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Accommodating flexible substitution patterns in multi-dimensional choice modeling: formulation and application to travel mode and departure time choice

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  • Bhat, Chandra R.
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    Abstract

    The nested logit model has been used extensively to model multi-dimensional choice situations. A drawback of the nested logit model is that it does not allow choice alternatives to share common unobserved attributes along all the dimensions characterizing the multidimensional choice context. This paper formulates a mixed multinomial logit structure that accommodates unobserved correlation across both dimensions in a two-dimensional choice context. The mixed multinomial logit structure is parsimonious in the number of parameters to be estimated and is also relatively easy to estimate using simulation methods. The mixed multinomial logit model is applied to an analysis of travel mode and departure time choice for home-based social-recreational trips using data drawn from the 1990 San Francisco Bay Area household survey. The empirical results underscore the need to capture unobserved attributes along both the mode and departure time dimensions, both for improved data fit as well as for more realistic policy evaluations of transportation control measures.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 32 (1998)
    Issue (Month): 7 (September)
    Pages: 455-466

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    Handle: RePEc:eee:transb:v:32:y:1998:i:7:p:455-466

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    1. Train, Kenneth, 1980. "A Structured Logit Model of Auto Ownership and Mode Choice," Review of Economic Studies, Wiley Blackwell, vol. 47(2), pages 357-70, January.
    2. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    3. Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
    4. Small, Kenneth A, 1982. "The Scheduling of Consumer Activities: Work Trips," American Economic Review, American Economic Association, vol. 72(3), pages 467-79, June.
    5. Horowitz, Joel L., 1991. "Reconsidering the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(6), pages 433-438, December.
    6. Koppelman, Frank S. & Pas, Eric I., 1986. "Multidimensional choice model transferability," Transportation Research Part B: Methodological, Elsevier, vol. 20(4), pages 321-330, August.
    7. Bhat, Chandra R. & Koppelman, Frank S., 1993. "A conceptual framework of individual activity program generation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(6), pages 433-446, November.
    8. Swait, Joffre & Ben-Akiva, Moshe, 1987. "Empirical test of a constrained choice discrete model: Mode choice in São Paulo, Brazil," Transportation Research Part B: Methodological, Elsevier, vol. 21(2), pages 103-115, April.
    9. Lee, L-F., 1990. "On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models," Papers 260, Minnesota - Center for Economic Research.
    10. Mannering, Fred L., 1989. "Poisson analysis of commuter flexibility in changing routes and departure times," Transportation Research Part B: Methodological, Elsevier, vol. 23(1), pages 53-60, February.
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    Cited by:
    1. Fredrik Carlsson, 2003. "The demand for intercity public transport: the case of business passengers," Applied Economics, Taylor & Francis Journals, vol. 35(1), pages 41-50.
    2. Arne Risa Hole, 2007. "Modelling Heterogeneity in Patients' Preferences for the Attributes of a General Practitioner Appointment," Working Papers 022cherp, Centre for Health Economics, University of York.
    3. Munizaga, Marcela A. & Heydecker, Benjamin G. & Ortúzar, Juan de Dios, 2000. "Representation of heteroskedasticity in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 34(3), pages 219-240, April.
    4. Vincent van den Berg & Eric Kroes & Erik T. Verhoef, 2010. "Biases in Willingness-To-Pay Measures from Multinomial Logit Estimates due to Unobserved Heterogeneity," Tinbergen Institute Discussion Papers 10-014/3, Tinbergen Institute.
    5. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    6. Srinivasan, Karthik K. & Mahmassani, Hani S., 2003. "Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS: a dynamic kernel logit formulation," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 793-814, November.
    7. Hu, Shucheng & Saleh, Wafaa, 2005. "Impacts of congestion charging on shopping trips in Edinburgh," Transport Policy, Elsevier, vol. 12(5), pages 443-450, September.
    8. Vincent van den Berg & Eric Kroes & Erik T. Verhoef, 2010. "Biases in Willingness-To-Pay Measures from Multinomial Logit Estimates due to Unobserved Heterogeneity," Tinbergen Institute Discussion Papers 10-014/3, Tinbergen Institute.
    9. Kockelman, Kara Maria, 2001. "A model for time- and budget-constrained activity demand analysis," Transportation Research Part B: Methodological, Elsevier, vol. 35(3), pages 255-269, March.
    10. Peer, Stefanie & Knockaert, Jasper & Koster, Paul & Tseng, Yin-Yen & Verhoef, Erik T., 2013. "Door-to-door travel times in RP departure time choice models: An approximation method using GPS data," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 134-150.
    11. Bhat, Chandra R. & Steed, Jennifer L., 2002. "A continuous-time model of departure time choice for urban shopping trips," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 207-224, March.

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