Skip to main content
SpringerLink
Log in

What is responsible for the response lag of a significant change in discretionary time use: the built environment, family and social obligations, temporal constraints, or a psychological delay factor?

  • Published:
Transportation Aims and scope Submit manuscript

Abstract

In this paper, we used the 10-wave Puget Sound Panel Dataset to investigate the response lag of a significant change in discretionary time use. In particular, we want to quantify the relative magnitude of the following factors: the built environment, family and social obligations, temporal constraints, or a psychological delay factor (people delay a behavioral change until the next life shock). To answer this question, we developed a survival model to treat (1) left-censoring, (2) partial observation, and (3) multi-type exits. The results suggest that family and social obligations, as well as temporal constraints, appear to play a more important role than the built environment. Support for the psychological delay factor is not evident. We also found that the probability of having a significant change in discretionary time use is negatively related to time progression, supporting the human adaptivity hypothesis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. The use of Weibull form is appropriate, as it allows for either an increasing or decreasing hazard rate function with respect to time.

  2. For a continuous integration\( \int\nolimits_{- \infty }^{\infty } {g\left( s \right)ds} , \) the value of integration is infinitive when g(s) is constant for \( s \in \left( { - \infty ,\infty } \right). \) Since human’s life is usually less than 100 years, it is reasonable to assume the density function of starting time of home location change is constant when \( s \in \left[ { - 100,100} \right] \) and zero when \( s \in \left( { - \infty , - 100} \right) \cup \left( {100,\infty } \right). \) Please note that here “−100” means 100 years prior to study period.

  3. The trip purposes used in the two surveys were slightly different. For wave 1 to 7, the following trip purposes were recorded: work, shopping, school, visiting, free-time, personal, appointments, and home. Among them, visiting, free-time, and personal were classified as discretionary. For wave 8 to 10, the following trip purposes were recorded: home, commute to work, other work-related travel, commute to school, taking child to/from school, lesson, etc., visiting friends or family, errands/picking up/dropping of others, doctor appointments/other medical-related, delivery, shopping, dining/coffee, etc., recreation/exercise/lessons/personal business, volunteering, going to another travel mode, other appointment or meeting, meet/pick up/drop off carpoolers, linked commute trip to/from work, commute from work, commute from college, commute from school, and other. Among them, visiting friends or family, dining/coffee, etc., recreation/exercise/lessons/personal business, and volunteering are classified as discretionary. The time allocation to discretionary activities and associated trips is calculated as the sum of both recorded days in each wave.

  4. A common observation period does not mean that every subject must first participate in the survey in the same year.

  5. Group 1 individuals are the only group on whom we do not need to strictly impose the 5-year study period. This is because as long as a significant change in the time allocation is observed within 5 years, we can calculate the response lag. For example, a person participates from waves 1 to 3. In wave 2, we observe a home location change and in wave 3 we observe a significant change in time allocation as compared to wave 2. Then, the response lag can be calculated as 1 year, even though the person’s total participation in the survey is less than 5 years.

  6. Discretionary facilities include: indoor recreational opportunities (e.g., bars, movie theaters, libraries, museums), indoor sports opportunities (e.g., bowling, swimming pools, health clubs), outdoor recreational opportunities (e.g., zoo, parks, resorts), outdoor sports opportunities (e.g., golf), and religious related opportunities (e.g., churches, temples).

  7. Kockelman and Zhang used land area.

  8. For wave 1 to 7, mandatory activities include those activities whose trip purposes are work or school; for wave 8 to 10, mandatory activities include those activities whose trip purposes are: commute to work, other work-related travel, commute to school, and commute to college are defined as mandatory activities. For maintenance activities, it includes shopping or appointment for wave 1 to 7 and taking child to/from school, lesson, etc., errands/picking up/dropping off others, doctor appointment/other medical-related, delivery, shopping, other appointment/meeting, and meet/pick up/drop off carpoolers for wave 8 to 10.

References

  • Amemiya, T.: A note on left censoring. In: Hsiao, C., Pesaran, M.H., Lahiri, K., Lee, L.-F. (eds.) Analysis of Panels and Limited Dependent Variable Models, pp. 7–22. Cambridge University Press, Cambridge (1999)

    Google Scholar 

  • Banister, D.: The influence of habit formation on modal choice—a Heuristic Model. Transportation 7, 19–33 (1978)

    Article  Google Scholar 

  • Bhat, R.C.: A generalized multiple durations proportional Hazard Model with an application to activity behavior during the evening work-to-home commute. Transp. Res. Part B 30(6), 465–480 (1996)

    Article  Google Scholar 

  • Buhaug, H.: Dangerous Dyads revisited: democracies may not be that peaceful after all. Conflict Manage. Peace Sci. 22(2), 95–111 (2005)

    Article  Google Scholar 

  • Chen, C., Lin, J.: Making an informed vehicle scrappage decision. Trans. Rev. 26(6), 731–748 (2006)

