Discrete choice modeling in Python with large datasets & models - Assortment & Pricing Optimization .

Advanced choice modeling with multidimensional utility representations.

Inverse Stochastic User Equilibrium with LOGIT assignment

Lab experiment to study the impact of time perception on route choices in public transit. It presents choice scenarios using animations or numerical attributes. It is implemented with PyQT and conducted with participants from Santiago, Chile and London, UK.

Binary logit based on the Mobility and Transport Microcensus 2015 explaining the "choice" of working - at least from time to time - from home in Switzerland.

Introducing the Apollo Choice Modelling for Penalty Shot Prediction GitHub repository. Utilizing Apollo choice modeling, our code predicts soccer penalty shots. Featuring a user-friendly R Shiny app, the model considers player data, shot locations, and goalkeeper behavior to estimate shot success.

This was the code I use to process a Multinomial Logistic Regression on R, with the Apollo Choice Modeling Package for R. Used to calculate the utility function of particular customers of vehicles in Bogotá, Colombia

A Collection of Utility Functions for Choice Modeling and Statistical Analysis

This repository contains R code that analyzes data gathered from a lab experiment that studies the influence of time perception on route choices in public transport

The model that has been uploaded to this repository aspires to describe routing behavior of micro-mobility modes, e.g., e-bikes and e-scoters, in relationship with traditional modes, e.g., private car and walking.