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Unconditional and conditional competing risk models of activity duration and activity sequencing decisions: An empirical comparison

Author

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  • Peter T.L. Popkowski Leszczyc

    (University of Alberta, Department of Marketing, Business, Economic and Law, Edmonton, T6G 2R6, Canada)

  • Harry Timmermans

    (Eindhoven University of Technology, Urban Planning Group, PO Box 513, 5600 MB Eindhoven, The Netherlands)

Abstract

. This paper reports the results of an empirical comparison of various types of competing risk models in predicting the timing and duration of activities. In particular, three types of models are compared: a non-competing risk model, an unconditional competing risk model, and a conditional competing risk model. The models are applied to an activity diary, collected in the Netherlands. The results of the comparison indicate that the conditional competing risk model performs best, indicating that the choice and timing of activities depends on the nature and duration of the activity conducted previously. The specific structure of these dependent transition probabilities are discussed in detail. Several socio-demographic variables are found to be significantly related to the transition probabilities.

Suggested Citation

  • Peter T.L. Popkowski Leszczyc & Harry Timmermans, 2002. "Unconditional and conditional competing risk models of activity duration and activity sequencing decisions: An empirical comparison," Journal of Geographical Systems, Springer, vol. 4(2), pages 157-170, June.
    • Handle: RePEc:kap:jgeosy:v:4:y:2002:i:2:d:10.1007_s101090200083
    DOI: 10.1007/s101090200083
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    Citations

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    Cited by:

    1. Linda Nijland & Theo Arentze & Harry Timmermans, 2014. "Multi-day activity scheduling reactions to planned activities and future events in a dynamic model of activity-travel behavior," Journal of Geographical Systems, Springer, vol. 16(1), pages 71-87, January.
    2. Allahviranloo, Mahdieh & Aissaoui, Leila, 2019. "A comparison of time-use behavior in metropolitan areas using pattern recognition techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 271-287.
    3. Allahviranloo, Mahdieh & Recker, Will, 2013. "Daily activity pattern recognition by using support vector machines with multiple classes," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 16-43.
    4. Lee, Backjin & Timmermans, Harry J.P., 2007. "A latent class accelerated hazard model of activity episode durations," Transportation Research Part B: Methodological, Elsevier, vol. 41(4), pages 426-447, May.
    5. Han, Gain & Sohn, Keemin, 2016. "Activity imputation for trip-chains elicited from smart-card data using a continuous hidden Markov model," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 121-135.
    6. Tai-Yu Ma & Charles Raux & Eric Cornelis & Iragaël Joly, 2009. "multi-state non-homogeneous semi-markov model of daily activity type, timing and duration sequence," Post-Print halshs-00310900, HAL.
    7. Tai-Yu Ma & Iragaël Joly & Charles Raux, 2010. "A shared frailty semi-parametric markov renewal model for travel and activity time-use pattern analysis," Working Papers hal-00477695, HAL.
    8. Yuke Wang & Christine L. Moe & Peter F. M. Teunis, 2018. "Children Are Exposed to Fecal Contamination via Multiple Interconnected Pathways: A Network Model for Exposure Assessment," Risk Analysis, John Wiley & Sons, vol. 38(11), pages 2478-2496, November.
    9. Iragaël Joly & Karl Littlejohn & Vincent Kaufmann, 2006. "La croissance des budgets-temps de transport en question : nouvelles approches," Post-Print halshs-00174992, HAL.
    10. Gaofeng Gu & Tao Feng & Dujuan Yang & Harry Timmermans, 2021. "Modeling dynamics in household car ownership over life courses: a latent class competing risks model," Transportation, Springer, vol. 48(2), pages 809-829, April.

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