    Article  Google Scholar 

  • Chen, C., McKnight, C.: Does the built environment make a difference? Additional evidence from the daily activity and travel behavior of homemakers living in NYC and Suburbs. J. Transp. Geogr. 15(6), 380–395 (2007)

    Article  Google Scholar 

  • Chen, C., Mokhtarian, P.: Tradeoffs between time allocations to maintenance activities/travel and discretionary activities/travel. Transportation 33, 223–240 (2006)

    Article  Google Scholar 

  • Chen, C., Niemeier, D.: A mass point vehicle scrappage model. Transp. Res. Part B 39(5), 401–415 (2005)

    Article  Google Scholar 

  • Chen, C., Garling, T., Kitamura, R.: Activity rescheduling: deliberate or habitual? Transp. Res. Part F 7(6), 351–371 (2004)

    Google Scholar 

  • Chen, C., Gong, H., Paaswell, R.: Role of the built environment on mode choice decisions: additional evidence on the impact of density. Transportation 35(3), 285–299 (2008)

    Article  Google Scholar 

  • Clarke, M., Dix, M., Goodwin, P.: Some issues of dynamics in forecasting travel behavior – a discussion paper. Transportation 11(2), 153–172 (1982)

    Article  Google Scholar 

  • Coombs, C.H., Dawes, R.W., Tversky, A.: Mathematical Psychology, An Elementary Introduction. Prentice-Hall, Englewood Cliffs, NJ (1970)

    Google Scholar 

  • Diamond, P., Hausman, J.: The retirement and unemployment behavior of older men. In Aaron, H., Burtless G. (eds.) Retirement and Economic Behavior. Brookings Institute, Washington DC, pp. 97–134 (1984)

  • Doohee, N., Fred, M.: An exploratory hazard-based analysis of highway incident duration. Transp. Res.: Part A: Policy Pract. 34(2), 85–102 (2000)

    Article  Google Scholar 

  • Fasolo, B., Misuraca, R., McClelland, G.H.: Individual differences in adaptive choice strategies. Research in Economics 57(3), 219–233 (2003)

    Article  Google Scholar 

  • Freda, C., Sudipto, B., Bradley, P.C., Debajyoti, S.: Flexible cure rate modeling under latent activation schemes. J. Am. Stat. Assoc. 102(478), 560–572 (2007)

    Article  Google Scholar 

  • Gilbert, C.: A duration model of automobile ownership. Transp. Res. Part B 26(2), 222–242 (1992)

    Google Scholar 

  • Golob, T.F.: The Dynamics of Household Travel Time Expenditures and Car Ownership Decisions. The University of California Transportation Center, (1990)

  • Goodwin, P.B.: Family changes and public transport use 1984–1987: a dynamic analysis using panel data. Transportation 16, 121–154 (1989)

    Article  Google Scholar 

  • Greene, W.H.: Econometric Analysis. Prentice Hall, Upper Saddle River, New Jersey (2003)

    Google Scholar 

  • Hagerstrand, T.: What about people in regional science? Reg. Sci. Assoc. 24, 7–21 (1970)

    Google Scholar 

  • Hachen, S.D.: The competing risks model. A method for analyzing processes with multiple types of events. Sociol. Methods Res. 17(1), 21–54 (1988)

    Article  Google Scholar 

  • Hanaki, N.: Individual and social learning. Comput. Econ. 26(3–4), 213–232 (2005)

    Google Scholar 

  • Han, A., Hausman, J.A.: Flexible parametric estimation of duration and competing risk models. J. Appl. Econom. 5, 1–28 (1990)

    Article  Google Scholar 

  • Hensher, D., Johnson, L.: Application of Multinomial Probit to a Time Series of Cross-sections Data Set. Conference on Disaggregate Travel Demand Models, Leeds University (1980)

  • Hensher, D., Mannering, F.: Hazard-based duration models and their application to transport analysis. Transp. Rev. 14(1), 63–82 (1994)

    Article  Google Scholar 

  • Joly, I.: Stability or regularity of the daily travel time in lyon? Application of a duration model. Int. J. Transp. Econ. 33(3), 369–400 (2006)

    Google Scholar 

  • Kitamura, R.: A causal analysis of car ownership and transit use. Transportation 16, 155–173 (1989)

    Article  Google Scholar 

  • Kitamura, R.: Panel analysis in transportation planning: an overview. Transp. Res. Part A 24(6), 401–415 (1990)

    Article  Google Scholar 

  • Kitamura, R.: Time use: Workshop Report. In Mahmassani, H.S. (ed.) In Perpetual Motion – Travel Behavior Research Opportunities and Application Challenges. Elsevier Science, Limited, Oxford, UK, pp. 333–338 (2002)

  • Kitamura, R., Yamamoto, T., Fujii, S.: The effectiveness of panels in detecting changes in discrete travel behavior. Transp. Res. Part B 37(2), 191–206 (2003)

