Publications by Michel Bierlaire

A new flexible multiple discrete–continuous extreme value (MDCEV) choice model: exploring non-monotonic preference in time use decisions

Derived from the Karush–Kuhn–Tucker (KKT) optimality conditions of utility maximization, the multiple discrete–continuous extreme value (MDCEV) models are extensively used in the field of time use. This is because they are grounded in a framework that maxi ...

2025

Bike sharing systems: The impact of precise trip demand forecasting on operational efficiency in different city structures

Michel Bierlaire, Selin Ataç

Increasing environmental concerns drive interest in sustainable solutions across various fields. Vehicle sharing systems such as one-way station-based bike sharing systems (BSSs) offer one such solution in transportation, although they pose operational cha ...

2025

Scalable kernel logistic regression with Nyström approximation: Theoretical analysis and application to discrete choice modelling

Michel Bierlaire

The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers ...

2025

New York, Abu Dhabi, London, or stay at home? Using a cross-nested logit model to identify complex substitution patterns in migration

Michel Bierlaire

We propose a cross-nested logit (CNL) approach to investigate how individuals adjust their migration decisions in response to changes in the global landscape. In contrast to the widely used logit model, the CNL enables more intricate substitution patterns ...

OXFORD UNIV PRESS2025

Mobility and Individual Choices in the Spread of Infectious Diseases: Enhancing Activity-Based Models with Awareness and Testing Dynamics

Michel Bierlaire, Cloe Cortes Balcells

2024

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Michel Bierlaire, Marija Kukic

This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...

IEEE2024

Multi-objective Optimization of Activity-Travel Policies for Epidemic Control: Balancing Health and Economic Outcomes on Socio-Economic Segments

Michel Bierlaire, Cloe Cortes Balcells, Rico Krüger

2024

Fast Algorithms for Capacitated Continuous Pricing with Discrete Choice Demand Models

Michel Bierlaire, Fabian Alejandro Torres Duran

2024

A conditional trust-region algorithm for the estimation of discrete choice models

Michel Bierlaire, Nicola Marco Ortelli, Matthieu Marie Cochon de Lapparent

2024

Resampling estimation of discrete choice models

Michel Bierlaire, Nicola Marco Ortelli, Matthieu Marie Cochon de Lapparent

In the context of discrete choice modeling, the extraction of potential behavioral insights from large datasets is often limited by the poor scalability of maximum likelihood estimation. This paper proposes a simple and fast dataset-reduction method that i ...

2024