Research
Background
The transportation of people and freight is ubiquitous and undergoes a transformation that will have tremendous impact on the society, environment and economy. Demand drives this transformation by adopting emerging transport services. This results in integrated transport systems (TS) where the historical separations between private and public transport modes as well between people and freight are blurred. We develop methodologies to predict demand and optimize supply of integrated TS taking the demand response into account. To achieve this objective we develop (i) statistical learning algorithms and (ii) optimization models and solutions methods.
Journal Articles
Submitted, under revision, accepted and published. Students and postdocs are indicated with a star.
Perspectives on Optimization with Interacting Supplies and Demands
Emma Frejinger and Mike Hewitt
INFOR: Information Systems and Operational Research, 1–34.
DOI: 10.1080/03155986.2025.2478699
Combining supervised learning and local search for the multicommodity capacitated fixed-charge network design problem
Charly Robinson La Rocca, Jean-François Cordeau and Emma Frejinger
Transportation Research Part E, Volume 192, 2024.
One-shot Learning for MIPs with SOS1 Constraints
Charly Robinson La Rocca, Jean-François Cordeau and Emma Frejinger
Operations Research Forum, 5(57), 2024.
Reinforcement learning for freight booking control problems
Justin Dumouchelle, Emma Frejinger and Andrea Lodi
Journal of Revenue and Pricing Management, 23: 318-345, 2024.
DOI: 10.1057/s41272-023-00459-1
A Survey of Contextual Optimization Methods for Decision-Making under Uncertainty
Erick Delage, Utsav Sadana, Abhilash Chenreddy, Alexandre Forel, Emma Frejinger and Thibaut Vidal
European Journal of Operational Research, 320(2): 271-448. 2025.​
A model-free approach for solving choice-based competitive facility location problems using simulation and submodularity
Robin Legault and Emma Frejinger
INFORMS Journal on Computing, published online on July 15, 2024.
Optimising Electric Vehicle Charging Station Placement using Advanced Discrete Choice Models
Steven Lamontagne, Margarida Carvalho, Emma Frejinger, Bernard Gendron, Miguel F. Anjos and Ribal Atallah.
INFORMS Journal on Computing, 35(5),1195–1213, 2023.
​Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning
Eric Larsen, Emma Frejinger, Bernard Gendron and Andrea Lodi.
INFORMS Journal on Computing, 36(1), 2023.
DOI: 10.1287/ijoc.2022.0175
A Logistics Provider's Profit Maximization Facility Location Problem with Random Utility Maximizing Followers
David Pinzon, Emma Frejinger and Bernard Gendron.
Computers & OR, (167) 106649, 2024.
DOI: 10.48550/arXiv.2303.06749
The Load Planning and Sequencing Problem for Double-Stack Trains
Mortiz Ruf, Jean-François Cordeau and Emma Frejinger
Journal of Rail Transport Planning & Management 23:100337, 2022.
Undiscounted Recursive Path Choice Models: Convergence Properties and Algorithms
Tien Mai and Emma Frejinger
Transportation Science, 56(6):1469-1482, May 2022.
A Time-Space Formulation for the Locomotive Routing Problem at the Canadian National Railways
Pedro Miranda, Jean-François Cordeau and Emma Frejinger
Computers and Operations Research, 139: 105629, november 2021.
Routing Policy Choice Prediction in a Stochastic Network: Recursive Model and Solution Algorithm
Tien Mai, Xinlian Yu, Song Gao and Emma Frejinger
Transportation Research Part B, 151: 42-58, July 2021.
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
*Eric Larsen, *Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien and Andrea Lodi
INFORMS Journal on Computing, 34(1):227-242, 2022.
The Locomotive Assignment Problem with Distributed Power at the Canadian National Railway Company
*Camilo Ortiz-Astorquiza, Jean-François Cordeau and Emma Frejinger
Transportation Science. 55(2): 275-552, 2021.
A Strategic Markovian Traffic Equilibrium Model for Capacitated Networks
*Maëlle Zimmermann, Emma Frejinger and Patrice Marcotte
Transportation Science. 55(3): 574-591, 2021.
DOI: 10.1287/trsc.2020.1033
Integrated Inbound Train Split and Load Planning in an Intermodal Railway Terminal
*Bruno Bruck, Jean-François Cordeau and Emma Frejinger
Transportation Research Part B, 145: 270-289, 2021.