    Article  Google Scholar 

  • Kockelman, K.: Travel behavior as a function of accessibility, land use mixing and land use balance: evidence from the San Francisco Bay Area. Transp. Res. Board 1607, 116–125 (1997)

    Article  Google Scholar 

  • Krizek, K.: Residential relocation and changes in urban travel. APA J. 69(3), 265–281 (2003)

    Google Scholar 

  • Li, Y., Lin, X.: Semiparametric normal transformation models for spatially correlated survival data. J. Am. Stat. Assoc. 101(474), 591–603 (2006)

    Article  Google Scholar 

  • Lin, J., Chen, C., Niemeier, D.: An analysis on long term emission benefits of a government vehicle fleet replacement plan in Northern Illinois. Transportation 35, 219–235 (2008)

    Article  Google Scholar 

  • Ma, J., Goulias, K.G.: A dynamic analysis of person and household activity and travel patterns using data from first two waves in the puget sound transportation panel. Transportation 24, 309–331 (1996)

    Article  Google Scholar 

  • Ma, J., Goulias, K.G.: Multivariate marginal frequency analysis of activity and travel patterns in first four waves of puget sound. Transp. Res. Rec. 1556, 67–76 (1997)

    Article  Google Scholar 

  • Omariba, D., Walter, R., Beaujot, R., Rajulton, F.: Determinants of infant and child mortality in Kenya: an analysis controlling for frailty effects. Popul. Res. Policy Rev. 26(3), 299–321 (2007)

    Article  Google Scholar 

  • Pas, E.I.: State of the art and research opportunities in travel demand: another perspective. Transp. Res. A 19(5/6), 460–464 (1985)

    Article  Google Scholar 

  • Pas, E.I.: Recent advances in activity-based travel demand modeling. In Texas Transportation Institute (eds.) Activity-Based Travel Forecasting Conference, June 2–5, 1996 Summary, Recommendations, and Compendium of Papers. Travel Model Improvement Program, U.S. Department of Transportation and U.S. Environmental Protection Agency, Washington, D.C., pp. 79–102 (1996)

  • Pendyala, R.: Time use and travel behavior in space and time (Chapter 2). In: Goulias, K.G. (ed.). Transportation Systems Planning: Methods and Applications. CRC Press, Boca Raton, FL, pp. 2-1–2-37 (2003)

  • Pendyala, R.M., Goulias, K.G.: Time use and activity perspectives in travel behavior research. Transportation 29(1), 1–4 (2002)

    Article  Google Scholar 

  • Rosholm, M.: An analysis of labour market exclusion and (Re-) inclusion. Transitions in the Labour Market. Discussion Paper 332, I2A, Bonn (2001)

  • Sam, A., Ronald, S.W.: The duration of homelessness: evidence from a national survey. J. Hous. Econ. 12(4), 273–390 (2003)

    Article  Google Scholar 

  • SAS Institute Inc.: SAS Online Documentation Version 9.1.3. http://support.sas.com/onlinedoc/913/docMainpage.jsp (2002). Last Accessed 6 Oct 2006

  • Stefano, M.: Urban environment and travel times to work. Int. J. Transp. Econ. 30(2), 187–203 (2003)

    Google Scholar 

  • Tammie, X., Malpezzi, S.: Household mobility in New York City’s regulated rental housing market Simmons-Mosley. J. Hous. Econ. 15(1), 38–62 (2006)

    Article  Google Scholar 

  • Xie, Y., Giles, E.D.: A survival analysis of the approval of U.S. Patent Applications Department of Economics, University of Victoria, Econometrics, Working Papers (2007)

  • Yamamoto, T., Madre, J., Kitamura, R.: An analysis of the effects of french vehicle inspection program and grant for scrappage in household vehicle transaction. Transp. Res. Part B 389(10), 905–926 (2004)

    Google Scholar 

  • Zhang, M.: The role of land use in travel mode choice. J. Am. Plann. Assoc. 70(3), 344–360 (2004)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their sincere thanks to Mr. Neil Kilgren and other staff members at the Puget Sound Regional Council who relentlessly helped us obtain various datasets and answered our numerous questions during the long course of this study. We also want to thank Fred Mannering, Ryuichi Kitamura and four anonymous reviewers for their comments. Any remaining errors are the authors’ responsibility.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, City College of New York, W. 140th Street and Convent Avenue, New York, NY, 10031, USA

    Cynthia Chen & Jason Chen

Authors
  1. Cynthia Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jason Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cynthia Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, C., Chen, J. What is responsible for the response lag of a significant change in discretionary time use: the built environment, family and social obligations, temporal constraints, or a psychological delay factor?. Transportation 36, 27–46 (2009). https://doi.org/10.1007/s11116-008-9184-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11116-008-9184-6

Keywords

Search

Navigation