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Multinomial Probit and Qualitative Choice: A Computationally Efficient Algorithm

Author

Listed:
  • Carlos F. Daganzo

    (University of California, Berkeley)

  • Fernando Bouthelier

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

  • Yosef Sheffi

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

Even though multinomial probit models have many attractive theoretical features and have been proposed for diverse choice problems (such as modal split and route choice in the transportation field), they have never been used in practice due to the lack of an adequate numerical technique for their application. The purpose of this paper is to introduce such a technique and to demonstrate the feasibility of forecasting with multinominal probit models. Our limited computational experience with the proposed numerical technique indicates that it is accurate, and can be efficiently applied to large choice problems.

Suggested Citation

  • Carlos F. Daganzo & Fernando Bouthelier & Yosef Sheffi, 1977. "Multinomial Probit and Qualitative Choice: A Computationally Efficient Algorithm," Transportation Science, INFORMS, vol. 11(4), pages 338-358, November.
    • Handle: RePEc:inm:ortrsc:v:11:y:1977:i:4:p:338-358
    DOI: 10.1287/trsc.11.4.338
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    Cited by:

    1. Tasos Nikoleris & Mark Hansen, 2012. "Queueing Models for Trajectory-Based Aircraft Operations," Transportation Science, INFORMS, vol. 46(4), pages 501-511, November.
    2. Kenneth Train, "undated". "Simulation Methods for Probit and Related Models Based on Convenient Error Partitioning," Working Papers _009, University of California at Berkeley, Econometrics Laboratory Software Archive.
    3. Yusuke Hara & Eiji Hato, 2019. "Analysis of dynamic decision-making in a bicycle-sharing auction using a dynamic discrete choice model," Transportation, Springer, vol. 46(1), pages 147-173, February.
    4. W C Halperin & G D Richardson & N Gale & C M Costanzo, 1984. "A Generalized Procedure for Comparing Models of Spatial Choice," Environment and Planning A, , vol. 16(10), pages 1289-1301, October.
    5. Vassilis A. Hajivassiliou, 1991. "Simulation Estimation Methods for Limited Dependent Variable Models," Cowles Foundation Discussion Papers 1007, Cowles Foundation for Research in Economics, Yale University.
    6. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441, Elsevier.
    7. C F Daganzo & Y Sheffi, 1982. "Multinomial Probit with Time-Series Data: Unifying State Dependence and Serial Correlation Models," Environment and Planning A, , vol. 14(10), pages 1377-1388, October.
    8. Xuan Di & Henry X. Liu & Shanjiang Zhu & David M. Levinson, 2017. "Indifference bands for boundedly rational route switching," Transportation, Springer, vol. 44(5), pages 1169-1194, September.
    9. Bao, Yue & Gao, Ziyou & Xu, Meng & Sun, Huijun & Yang, Hai, 2015. "Travel mental budgeting under road toll: An investigation based on user equilibrium," Transportation Research Part A: Policy and Practice, Elsevier, vol. 73(C), pages 1-17.
    10. Di, Xuan & Liu, Henry X., 2016. "Boundedly rational route choice behavior: A review of models and methodologies," Transportation Research Part B: Methodological, Elsevier, vol. 85(C), pages 142-179.
    11. Wada, Kentaro & Usui, Kento & Takigawa, Tsubasa & Kuwahara, Masao, 2018. "An optimization modeling of coordinated traffic signal control based on the variational theory and its stochastic extension," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 907-925.
    12. Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.
    13. 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.
    14. Batram, Manuel & Bauer, Dietmar, 2019. "On consistency of the MACML approach to discrete choice modelling," Journal of choice modelling, Elsevier, vol. 30(C), pages 1-16.

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