88 citations · 190 across the 11 of their papers we have counts for
12 papers
A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections
Rico Krueger, Michel Bierlaire, Prateek Bansal
Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and mana…
Face masks, vaccination rates and low crowding drive the demand for the London Underground during the COVID-19 pandemic
Prateek Bansal, Roselinde Kessels, Rico Krueger +1
The COVID-19 pandemic has drastically impacted people's travel behaviour and out-of-home activity participation. While countermeasures are being eased with increasing vaccination r…
Robust discrete choice models with t-distributed kernel errors
Rico Krueger, Michel Bierlaire, Thomas Gasos +1
Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling…
Fast Bayesian Estimation of Spatial Count Data Models
Prateek Bansal, Rico Krueger, Daniel J. Graham
Spatial count data models are used to explain and predict the frequency of phenomena such as traffic accidents in geographically distinct entities such as census tracts or road seg…
A New Spatial Count Data Model with Bayesian Additive Regression Trees for Accident Hot Spot Identification
Rico Krueger, Prateek Bansal, Prasad Buddhavarapu
The identification of accident hot spots is a central task of road safety management. Bayesian count data models have emerged as the workhorse method for producing probabilistic ra…
Semi-Parametric Hierarchical Bayes Estimates of New Yorkers' Willingness to Pay for Features of Shared Automated Vehicle Services
Rico Krueger, Taha H. Rashidi, Akshay Vij
In this paper, we contrast parametric and semi-parametric representations of unobserved heterogeneity in hierarchical Bayesian multinomial logit models and leverage these methods t…
Autonomous Driving and Residential Location Preferences: Evidence from a Stated Choice Survey
Rico Krueger, Taha H. Rashidi, Vinayak V. Dixit
The literature suggests that autonomous vehicles (AVs) may drastically change the user experience of private automobile travel by allowing users to engage in productive or relaxing…
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…
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…
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…
Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions
Akshay Vij, Rico Krueger
This study proposes a mixed logit model with multivariate nonparametric finite mixture distributions. The support of the distribution is specified as a high-dimensional grid over t…
A Dirichlet Process Mixture Model of Discrete Choice
Rico Krueger, Akshay Vij, Taha H. Rashidi
We present a mixed multinomial logit (MNL) model, which leverages the truncated stick-breaking process representation of the Dirichlet process as a flexible nonparametric mixing di…