A Network Generalized Extreme Value Model for Route Choice Allowing Implicit Route Enumeration

2013 ◽  
Vol 28 (8) ◽  
pp. 560-580 ◽  
Author(s):  
Andrea Papola ◽  
Vittorio Marzano
Keyword(s):  
Route Choice ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Extreme Value Model ◽  
Value Model ◽  
Generalized Extreme Value Model

Practical Method to Simulate Multiple Discrete-Continuous Generalized Extreme Value Model: Application to Examine Substitution Patterns of Household Transportation Expenditures

2019 ◽  
Vol 2673 (8) ◽  
pp. 145-156
Author(s):  
Jie Ma ◽  
Xin Ye ◽  
Abdul Rawoof Pinjari
Keyword(s):  
Choice Model ◽  
Practical Method ◽  
Extreme Value ◽  
Base Case ◽  
Generalized Extreme Value ◽  
Explanatory Variables ◽  
Mnl Model ◽  
Extreme Value Model ◽  
Value Model ◽  
Generalized Extreme Value Model

The multiple discrete-continuous generalized extreme value (MDCGEV) model has been derived from multivariate extreme value (MEV)-based stochastic specifications to relax the independence assumption in the multiple discrete-continuous extreme value (MDCEV) model. It is analogous to the situation where a generalized extreme value (GEV) model relaxes the same assumption in a multinomial logit (MNL) model. However, unlike the case of single discrete choice model where substitution patterns can be understood based on elasticity expressions for a change in the value of an explanatory variable, the MDCEV and its variants do not offer closed-form elasticity expressions. The predictions must be compared explicitly under the base case and policy case scenarios. To perform a prediction exercise with MDCEV or its variants, random samples have to be drawn from the relevant stochastic distributions, which is actually not a straightforward task. In this paper, a practical method is proposed for drawing from an MEV distribution and the method is demonstrated to examine substitution patterns in an MDCGEV model for household transportation expenditures. The empirical results show that the cross-elasticities of explanatory variables in the MDCGEV model exhibit more variations than those in MDCEV and multiple discrete-continuous nested extreme value (MDCNEV) models.

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