Publications by Michel Bierlaire

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Map matching algorithms are the conventional way to generate path observations from GPS data for route choice models. The deterministic matching may introduce extra biases to parameters of route choice models if the matching is wrong. In this paper, a new methodology is proposed to probabilistically generate path representation from GPS location data and the underlying network. This methodology takes advantage of both spatial and temporal relationships existing in the location data and the network. The gene

GPS capable smart phones are emerging survey tools in transportation research field, especially in modeling individuals' mobility patterns. In route choice modeling, path observations need to be generated explicitly for the estimation. It is a challenge because the recorded data is not as dense or accurate as those from dedicated GPS devices. In this paper, we develop a methodology for generating probabilistic path observations from sparse and inaccurate location data, for state-of-the-art discrete route ch

We propose a probabilistic modeling approach to represent the speed-density relationship of pedestrian traffic. The approach is data-driven, and it is motivated by the presence of high scatter in the raw data that we have analyzed. We show the validity of the proposed approach, and its superiority compared to deterministic approaches from the literature using a dataset collected from a real scene and another from a controlled experiment.

Social Engagement is a novel business model whose goal is transforming final users of a service from passive components into active ones. In this framework, people are contacted by the decision-maker (generally a company) and they are asked to perform task ...

We present a new approach for modeling and analyzing route choice behavior. It is motivated by the need to reduce the complexity of the state-of-the-art choice models. It is inspired by the simplifications done by the travelers, using representations of their surrounding space. The proposed framework is based on elements designed to mimic the mental representations used by travelers, denoted as Mental Representation Items (MRIs). It allows the modeler to adjust the level of complexity according to the needs

Demand information is an input for a great deal of operations research models. Assumed as given in many problem instances addressed in the literature, demand data are difficult to generate. In this tutorial, we provide an introduction to disaggregate demand models that are designed to capture in detail the underlying behavioral mechanisms at the foundation of the demand.

A new approach for the estimation of bid-rent functions for residential location choice is proposed. The method is based on the bid-auction approach and considers that the expected maximum bid of the auction is a latent variable that can be related to obse ...

Optimization problems due to noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise. In this paper, we introduce a framework that considers the uncertain data implicitly. We define the concept of Uncertainty Features (UF), which are problem-specific structural properties

Hybrid choice models have proved to be a powerful framework that integrates attitudinal and perceptional data into discrete choice models. However the measurement component of such a framework often fails to exploit individual-specific information that might affect the way subjects answer to indicators of opinion. In this paper we propose an HCM with a measurement model that takes into account heterogeneity in the response behavior. Precisely, we capture effects of exaggeration in answers to psychometrics.

In this paper we consider the recovery of an airline schedule after an unforeseen event, commonly called disruption, that makes the planned schedule unfeasible. In particular we consider the aircraft recovery problem for an heterogeneous fleet of aircrafts, made of regular and reserve planes, where the maintenance constraints are explicitly taken into account. We propose a multicommodity network flow model, where each commodity represents a plane, a dynamic programming algorithm to build the underlying netw

Optimization problems with noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the unc ...

In airline schedule planning models, the demand and price information are usually taken as inputs to the model. Therefore, schedule and capacity decisions are taken separately from pricing decisions. In this article, we present an integrated scheduling, fl ...

Facial expression recognition is a hard and ambiguous problem in computer vision. It is hard due to the wide variety of faces and the wide variety of ways of representing the same expression. And it is ambiguous because, even though common approaches treat it as a classification problem, actually when looking at the same scene, different people can feel a different expression. In this presentation, we will show preliminary results obtained in an ongoing project of Discrete Choice Modeling of human perceptio

A novel approach is proposed to use the Cross Nested Logit (CNL) model in route choice when sampling of paths is considered. It adopts the Metropolis-Hasting algorithm to sample the choice sets for the model. A new expansion factor and an approximation method are put forward to calculate the sampled probabilities of alternatives. We build on state-of-the-art results for the Multivariate Extreme Value models and extend then to the route choice context. Case studies on both synthetic data and a real network d

This paper establishes that every random utility discrete choice model (RUM) has a representation that can be characterized by a choice-probability generating function (CPGF) with specific properties, and that every function with these specific properties is consistent with a RUM. The choice probabilities from the RUM are obtained from the gradient of the CPGF. Mixtures of RUM are characterized by logarithmic mixtures of their associated CPGF. The paper relates CPGF to multivariate extreme value distributio

Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices.When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favo ...

Mixed logit models with unobserved inter- and intra-individual heterogeneity hierarchi-cally extend standard mixed logit models by allowing tastes to vary randomly both acrossindividuals as well as across choice tasks encountered by the same individual. Recentwork advocates the use of these methods in choice-based recommender systems underthe premise that mixed logit models with unobserved inter- and intra-individual hetero-geneity afford personalised preference estimation and prediction. In this research n

Michel Bierlaire

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Generating probabilistic path observation from GPS data for route choice modeling

Modeling Route Choice Behavior From Smart-phone GPS data

Modeling Route Choice Behavior From Smart-phone GPS data

Probabilistic speed-density relationship for pedestrian traffic: a data-driven approach

Workforce Allocation for Social Engagement Services via Stochastic Optimization

Revisiting the Route Choice Problem: A Modeling Framework Based on Mental Representations

Introduction to disaggregate demand models

Estimation of bid functions for location choice and price modeling with a latent variable approach

UFO: Uncertainty Feature Optimization, an Implicit Paradigm for Problems with Noisy Data

Accounting for response behavior heterogeneity in the measurement of attitudes: an application to demand for electric vehicles

Airline Disruptions: Aircraft Recovery with Maintenance Constraints

Uncertainty Feature Optimization: an implicit paradigm for problems with noisy data

An integrated airline scheduling, fleeting and pricing model for a monopolized market

Accounting for response behavior heterogeneity in the measurement of attitudes: an application to demand for electric vehicles

Modelling Human Perception of Facial Expressions by Discrete Choice Models

Modelling Human Perception of Facial Expressions by Discrete Choice Models

Specification of the cross nested logit model with sampling of alternatives for route choice models

Choice probability generating functions

Robust optimization for strategic energy planning

On the usefulness of mixed logit models with unobserved inter- and intra-individual heterogeneity