IDEAS home Printed from https://ideas.repec.org/a/kap/transp/v28y2001i1p33-54.html
   My bibliography  Save this article

A structural equations analysis of commuters' activity and travel patterns

Author

Listed:
  • Arun Kuppam
  • Ram Pendyala

Abstract

An exploratory analysis of commuters' activity and travel patterns was carried out using activity-based travel survey data collected in the Washington, DC metropolitan area to investigate and estimate relationships among socio-demographics, activity participation, and travel behavior. Structural equations modeling methodology was adopted to determine the structural relationships among commuters' demographics, activity patterns, trip generation, and trip chaining information. Three types of structural equations model systems were estimated: one that models relationships between travel and activity participation, another that captures trade-offs between in-home and out-of-home activity durations, and a third that models the generation of complex work trip chains. The model estimation results show that strong relationships do exist among commuters' socio-demographic characteristics, activity engagement information, and travel behavior. The finding that significant trade-offs exist between in-home and out-of-home activity participation is noteworthy in the context of in-home vs. out-of-home substitution effects. Virtually all of the results obtained in this paper corroborate earlier findings reported in the literature regarding relationships among time use, activity participation, and travel. Copyright Kluwer Academic Publishers 2001

Suggested Citation

  • Arun Kuppam & Ram Pendyala, 2001. "A structural equations analysis of commuters' activity and travel patterns," Transportation, Springer, vol. 28(1), pages 33-54, February.
  • Handle: RePEc:kap:transp:v:28:y:2001:i:1:p:33-54
    DOI: 10.1023/A:1005253813277
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1005253813277
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ryuichi Kitamura & Cynthia Chen & Ram Pendyala & Ravi Narayanan, 2000. "Micro-simulation of daily activity-travel patterns for travel demand forecasting," Transportation, Springer, vol. 27(1), pages 25-51, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Cynthia & Mokhtarian, Patricia L, 2005. "An Exploratory Study Using an AIDS Model For Tradeoffs Between Time Allocations to Maintenance Activities/Travel and Discretionary Activities/Travel," Institute of Transportation Studies, Working Paper Series qt2wr907nc, Institute of Transportation Studies, UC Davis.
    2. Andre de Palma & Fay Dunkerley & Stef Proost, 2010. "Trip Chaining: Who Wins Who Loses?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 19(1), pages 223-258, March.
    3. Iragaël Joly & Karl Littlejohn & Vincent Kaufmann, 2006. "La croissance des budgets-temps de transport en question : nouvelles approches," Post-Print halshs-00174992, HAL.
    4. Chen, Cynthia, 2005. "Feasible Activity and Travel Time Allocations with a Discrete Choice Model: An Exploratory Study," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 44(2).
    5. Lee, Yuhwa & Hickman, Mark & Washington, Simon, 2007. "Household type and structure, time-use pattern, and trip-chaining behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(10), pages 1004-1020, December.
    6. Yu Ding & Huapu Lu & Lei Zhang, 2016. "An analysis of activity time use on vehicle usage rationed days," Transportation, Springer, vol. 43(1), pages 145-158, January.
    7. Subbarao, S.S.V. & Krishna Rao, K,V., 2013. "Trip Chaining Behavior in Developing Countries: A Study of Mumbai Metropolitan Region, India," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 53, pages 1-7.
    8. de Graaff, Thomas & Rietveld, Piet, 2007. "Substitution between working at home and out-of-home: The role of ICT and commuting costs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(2), pages 142-160, February.
    9. Lee, Yuhwa & Washington, Simon & Frank, Lawrence D., 2009. "Examination of relationships between urban form, household activities, and time allocation in the Atlanta Metropolitan Region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 360-373, May.
    10. Golob, Thomas F., 2001. "Structural Equation Modeling For Travel Behavior Research," University of California Transportation Center, Working Papers qt8pb2m1pk, University of California Transportation Center.
    11. Kang, Hejun & Scott, Darren M., 2010. "Exploring day-to-day variability in time use for household members," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(8), pages 609-619, October.
    12. Golob, Thomas F., 2003. "Structural equation modeling for travel behavior research," Transportation Research Part B: Methodological, Elsevier, vol. 37(1), pages 1-25, January.
    13. Yu Ding & Huapu Lu & Lei Zhang, 2016. "An analysis of activity time use on vehicle usage rationed days," Transportation, Springer, vol. 43(1), pages 145-158, January.
    14. Xuemei Fu & Zhicai Juan, 2016. "Empirical analysis and comparisons about time-allocation patterns across segments based on mode-specific preferences," Transportation, Springer, vol. 43(1), pages 37-51, January.
    15. M. Manoj & Ashish Verma, 2017. "A structural equation model based analysis of non-workers’ activity-travel behaviour from a city of a developing country," Transportation, Springer, vol. 44(2), pages 241-269, March.
    16. Xuemei Fu & Zhicai Juan, 2016. "Empirical analysis and comparisons about time-allocation patterns across segments based on mode-specific preferences," Transportation, Springer, vol. 43(1), pages 37-51, January.
    17. Su, EnDer & Fen, Yu-Gin, 2011. "Applying the structural equation model rule-based fuzzy system with genetic algorithm for trading in currency market," MPRA Paper 35474, University Library of Munich, Germany.
    18. Weis, Claude & Axhausen, Kay W., 2009. "Induced travel demand: Evidence from a pseudo panel data based structural equations model," Research in Transportation Economics, Elsevier, vol. 25(1), pages 8-18.
    19. Querini, Florent & Benetto, Enrico, 2014. "Agent-based modelling for assessing hybrid and electric cars deployment policies in Luxembourg and Lorraine," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 149-161.
    20. Golob, Thomas F., 2011. "Structural Equation Modeling For Travel Behavior Research," University of California Transportation Center, Working Papers qt2pn5j58n, University of California Transportation Center.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:transp:v:28:y:2001:i:1:p:33-54. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.