Recherche

Contexte

Le transport des personnes et des marchandises est omniprésent et connaît une transformation qui aura un impact énorme sur la société, l'environnement et l'économie. La demande est le moteur de cette transformation en adoptant les nouveaux services de transport. Il en résulte des systèmes de transport intégrés (TS) dans lesquels les distinctions historiques entre les modes de transport privés et publics ainsi qu'entre les personnes et les marchandises sont floues. Nous développons des méthodologies pour prévoir la demande et optimiser l'offre de TS intégrés en tenant compte de la réponse de la demande. Pour atteindre cet objectif, nous développons (i) des algorithmes d'apprentissage statistique et (ii) des modèles d'optimisation et des méthodes de solutions.

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Articles

Soumis, en révision, accepté et publié. Les étudiants et postdocs sont indentifiés d'une étoile.

Perspectives on Optimizing Transportation Systems with Interacting Supplies and Demands

Emma Frejinger et Mike Hewitt

Soumis à Transportation Science le 9 Octobre, 2021.

A Two-step Heuristic for the Periodic Demand Estimation Problem

Greta Laage, Emma Frejinger et Gilles Savard

Soumis à Transportation Research Part B le 18 août 2021.

arxiv.org/abs/2108.08331

Estimation of Undiscounted Recursive Path Choice Models: Convergence Properties and Algorithms

Tien Mai et Emma Frejinger

Soumis au Transportation Science, 2021.

Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?

Tien Mai, Xinlian Yu, Song Gao et Emma Frejinger

Soumis au Omega, 2021.

Periodic Freight Demand Forecasting for Large-scale Tactical Planning

Greta Laage, Emma Frejinger et Gilles Savard

Version révisée soumise au Transportation Research Part B le 14 septembre, 2021.

The Load Planning and Sequencing Problem for Double-Stack Intermodal Trains

Mortiz Ruf, Jean-François Cordeau and Emma Frejinger

Version révisée soumise au Transportation Research Part E le 12 septembre, 2021.

A Time-Space Formulation for the Locomotive Routing Problem at the Canadian National Railways

Pedro Miranda, Jean-François Cordeau et Emma Frejinger

Accepté pour publication au Computers and Operations Research le 7 novembre, 2021.

https://www.cirrelt.ca/documentstravail/cirrelt-2020-19.pdf

Routing Policy Choice Prediction in a Stochastic Network: Recursive Model and Solution Algorithm

Tien Mai, Xinlian Yu, Song Gao et Emma Frejinger

Transportation Research Part B, 151: 42-58, Juillet 2021.

10.1016/j.trb.2021.06.016

Data-driven Optimization Model Customization

Michael Hewitt et Emma Frejinger

European Journal on Operations Research, 287(2): 438-451, 2020.

10.1016/j.ejor.2020.05.010

Integrated Inbound Train Split and Load Planning in an Intermodal Railway Terminal

*Bruno Bruck, Jean-François Cordeau et Emma Frejinger

Transportation Research Part B, 145: 270-289, 2021.

10.1016/j.trb.2021.01.006

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

*Eric Larsen, *Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien et Andrea Lodi

Publié en ligne sur INFORMS Journal on Computing le 21 septembre, 2021.

10.1287/ijoc.2021.1091

The Locomotive Assignment Problem with Distributed Power at the Canadian National Railway Company

*Camilo Ortiz-Astorquiza, Jean-François Cordeau et Emma Frejinger

Accepté pour publication au Transportation Science le 14 Octobre, 2020.

A tutorial on recursive models for analyzing and predicting path choice behavior

*Maëlle Zimmermann et Emma Frejinger

Accepté pour publication au EURO Journal on Transportation and Logistics le 3 Décembre, 2019.

https://doi.org/10.1016/j.ejtl.2020.100004

A Strategic Markovian Traffic Equilibrium Model for Capacitated Networks

*Maëlle Zimmermann, Emma Frejinger et Patrice Marcotte

Transportation Science. Volume: 55, Numéro: 3 (Mai-Juin 2021): 574-591.

https://doi.org/10.1287/trsc.2020.1033

Route choice behavior and travel information in a congested network: Static and dynamic recursive models

*Giselle De Moraes Ramos, *Tien Mai, Winnie Daamen, Emma Frejinger et Serge Hoogendoorn

An empirical study on aggregation of alternatives and its influence on prediction in car type choice models

*Shiva Habibi, Emma Frejinger et Markus Sundberg

Transportation 46(3): 563-582, 2019.

