TY - JOUR
T1 - On the development of a semi-nonparametric generalized multinomial logit model for travel-related choices
AU - Wang, Ke
AU - Ye, Xin
AU - Pendyala, Ram
AU - Zou, Yajie
N1 - Publisher Copyright:
© 2017 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/10
Y1 - 2017/10
N2 - A semi-nonparametric generalized multinomial logit model, formulated using orthonormal Legendre polynomials to extend the standard Gumbel distribution, is presented in this paper. The resulting semi-nonparametric function can represent a probability density function for a large family of multimodal distributions. The model has a closed-form log-likelihood function that facilitates model estimation. The proposed method is applied to model commute mode choice among four alternatives (auto, transit, bicycle and walk) using travel behavior data from Argau, Switzerland. Comparisons between the multinomial logit model and the proposed semi-nonparametric model show that violations of the standard Gumbel distribution assumption lead to considerable inconsistency in parameter estimates and model inferences.
AB - A semi-nonparametric generalized multinomial logit model, formulated using orthonormal Legendre polynomials to extend the standard Gumbel distribution, is presented in this paper. The resulting semi-nonparametric function can represent a probability density function for a large family of multimodal distributions. The model has a closed-form log-likelihood function that facilitates model estimation. The proposed method is applied to model commute mode choice among four alternatives (auto, transit, bicycle and walk) using travel behavior data from Argau, Switzerland. Comparisons between the multinomial logit model and the proposed semi-nonparametric model show that violations of the standard Gumbel distribution assumption lead to considerable inconsistency in parameter estimates and model inferences.
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U2 - 10.1371/journal.pone.0186689
DO - 10.1371/journal.pone.0186689
M3 - Article
C2 - 29073152
AN - SCOPUS:85032456336
VL - 12
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 10
M1 - e0186689
ER -