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A multiple discrete-continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays

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  • Pinjari, Abdul Rawoof
  • Bhat, Chandra

Abstract

This paper develops a multiple discrete-continuous nested extreme value (MDCNEV) model that relaxes the independently distributed (or uncorrelated) error terms assumption of the multiple discrete-continuous extreme value (MDCEV) model proposed by Bhat [Bhat, C.R., 2005. A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions. Transportation Research Part B 39 (8), 679-707; Bhat, C.R., 2008. The multiple discrete-continuous extreme value (MDCEV) model: role of utility function parameters, identification considerations, and model extensions. Transportation Research Part B 42 (3), 274-303]. The MDCNEV model captures inter-alternative correlations among alternatives in mutually exclusive subsets (or nests) of the choice set, while maintaining the closed-form of probability expressions for any (and all) consumption pattern(s). The MDCNEV model is applied to analyze non-worker out-of-home discretionary activity time-use and activity timing decisions on weekdays using data from the 2000 San Francisco Bay Area data. This empirical application contributes to the literature on activity time-use and activity timing analysis by considering daily activity time-use behavior and activity timing preferences in a unified utility maximization-based framework. The model estimation results provide several insights into the determinants of non-workers' activity time-use and timing decisions. The MDCNEV model performs better than the MDCEV model in terms of goodness of fit. However, the nesting parameters are very close to 1, indicating low levels of correlation. Nonetheless, even with such low correlation levels, empirical policy simulations indicate non-negligible differences in policy predictions and substitution patterns exhibited by the two models. Experiments conducted using simulated data also corroborate this result.

Suggested Citation

  • Pinjari, Abdul Rawoof & Bhat, Chandra, 2010. "A multiple discrete-continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 562-583, May.
    • Handle: RePEc:eee:transb:v:44:y:2010:i:4:p:562-583
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    1. Small, Kenneth A, 1987. "A Discrete Choice Model for Ordered Alternatives," Econometrica, Econometric Society, vol. 55(2), pages 409-424, March.
    2. Arentze, Theo A. & Timmermans, Harry J. P., 2004. "A learning-based transportation oriented simulation system," Transportation Research Part B: Methodological, Elsevier, vol. 38(7), pages 613-633, August.
    3. Goulias, Konstadinos G. & Henson, Kriste M., 2006. "On Altruists and Egoists in Activity Participation and Travel: Who are they and do they live together?," University of California Transportation Center, Working Papers qt0p36z3r0, University of California Transportation Center.
    4. Fang, Hao Audrey, 2008. "A discrete-continuous model of households' vehicle choice and usage, with an application to the effects of residential density," Transportation Research Part B: Methodological, Elsevier, vol. 42(9), pages 736-758, November.
    5. Chandra Bhat & Rajul Misra, 1999. "Discretionary activity time allocation of individuals between in-home and out-of-home and between weekdays and weekends," Transportation, Springer, vol. 26(2), pages 193-229, May.
    6. Sivaramakrishnan Srinivasan & Chandra Bhat, 2006. "A multiple discrete-continuous model for independent- and joint-discretionary-activity participation decisions," Transportation, Springer, vol. 33(5), pages 497-515, September.
    7. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    8. Ettema, Dick & Bastin, Fabian & Polak, John & Ashiru, Olu, 2007. "Modelling the joint choice of activity timing and duration," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(9), pages 827-841, November.
    9. Joh, Chang-Hyeon & Arentze, Theo & Hofman, Frank & Timmermans, Harry, 2002. "Activity pattern similarity: a multidimensional sequence alignment method," Transportation Research Part B: Methodological, Elsevier, vol. 36(5), pages 385-403, June.
    10. Mark Bradley & Peter Vovsha, 2005. "A model for joint choice of daily activity pattern types of household members," Transportation, Springer, vol. 32(5), pages 545-571, September.
    11. Cynthia Chen & Patricia Mokhtarian, 2006. "Tradeoffs between Time Allocations to Maintenance Activities/Travel and Discretionary Activities/Travel," Transportation, Springer, vol. 33(3), pages 223-240, May.
    12. Wales, T. J. & Woodland, A. D., 1983. "Estimation of consumer demand systems with binding non-negativity constraints," Journal of Econometrics, Elsevier, vol. 21(3), pages 263-285, April.
    13. Pinjari, Abdul Rawoof & Bhat, Chandra R. & Hensher, David A., 2009. "Residential self-selection effects in an activity time-use behavior model," Transportation Research Part B: Methodological, Elsevier, vol. 43(7), pages 729-748, August.
    14. Ram Pendyala & Konstadinos Goulias, 2002. "Time use and activity perspectives in travel behavior research," Transportation, Springer, vol. 29(1), pages 1-4, February.
    15. Toshiyuki Yamamoto & Ryuichi Kitamura, 1999. "An analysis of time allocation to in-home and out-of-home discretionary activities across working days and non- working days," Transportation, Springer, vol. 26(2), pages 231-250, May.
    16. Bhat, Chandra R. & Sen, Sudeshna & Eluru, Naveen, 2009. "The impact of demographics, built environment attributes, vehicle characteristics, and gasoline prices on household vehicle holdings and use," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 1-18, January.
    17. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
    18. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    19. Konstadinos Goulias & Kriste Henson, 2006. "On altruists and egoists in activity participation and travel: who are they and do they live together?," Transportation, Springer, vol. 33(5), pages 447-462, September.
    20. Jaehwan Kim & Greg M. Allenby & Peter E. Rossi, 2002. "Modeling Consumer Demand for Variety," Marketing Science, INFORMS, vol. 21(3), pages 229-250, December.
    21. Bhat, Chandra R., 1998. "A model of post home-arrival activity participation behavior," Transportation Research Part B: Methodological, Elsevier, vol. 32(6), pages 387-400, August.
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