Publications by Michel Bierlaire

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There is an increasing interest for the integration of supply-demand interactions in airline schedule planning models. We work with an itinerary-based fleet assignment model where an itinerary choice model is explicitly integrated. The choice model represents the decision on pricing as well as the spill and recapture effects. We present a sensitivity analysis for the key assumptions of the demand model in order to quantify the added value of choice-based fleet assignment compared to leg-based fleet assignme

We consider the previously unsolved problem of sampling paths according to a given distribution from a general network. The problem is difficult because of the combinatorial number of alternatives, which prohibits a complete enumeration of all paths and he ...

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 models. It is inspired by the simplifications actually done by the people, 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). This paper describes how operational models based on MRIs can be derived and di

In this paper we introduce the Incremental Temporally Weighted Principal Component Analysis (ITWPCA) algorithm, based on SVD update, and the Incremental Temporally Weighted Visual Tracking with Spatial Penalty (ITWVTSP) algorithm for robust visual tracking. ITWVTSP uses ITWPCA for computing incrementally a robust low dimensional subspace representation (model) of the tracked object. The robustness is based on the capacity of weighting the contribution of each single sample to the subspace generation, in ord

In this paper we propose a general framework for pedestrian walking behavior, based on discrete choice modeling. Two main behaviors are identified: unconstrained and constrained. The constrained patterns are further classified into attractive interactions and repulsive interactions. The formers are captured by a leader-follower model while the latters through a collision avoidance model. The spatial correlation between the alternatives is taken into account defining a cross nested logit model. Quantitative

In this paper we present themethodological framework of a dynamic discrete-continuous choice model (DDCCM) of car ownership, usage and fuel type. The approach consists of embedding a discrete-continuous choice model into a dynamic programming (DP) framework. This work proposes the following novel features. First, decisions are modeled at a household level. Second, we consider an extensive choice variable which involves the car replacement decision, the annual driving distance, the fuel type, the decision to

In the context of the arrival of electric vehicles on the car market, new mathematical models are needed to understand and predict the impact on the market shares. This research provides a comprehensive methodology to forecast the demand of a technology wh ...

This article proposes an improvement of existing methods of origin-destination matrix estimation by an explicit use of data describing the structure of the matrix. These data can be obtained from parking surveys. The new model is applied on both illustrative and real examples, and the results are discussed. Comparisons with the results obtained with SATURN/ME2 and the generalized least-squares method are also presented.

We propose a methodological framework to include a wide variety of discrete choice models in (mixed) integer optimization problems. We succeed in obtaining a specification that is linear in the decision variables, allowing to use exact methods to solve the problem. This opens the door to solving integrated supply and demand models in many fields. This working paper describes the methodology and proposes the full model.

In this paper, we formulate a mixed integer linear program (MILP) for the simulated maximum likelihood estimation (MLSE) problem and devise a Benders decomposition approach to speed up the solution process. This framework can be applied to any advanced discrete choice model and exploits total unimodularity to keep the master problem linear in the decomposition. The proposed decomposition approach is benchmarked against the original MILP formulation and PandasBiogeme. Computational experiments are performed

Many U.S. cities use vehicle emissions testing programs to improve air quality by identifying gross polluting vehicles and requiring their owners to make emissions-related repairs. All vehicles that meet certain criteria must pass an emissions test as part ...

In this paper we describe a new approach for the multitrack initiation problem. We propose an extensive use of the top-view reconstruction of the scene to solve the detection step in tracking pedestrians. We leave a large set of starting hypothetical moving objects free to evolve in the scene for a certain number of frames. The number of trajectories is pre-filtered using distance and direction constraints on a one-step movement of pedestrians. The output trajectories from pre-filtering step are then filter

Latent class models are a convenient and intuitive way to introduce taste heterogeneity in discrete choice models by relating attributes of the decision makers with unobserved behavioral classes, hence allowing for a more accurate market segmentation. Esti ...

Derivative-free optimization involves the methods used to minimize an expensive objective functionwhen its derivatives are not available. We present here a trust-region algorithmbased on Radial Basis Functions (RBFs). The main originality of our approach i ...

The Locomotive Assignment Problem (LAP) is a class of planning and scheduling problems solved by assigning a fleet of locomotives to a network of trains. In the planning versions of the LAP, the type of consist (a group of linked locomotives) assigned to e ...

This study presents a flexible model for risk-taking behavior and accident injury severity. It is specifically designed to evaluate the impact of Via Sicura, a Swiss road safety program, on the severity of accident outcomes. Our proposed model treats the risk-taking behavior of each driver as a latent variable that depends on a number of socioeconomic and contextual factors, and whose manifestation can be measured by means of behavioral indicators. The aggregated risk, a central feature of our framework, re

Michel Bierlaire

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Analysis of an itinerary-based airline fleet assignment model with passenger-choice driven recapture and pricing

Metropolis-Hastings sampling of paths

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

Sample and Pixel Weighting Strategies for Robust Incremental Visual Tracking

Capturing interactions in pedestrian walking behavior in a discrete choice framework

A dynamic discrete-continuous choice model of car ownership and usage: Methodological framework

Forecasting the demand for electric vehicles: accounting for attitudes and perceptions

Capturing interactions in pedestrian walking behavior in a discrete choice framework

MEUSE: an origin-destination estimator that exploits structure

Demand-based discrete optimization

A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models

Associations among Household Characteristics, Vehicle Characteristics and Emissions Failures: An Application of Targeted Marketing Data

BEHAVIORAL FILTERING OF HUMAN TRAJECTORIES FOR AUTOMATIC-MULTI-TRACK INITIATION

A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models

Integrating psychometric indicators in latent class choice models

BOOSTERS: a derivative-free algorithm based on radial basis functions

A Benders decomposition for maximum simulated likelihood estimation of advanced discrete choice models

Introducing a preliminary Consists Selection in the Locomotive Assignment Problem

An integrated ordered logit and latent variable model for accident injury severity and risk-taking behavior

An integrated ordered logit and latent variable model for accident injury severity and risk-taking behavior