146 citations · 209 across the 9 of their papers we have counts for
10 papers
Statistical significance in choice modelling: computation, usage and reporting
Stephane Hess, Andrew Daly, Michiel Bliemer +3
This paper offers a commentary on the use of notions of statistical significance in choice modelling. We review the reasons for uncertainty in parameter estimates, provide a precis…
Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization
Siqi Feng, Rui Yao, Stephane Hess +4
Deep neural networks (DNNs) frequently present behaviorally irregular patterns, significantly limiting their practical potentials and theoretical validity in travel behavior modeli…
Designed Quadrature to Approximate Integrals in Maximum Simulated Likelihood Estimation
Prateek Bansal, Vahid Keshavarzzadeh, Angelo Guevara +2
Maximum simulated likelihood estimation of mixed multinomial logit (MMNL) or probit models requires evaluation of a multidimensional integral. Quasi-Monte Carlo (QMC) methods such…
Variational Bayesian Inference for Mixed Logit Models with Unobserved Inter- and Intra-Individual Heterogeneity
Rico Krueger, Prateek Bansal, Michel Bierlaire +2
Variational Bayes (VB), a method originating from machine learning, enables fast and scalable estimation of complex probabilistic models. Thus far, applications of VB in discrete c…
Can Mobility-on-Demand services do better after discerning reliability preferences of riders?
Prateek Bansal, Yang Liu, Ricardo Daziano +1
We formalize one aspect of reliability in the context of Mobility-on-Demand (MoD) systems by acknowledging the uncertainty in the pick-up time of these services. This study answers…
Eliciting Preferences of Ridehailing Users and Drivers: Evidence from the United States
Prateek Bansal, Akanksha Sinha, Rubal Dua +1
Transportation Network Companies (TNCs) are changing the transportation ecosystem, but micro-decisions of drivers and users need to be better understood to assess the system-level…
Pólygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit Models
Prateek Bansal, Rico Krueger, Michel Bierlaire +2
The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due…
A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel
Subodh Dubey, Prateek Bansal, Ricardo A. Daziano +1
In multinomial response models, idiosyncratic variations in the indirect utility are generally modeled using Gumbel or normal distributions. This study makes a strong case to subst…
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations
Prateek Bansal, Rico Krueger, Michel Bierlaire +2
Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed…
A Framework to Integrate Mode Choice in the Design of Mobility-on-Demand Systems
Yang Liu, Prateek Bansal, Ricardo Daziano +1
Mobility-on-Demand (MoD) systems are generally designed and analyzed for a fixed and exogenous demand, but such frameworks fail to answer questions about the impact of these servic…