https://doi.org/10.1007/s11116-017-9828-5

The Load Planning Problem for Double-Stack Intermodal Trains

*Serena Mantovani, *Gianluca Morganti, *Nitish Umang, Teodor Gabriel Crainic, Emma Frejinger et *Eric Larsen

European Journal  of Operations Research 267(1): 107-119, 2018.

https://doi.org/10.1016/j.ejor.2017.11.016

A decomposition method for estimating recursive logit based route choice models

*Tien Mai, Fabian Bastin et Emma Frejinger

EURO Journal on Transportation and Logistics 7(3):253-275, 2018.

https://doi.org/10.1016/j.ejtl.2020.100004

Capturing correlation with a mixed recursive logit model for activity-travel scheduling

*Maëlle Zimmermann, *Oscar Blom Västberg, Emma Frejinger et Anders Karlström

Transportation Research Part C: Emerging Technologies 93: 273-291, 2018.

https://doi.org/10.1016/j.trc.2018.05.032

A dynamic programming approach for quickly estimating large network-based MEV models

*Tien Mai, Emma Frejinger, Mogens Fosgerau et Fabian Bastin

Transportation Research Part B 98(1): 179-197, 2017.

https://doi.org/10.1016/j.trb.2016.12.017

Bike route choice modelling using GPS data without choice sets of paths

*Maëlle Zimmermann, *Tien Mai et Emma Frejinger

Transportation Research Part C 75(1): 183-196, 2017.

https://doi.org/10.1016/j.trc.2016.12.009

On the similarities between random regret minimization and mother logit : The case of recursive route choice models

*Tien Mai, Fabian Bastin et Emma Frejinger

Journal of Choice Modelling 23(1): 21-33, 2017.

https://doi.org/10.1016/j.jocm.2017.03.002

A misspecification test for logit-based route choice models

*Tien Mai, Emma Frejinger et Fabian Bastin

Economics of Transportation. 4(4): 215-226, 2015.

https://doi.org/10.1016/j.ecotra.2015.08.002

A nested recursive logit model for route choice analysis

*Tien Mai, Mogens Fosgerau et Emma Frejinger

Transportation Research Part B 75(1): 100-112, 2015.

https://doi.org/10.1016/j.trb.2015.03.015

A link-based network route choice model with unrestricted choice set

Mogens Fosgerau, Emma Frejinger et Anders Karlstöm

Transportation Research Part B 56(1): 70-80, 2013.

https://doi.org/10.1016/j.trb.2013.07.012

Cognitive Cost in Route Choice with Real-Time Traffic Information: An Exploratory Analysis

Song Gao, Emma Frejinger et Moshe Ben-Akiva

Transportation Research Part A 45(9): 916-926, 2011.

https://doi.org/10.1016/j.sbspro.2011.04.511

Adaptive route choices in risky traffic networks: A prospect theory approach

Song Gao, Emma Frejinger et Moshe Ben-Akiva

Transportation Research Part C 18(5): 727-740, 2010.

https://doi.org/10.1016/j.trc.2009.08.001

Sampling of alternatives for route choice modeling

Emma Frejinger, Michel Bierlaire et Moshe Ben-Akiva

Transportation Research Part B 43(10): 984-994, 2009.

https://doi.org/10.1016/j.trb.2009.03.001

Adaptive Route Choice Models in Stochastic Time-Dependent networks

Song Gao, Emma Frejinger et Moshe Ben-Akiva

Transportation Research Record 2085: 136-143, 2008.

https://doi.org/10.3141/2085-15

Route choice modeling with network-free data

Michel Bierlaire et Emma Frejinger

Transportation Research Part C 16(2): 187-198, 2008.

https://doi.org/10.1016/j.trc.2007.07.007

Capturing correlation with subnetworks in route choice models

Emma Frejinger et Michel Bierlaire

Transportation Research Part B 41(3): 363-378, 2007.

https://doi.org/10.1016/j.trb.2006.06.003

 

Conférences

A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs

Yoshua Bengio, Emma Frejinger, Andrea Lodi, *Rahul Patel et *Sriram Sankaranarayanan

Seventeenth International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR). Acceptée pour publication, 2020.

arXiv:1912.08112

Block planning for intermodal rail: methodology and case study

*Gianluca Morganti, Teodor Gabriel Crainic, Emma Frejinger et Nicoletta Ricciardi

Publiée au Transportation Research Procedia 47, 19–26, 2020.

https://doi.org/10.1016/j.trpro.2020.03.068

 

Logiciel

RECURSIVE LOGIT ESTIMATION