Data-driven Optimization Model Customization
Michael Hewitt and Emma Frejinger
European Journal on Operations Research, 287(2): 438-451, 2020.
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 and Serge Hoogendoorn
Transportation Research Part C: Emerging Technologies, 114: 681-693, 2020.
A tutorial on recursive models for analyzing and predicting path choice behavior
*Maëlle Zimmermann and Emma Frejinger
EURO Journal on Transportation and Logistics, 9(2):100004, 2020.
An empirical study on aggregation of alternatives and its influence on prediction in car type choice models
*Shiva Habibi, Emma Frejinger and Markus Sundberg
Transportation 46(3): 563-582, 2019.
The Load Planning Problem for Double-Stack Intermodal Trains
*Serena Mantovani, *Gianluca Morganti, *Nitish Umang, Teodor Gabriel Crainic, Emma Frejinger and *Eric Larsen
European Journal of Operations Research 267(1): 107-119, 2018.
A decomposition method for estimating recursive logit based route choice models
*Tien Mai, Fabian Bastin and Emma Frejinger
EURO Journal on Transportation and Logistics 7(3):253-275, 2018.
Capturing correlation with a mixed recursive logit model for activity-travel scheduling
*Maëlle Zimmermann, *Oscar Blom Västberg, Emma Frejinger and Anders Karlström
Transportation Research Part C: Emerging Technologies 93: 273-291, 2018.
A dynamic programming approach for quickly estimating large network-based MEV models
*Tien Mai, Emma Frejinger, Mogens Fosgerau and Fabian Bastin
Transportation Research Part B 98(1): 179-197, 2017.
Bike route choice modelling using GPS data without choice sets of paths
*Maëlle Zimmermann, *Tien Mai and Emma Frejinger
Transportation Research Part C 75(1): 183-196, 2017.
On the similarities between random regret minimization and mother logit : The case of recursive route choice models
*Tien Mai, Fabian Bastin and Emma Frejinger
Journal of Choice Modelling 23(1): 21-33, 2017.
A misspecification test for logit-based route choice models
*Tien Mai, Emma Frejinger and Fabian Bastin
Economics of Transportation. 4(4): 215-226, 2015.
A nested recursive logit model for route choice analysis
*Tien Mai, Mogens Fosgerau and Emma Frejinger
Transportation Research Part B 75(1): 100-112, 2015.
A link-based network route choice model with unrestricted choice set
Mogens Fosgerau, Emma Frejinger and Anders Karlstöm
Transportation Research Part B 56(1): 70-80, 2013.
Cognitive Cost in Route Choice with Real-Time Traffic Information: An Exploratory Analysis
Song Gao, Emma Frejinger and Moshe Ben-Akiva
Transportation Research Part A 45(9): 916-926, 2011.
Adaptive route choices in risky traffic networks: A prospect theory approach
Song Gao, Emma Frejinger and Moshe Ben-Akiva
Transportation Research Part C 18(5): 727-740, 2010.
Sampling of alternatives for route choice modeling
Emma Frejinger, Michel Bierlaire and Moshe Ben-Akiva
Transportation Research Part B 43(10): 984-994, 2009.
Adaptive Route Choice Models in Stochastic Time-Dependent networks
Song Gao, Emma Frejinger and Moshe Ben-Akiva
Transportation Research Record 2085: 136-143, 2008.
Route choice modeling with network-free data
Michel Bierlaire and Emma Frejinger
Transportation Research Part C 16(2): 187-198, 2008.
Capturing correlation with subnetworks in route choice models
Emma Frejinger and Michel Bierlaire
Transportation Research Part B 41(3): 363-378, 2007.
Conference Proceedings
Maximum entropy GFlowNets with soft Q-learning
*Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger and Pierre-Luc Bacon
Artificial Intelligence and Statistics 2024.
Decoupling regularization from the action space
*Sobhan Mohammadpour, Emma Frejinger and Pierre-Luc Bacon
International Conference on Learning Representations 2024
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs
Yoshua Bengio, Emma Frejinger, Andrea Lodi, *Rahul Patel and *Sriram Sankaranarayanan
Seventeenth International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR). Accepted for publication, 2020.
Block planning for intermodal rail: methodology and case study
*Gianluca Morganti, Teodor Gabriel Crainic, Emma Frejinger and Nicoletta Ricciardi
Published in Transportation Research Procedia 47, 19–26, 2020.