Pergamon Press/Elsevier, Oxford, UK, pp. 143–182 (2002)Google Scholar
  27. Mokhtarian, P.L., Salomon, I., Handy, S.L.: The impacts of ICT on leisure activities and travel: a conceptual exploration. Transportation 33, 263–289 (2006)CrossRefGoogle Scholar
  28. Pas, E.I.: Recent advances in activity-based travel demand modeling. In: Activity-Based Travel Forecasting Conference, June 2–5, 1996: Summary, Recommendations and Compendium of Papers. New Orleans, LA, pp.79–102 (1997)Google Scholar
  29. Pas, E.I., Harvey, A.S.: Time use research and travel demand analysis and modeling. In: Stopher, P., Gosselin, M.L. (eds.) Understanding Travel Behavior in an Era of Change. Elsevier Science Ltd., Oxford (1996)Google Scholar
  30. Passmore, A., French, D.: Development and administration of a measure to assess adolescents’ participation in leisure activities. Adolescence 36(141), 67–75 (2001)Google Scholar
  31. Redmond, L.S., Mokhtarian, P.L.: The positive utility of the commute: modeling ideal commute time and relative desired commute amount. Transportation 28(2), 179–205 (2001)CrossRefGoogle Scholar
  32. Reichman, S.: Travel adjustments and life styles: a behavioral approach. In: Stopher, P., Meyburg, A. (eds.) Behavioral Travel Models. Lexington Books, Lexington, Massachusetts (1976)Google Scholar
  33. Roorda, M.J., Miller, E.J., Habib, K.: Validation of TASHA: a 24-hour activity scheduling microsimulation model. 86th Annual Meeting of the TRB (Transportation Research Board), Washington, DC (2006)Google Scholar
  34. Train, K.: Discrete Choice Methods with Simulation, Cambridge University Press, Cambridge (2002)Google Scholar
  35. Yamamoto, T., Kitamura, R.: An analysis of time allocation to in-home and out-of-home discretionary activities across working days and non-working days. 26, 211–230 (1999)Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • Gulsah Akar
    • 1
  • Kelly J. Clifton
    • 2
  • Sean T. Doherty
    • 3
  1. 1.City and Regional Planning, Knowlton School of ArchitectureThe Ohio State UniversityColumbusUSA
  2. 2.Civil and Environmental EngineeringPortland State UniversityPortlandUSA
  3. 3.Department of Geography and Environmental StudiesWilfrid Laurier UniversityWaterlooCanada

Personalised recommendations