, Volume 38, Issue 1, pp 45–63 | Cite as

The validity of using activity type to structure tour-based scheduling models

  • Sean T. Doherty
  • Abolfazl Mohammadian


A unique set of activity scheduling data is utilized in this paper to provide much needed empirical analysis of the sequence in which activities are planned in everyday life. This is used to assess the validity of the assumption that activities are planned in accordance to a fixed hierarchy of activity types: mandatory activities first (work/school), followed by joint maintenance, joint discretionary, allocated maintenance, and individual discretionary activities. Such an assumption is typical of current generation activity and tour-based travel demand models. However, the empirical results clearly do not support such assumptions. For instance, fewer than 50% of mandatory activities were actually planned first in related out-of-home tours; remaining activity types also did not take any particular precedence in the planning sequence. Given this, a search was made for the more salient attributes of activities (beyond activity type) that would better predict how they are planned within tours. Several ordered response choice models for different tour sizes were developed for this purpose, predicting the choice order of the 1st, 2nd, 3rd, etc. planned activity in the tour as a function of activity type, activity characteristics (duration, frequency, travel time, and involved persons), and individual characteristics. Activity duration played the most significant role in the models compared to any other single variable, wherein longer duration activities tended to be planned much earlier in tours. This strongly suggests that the amount of time-use, rather than the nature of the event as indicated by activity type, is a primary driver of within-tour planning order and offers potential for a much improved and valid fit.


Activity scheduling Tours Mandatory activities Discretionary activities Ordered choice model 



The authors would like to acknowledge the generous financial support received for this project from the Social Sciences and Humanities Research Council of Canada and from the GEOIDE (Geomatics for Informed Decisions) Network of Centres of Excellence Program of the Canadian federal research councils. Special thanks go to Joshua Auld for his assistance in table preparation, to the diligent field workers who collected the data, and all those who generously supplied their time in completing the survey.


  1. Arentze, T.A., Timmermans, H.J.P.: ALBATROSS: A Learning Based Transportation Oriented Simulation System. The European Institute of Retailing and Services Studies, Eindhoven, The Netherlands (2000)Google Scholar
  2. Bhat, C.R., Pulugurta, V.: A comparison of two alternative behavioral choice mechanisms for household auto ownership decisions. Transp. Res. B 32, 61–77 (1998)CrossRefGoogle Scholar
  3. 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
  4. Bowman, J.L., Bradley, M., Shiftan, Y., Lawton, K.T., Ben-Akiva, M: demonstration of an activity-based model system for Portland. In: 8th World Conference on Transport Research, Antwerp, Belgium (1998)Google Scholar
  5. Data Management Group: Transportation Tomorrow Survey 2001: Design and Conduct of the Survey. Joint Program in Transportation, University of Toronto, Toronto (2003)Google Scholar
  6. Davidson, W., Donnelly, R., Vovsha, P., Freedman, J., Ruegg, S., Hicks, J., Castiglione, J., Picado, R.: Synthesis of first practices and operational research approaches in activity-based travel demand modeling. Transp. Res. A 41(5), 464–488 (2007)Google Scholar
  7. Doherty, S.T.: Rules for assessing activity scheduling survey respondents’ data quality. J. Transp. Res. Board 1870, 109–115 (2004)CrossRefGoogle Scholar
  8. Doherty, S.T.: Interactive methods for activity scheduling processes. In: Goulias, K. (ed.) Transportation Systems Planning: Methods And Applications, pp. 7-1–7-25. CRC Press, New York (2002)Google Scholar
  9. Doherty, S.T.: How far in advance are activities planned? Measurement challenges and analysis. J. Transp. Res. Board 1926, 41–49 (2005)Google Scholar
  10. Doherty, S.T., Miller, E.J.: A computerized household activity scheduling survey. Transportation 27(1), 75–97 (2000)CrossRefGoogle Scholar
  11. 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
  12. Ettema, D., Borgers, A., Timmermans, H.: Simulation model of activity scheduling behavior. Transp. Res. Rec. 1413, 1–11 (1993)Google Scholar
  13. Gärling, T., Kalén, T., Romanus, J., Selart, M.: Computer simulation of household activity scheduling. Environ. Plan. A 30, 665–679 (1998)CrossRefGoogle Scholar
  14. Goulias, K.G., Bradley, M., Noronha, V., Golledge, R., Vovsha, P.S.: Data needs for innovative modelling workshop. In: National Household Travel Survey Conference: Understanding our Nation’s Travel, Washington D.C., November 1–2. (2004)
  15. Greene, W.: Limdep Version 8.0, Econometric Modeling Guide. Econometric Software, Inc, Plainview, NY (2002)Google Scholar
  16. Greene, W.: Econometric Analysis. Prentice Hall, London (2003)Google Scholar
  17. Kemperman, A., Borgers, A., Oppewal, H., Timmermans, H.: Predicting the duration of theme park visitors’ activities: an ordered logit model using conjoint choice data. J. Travel Res. 41(4), 375–384 (2003)CrossRefGoogle Scholar
  18. Kitamura, R., Chen, C., Pendyala, R.M., Narayanan, R.: Micro-simulation of daily activity-travel patterns for travel demand forecasting. Transportation 27(1), 25–51 (2000)CrossRefGoogle Scholar
  19. Kockelman, K., Zhao, Y., Blanchard-Zimmerman, C.: Meeting the intent of ADA in sidewalk cross-slope design. J. Rehabil. Res. Dev. 38(1), 101–110 (2001)Google Scholar
  20. Limanond, T., Niemeier, D.A., Mokhtarian, P.L.: Specification of a tour-based neighborhood shopping model. Transportation 32, 105–134 (2005)CrossRefGoogle Scholar
  21. Miller, E.J., Roorda, M.J.: A prototype model of household activity/travel scheduling. J.Transp. Res. Board 1831, 114–121 (2003)CrossRefGoogle Scholar
  22. Mohammadian, A., Doherty, S.T.: A mixed logit model of activity scheduling time horizon incorporating spatial-temporal variables. J. Transp. Res. Board 1926, 33–40 (2005)CrossRefGoogle Scholar
  23. Mohammadian, A., Doherty, S.T.: Modeling activity scheduling time horizon: duration of time between planning and execution of pre-planned activities. Transp. Res. A 40(6), 475–490 (2006)Google Scholar
  24. Shiftan, Y.: Practical approach to model trip chaining. Transp. Res. Rec. 1645, 17–23 (1998)CrossRefGoogle Scholar
  25. Stinson, M.A., Bhat, C.: Frequency of bicycle commuting: internet-based survey analysis. Transp. Res. Rec. 1878, 122–130 (2004)CrossRefGoogle Scholar
  26. Vovsha, P., Bradley, M., Bowman, J.: Activity-based travel forecasting models in the United States: progress since 1995 and prospects for the future. In: Timmermans, H. (ed.) Progress in Activity-Based Analysis, pp. 389–414. Oxford, Elsevier (2005)CrossRefGoogle Scholar
  27. Yagi, S., Mohammadian, A.: An activity-based microsimulation model of travel demand in the Jakarta metropolitan area. J. Choice Model. 3(1), 32–57 (2010)Google Scholar
  28. Zaviona, W., McElevy, R.D.: A statistical model for the analysis of ordinal level dependent variables. J. Math. Soc. 4, 103–120 (1975)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  1. 1.Department of Geography and Environmental StudiesWilfrid Laurier UniversityWaterlooCanada
  2. 2.Department of Civil and Materials EngineeringUniversity of Illinois at ChicagoChicagoUSA

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