01697887

Component

Skewed and constrained distributions for model parameters are better suited to provide realistic taste sensitivity and Willingness-To-Pay (WTP) distributions in many empirical applications. In this context of random taste heterogeneity, the standard multinomial probit (MNP) has seen limited applicability largely owing to the normal distributional assumption of model parameters that will invariably result in (a) counter-intuitive taste sensitivities for a significant proportion of the population, and (b) WTP distributions without finite moments. In this paper, a Generalized MNP (GMNP) model that allows constrained random parameters with multivariate truncated normal distribution was developed. The ability of the maximum simulated likelihood inference method to retrieve the model parameters was demonstrated on synthetic data using the GHK simulator with quasi-Monte Carlo sequences. The bias in the parameters of the MNP model that ignores the constraints on model parameters was also demonstrated. Also, the proposed model was used to analyze car parking preferences in Jerusalem while ensuring (a) negative tastes for cost and time attributes, and (b) finite moments of the WTP measures associated with walk and search times.

This paper was sponsored by TRB committee ABJ80 Standing Committee on Statistical Methods.

19-01958

English

7p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References

Consumer preferences; Costs; Monte Carlo method; Multinomial probits; Parking; Simulation; Travel time

Willingness-to-pay

Jerusalem

Economics; Highways; Planning and Forecasting

Transportation Research Board Annual Meeting 2019 Paper #19-01958

TRIS, TRB, ATRI

Dec 7 2018 9:40AM