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Discrete choice models with multiplicative stochasticity in choice environment variables: Application to accommodating perception errors in driver behaviour models

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  • Nirmale, Sangram Krishna
  • Pinjari, Abdul Rawoof

Abstract

This paper presents a mixed multinomial logit-based discrete choice modelling framework to accommodate decision-makers’ errors in perceiving choice environment variables that do not vary across choice alternatives. An analysis is undertaken to evaluate two different ways of specifying errors in the choice environment variables in discrete choice models – (a) the additive specification and (b) the multiplicative specification. Between these two approaches, the multiplicative error specification is consistent with psychophysical theories of human perception of physical quantities in that the variability in perception tends to be greater for quantities of greater magnitude. Further, it is shown that models with an additive error specification run into parameter (un)identifiability problems if the analyst attempts to accommodate errors in several variables. In contrast, models with multiplicative errors in variables allow separate identification of stochasticity in as many variables as needed, as long as those variables have a significant influence on the choice outcome.

Suggested Citation

  • Nirmale, Sangram Krishna & Pinjari, Abdul Rawoof, 2023. "Discrete choice models with multiplicative stochasticity in choice environment variables: Application to accommodating perception errors in driver behaviour models," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 169-193.
    • Handle: RePEc:eee:transb:v:170:y:2023:i:c:p:169-193
    DOI: 10.1016/j.trb.2023.02.014
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    as
    1. Robert Bartels & Denzil Fiebig & Arthur Soest, 2006. "Consumers and experts: an econometric analysis of the demand for water heaters," Empirical Economics, Springer, vol. 31(2), pages 369-391, June.
    2. Steimetz, Seiji S.C. & Brownstone, David, 2005. "Estimating commuters' "value of time" with noisy data: a multiple imputation approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(10), pages 865-889, December.
    3. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457, Elsevier.
    4. Hess, Stephane & Train, Kenneth, 2017. "Correlation and scale in mixed logit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 1-8.
    5. Treiber, Martin & Kesting, Arne & Helbing, Dirk, 2006. "Delays, inaccuracies and anticipation in microscopic traffic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(1), pages 71-88.
    6. Kao, Chihwa & Schnell, John F., 1987. "Errors in variables in the multinomial response model," Economics Letters, Elsevier, vol. 25(3), pages 249-254.
    7. Yatchew, Adonis & Griliches, Zvi, 1985. "Specification Error in Probit Models," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 134-139, February.
    8. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    9. Hess, Stephane & Daly, Andrew & Dekker, Thijs & Cabral, Manuel Ojeda & Batley, Richard, 2017. "A framework for capturing heterogeneity, heteroskedasticity, non-linearity, reference dependence and design artefacts in value of time research," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 126-149.
    10. Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
    11. Chandra R. Bhat & Patrícia S. Lavieri, 2018. "A new mixed MNP model accommodating a variety of dependent non-normal coefficient distributions," Theory and Decision, Springer, vol. 84(2), pages 239-275, March.
    12. Gipps, P.G., 1981. "A behavioural car-following model for computer simulation," Transportation Research Part B: Methodological, Elsevier, vol. 15(2), pages 105-111, April.
    13. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    14. Díaz, Federico & Cantillo, Víctor & Arellana, Julian & Ortúzar, Juan de Dios, 2015. "Accounting for stochastic variables in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 222-237.
    15. Joel L. Horowitz, 1983. "Statistical Comparison of Non-Nested Probabilistic Discrete Choice Models," Transportation Science, INFORMS, vol. 17(3), pages 319-350, August.
    16. Chikaraishi, Makoto & Nakayama, Shoichiro, 2016. "Discrete choice models with q-product random utilities," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 576-595.
    17. Varela, Juan Manuel Lorenzo & Börjesson, Maria & Daly, Andrew, 2018. "Quantifying errors in travel time and cost by latent variables," Working papers in Transport Economics 2018:3, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    18. Sanko, Nobuhiro & Hess, Stephane & Dumont, Jeffrey & Daly, Andrew, 2014. "Contrasting imputation with a latent variable approach to dealing with missing income in choice models," Journal of choice modelling, Elsevier, vol. 12(C), pages 47-57.
    19. Chakroborty, Partha & Pinjari, Abdul Rawoof & Meena, Jayant & Gandhi, Avinash, 2021. "A Psychophysical Ordered Response Model of Time Perception and Service Quality: Application to Level of Service Analysis at Toll Plazas," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 44-64.
    20. Bhatta, Bharat P. & Larsen, Odd I., 2011. "Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice," Transport Policy, Elsevier, vol. 18(2), pages 326-335, March.
    21. Varotto, Silvia F. & Glerum, Aurélie & Stathopoulos, Amanda & Bierlaire, Michel & Longo, Giovanni, 2017. "Mitigating the impact of errors in travel time reporting on mode choice modelling," Journal of Transport Geography, Elsevier, vol. 62(C), pages 236-246.
    22. Koutsopoulos, Haris N. & Farah, Haneen, 2012. "Latent class model for car following behavior," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 563-578.
    23. Carlos F. Daganzo & Yosef Sheffi, 1977. "On Stochastic Models of Traffic Assignment," Transportation Science, INFORMS, vol. 11(3), pages 253-274, August.
    24. Fosgerau, M. & Bierlaire, M., 2009. "Discrete choice models with multiplicative error terms," Transportation Research Part B: Methodological, Elsevier, vol. 43(5), pages 494-505, June.
    25. Castillo, Enrique & Menéndez, José María & Jiménez, Pilar & Rivas, Ana, 2008. "Closed form expressions for choice probabilities in the Weibull case," Transportation Research Part B: Methodological, Elsevier, vol. 42(4), pages 373-380, May.
    26. Hamdar, Samer H. & Mahmassani, Hani S. & Treiber, Martin, 2015. "From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 32-53.
    27. Daniel Hellerstein, 2005. "Modeling Discrete Choice with Uncertain Data: An Augmented MNL Estimator," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(1), pages 77-84.
    28. Varela, Juan Manuel Lorenzo & Börjesson, Maria & Daly, Andrew, 2018. "Quantifying errors in travel time and cost by latent variables," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 520-541.
    29. Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February.
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