A Probit-based Joint Discrete-continuous Model System: Analyzing the Relationship between Timing and Duration of Maintenance Activities
This paper presents a probit-based discrete-continuous modeling methodology to analyze relationships between discrete and continuous choice dimensions often encountered in activity-travel behaviour research. The probit-based approach allows one to adopt a flexible multivariate normally distributed error covariance structure that overcomes the limitations associated with other discrete-continuous modeling methods that rely on two-step limited information techniques or restrictive distributional transformations. The paper presents the detailed formulation of the modeling methodology and demonstrates its applicability through an analysis of the relationship between the timing (scheduling) and duration of household maintenance activities that include shopping and personal business activity episodes. A new non-nested test developed by the authors is used to compare alternative model structures and identify the nature of the joint relationship between the timing and duration of maintenance activities. Models are estimated separately for commuter and non-commuter samples drawn from the 2000 Switzerland Microcensus of Travel. Model estimation results show that error covariances are significant for commuter models of maintenance activity timing and duration. Non-nested model comparisons indicate that the model specification where time-of-day choice affects activity duration offers a statistically superior goodness-of-fit in comparison to the model specification where activity duration affects time-of-day choice. These findings lend credence to the notion that the joint relationship between timing and duration adopted in activity-based model systems should be one in which the activity schedule or agenda drives activity time allocation or duration.
KeywordsDiscrete Choice Maintenance Activity Activity Duration Discrete Choice Model Mixed Logit
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- Ettema, D., Borgers, A.W.J. and Timmermans, H.J.P. (1993). Simulation model of activity scheduling behavior. Transportation Research Record, 1413, 1-11.Google Scholar
- Golledge, R.G., Kwan, M.P. and Gärling, T. (1994). Computational process modeling of travel decisions using geographical information systems. Regional Science, 73-99.Google Scholar
- Kitamura, R., Yamamoto, T., Fujii, S. and Sampath, S. (1996). A discrete-continuous analysis of time allocation to two types of discretionary activities which accounts for unobserved heterogeneity. Transportation and Traffic Theory, 431-453.Google Scholar
- Pas, E.I. and Harvey, A.S. (1997). Time use research and travel demand analysis and modeling. Understanding Travel Behavior in an Era of Change, 315-338.Google Scholar
- Pinjari, A.R., Pendyala, R.M., Bhat, C.R. and Waddell, P.A. (2008). Modeling the choice continuum: an integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions. Proceedings of the 87th Annual Meeting of the Transportation Research Board, National Research Council, Washington, D.C.Google Scholar
- Ye, X. and Pendyala, R.M. (2007). Analysis of relationship between timing and duration of maintenance activities using alternative joint discrete-continuous modeling systems. Proceedings of the 86th Annual Meeting of the Transportation Research Board, National Research Council, Washington, D.C.Google Scholar
- Ye, X. and Pendyala, R.M. (2009). A probit-based joint discrete-continuous model system: analyzing the relationship between timing and duration of maintenance activities. Proceedings of the 88th Annual Meeting of the Transportation Research Board, National Research Council, Washington, D.C.Google Scholar
- Yee, J.L. and Niemeier, D.A. (2000). Analysis of activity duration using the Puget sound transportation panel. Transportation Research Part A, 34(8), 607-624.Google Scholar