The workshop is an informal meeting for the exchange of ideas around discrete choice models, with the objective to trigger new collaborations, or strengthen existing ones, and to expose PhD students to the international community. The participation to the workshop is by invitation only.

Registration fee: 300 CHF.

The registration fee includes: dinner on Thursday, lunch on Friday, lunch on Saturday and coffee breaks.

Keynote speaker: Prateek Bansal

National University of Singapore,

Integrating Attention and Response Time Data into Cognitive Psychology Models to Understand Discrete Choices

Traditional choice data captures outcomes, not processes. Response times and attention patterns, increasingly available at scale through webcam-based eye-tracking, reveal how decisions are made, not just what was chosen. Standard utility models struggle to absorb these signals or explain phenomena like the decoy effect and intra-individual heterogeneity. This talk makes the case for sequential sampling models as a more natural framework: one where attention and response time emerge directly from the decision process, improving both the interpretability of estimates and the efficiency of inference

Prateek
Bansal

Prateek Bansal is a Presidential Young (Assistant) Professor at the National University of Singapore (NUS). Before joining NUS in 2022, he was a Leverhulme Trust Early Career Fellow at Imperial College London and did a Ph.D. from Cornell, an MSc from UT Austin, and a BTech from IIT Delhi. Prateek leads the Behavioural Computational Science Lab at NUS and the Adaptive Mobility module at Future Cities Laboratory Global. His research group is interested in creating new methods to address challenging questions related to mobility behavior and the adoption of emerging technologies at an individual level and on an urban scale. His research has led to over 80 journal articles. Apart from the top Transportation journals, he regularly publishes in interdisciplinary journals like Nature Communications and Statistics and Computing. He serves as the Associate Editor of Transportation Research Part A: Policy and Practice and Transportation Research Part B: Methodological. He is a member of the TRB's standing committees on Travel Data & Methods (AED17) and an elected board member of the International Association of Travel Behavior Research (IATBR).

More Info

Venue

The workshop will take place at Ecole Polytechnique Fédérale de Lausanne. Room: GC B1 10

Restaurants

Dinner on Thursday: TBD.
Lunch on Friday: Restaurant Gina, EPFL.
Lunch on Saturday: Restaurant La Gittaz, La Gittaz-Dessous 325, 1450 Ste Croix.

Program (tentative)

Thursday afternoon
14:00	-	14:10	Welcome
14:10	-	14:35	Talk
14:35	-	15:00	Talk
15:00	-	15:25	Talk
15:25	-	15:35	Simple break (no coffee)
15:35	-	16:00	Talk
16:00	-	16:25	Talk
16:25	-	16:40	Coffee break
16:40	-	17:05	Talk
17:05	-	17:30	Talk
17:30	-	17:55	Talk

Friday morning
09:00	-	09:45	Keynote presentation
09:45	-	09:55	Simple break (no coffee)
09:55	-	10:20	Talk
10:20	-	10:45	Talk
10:45	-	11:00	Coffee break
11:00	-	11:25	Talk
11:25	-	11:50	Talk
11:50	-	12:15	Talk

Friday afternoon
13:45	-	14:10	Talk
14:10	-	14:35	Talk
14:35	-	15:00	Talk
15:00	-	15:15	Coffee break
15:15	-	15:40	Talk
15:40	-	16:05	Talk
16:05	-	16:15	Simple break (no coffee)
16:15	-	16:45	Discussions

Saturday

Boat trip Lausanne-Vevey.
Lunch: traditional cheese fondue.
Walk through the vineyards of Lavaux [Click here].
Dinner: BBQ.

List of participants

Name	First name	Institution	Title
Pavel	Ilinov	StGallen U	TBA
TBA
Schmid	Basil	Swiss Federal Office for Spatial Development (ARE)	A pooled RP/SP mode, route and departure time choice model to investigate travel preferences in Switzerland
As part of the Swiss Mobility and Transport Microcensus (MTMC) 2025, Switzerland's representative travel survey that is conducted every five years, an SP-survey on mode, route and departure time choice was conducted for a subsample of 6’235 respondents. The aim is to provide an empirical basis for the Swiss national transport model (NPVM), the transport perspectives (VP) as well as the value of time studies to update the cost-benefit norms. The SP-tasks are personalized for each respondent based on a revealed preference (RP) trip observed in the MTMC. Based on this dataset, a pooled RP/SP mode, route and departure time choice model will be estimated, accounting for different types of preference heterogeneity.
Ortelli	Nicola	Transports publics genevois (TPG)	TBA

Ovali	Yasemin	Koç University

Prateek Bansal	Prateek	National University of Singapore	Integrating Attention and Response Time Data into Cognitive Psychology Models to Understand Discrete Choices
Traditional choice data captures outcomes, not processes. Response times and attention patterns, increasingly available at scale through webcam-based eye-tracking, reveal how decisions are made, not just what was chosen. Standard utility models struggle to absorb these signals or explain phenomena like the decoy effect and intra-individual heterogeneity. This talk makes the case for sequential sampling models as a more natural framework: one where attention and response time emerge directly from the decision process, improving both the interpretability of estimates and the efficiency of inference
Picard	Nathalie	BETA, Unistra	TBA
TBA
El Yaakoubi	Youssef	BETA, Unistra	Negotiation of joint discrete decisions in dual-earner couples
We develop a collective discrete-choice framework for joint decisions in dual-earner households. Observed outcomes are Pareto efficient and represented by a weighted aggregation of individual utilities, where the Pareto weight captures intra-household bargaining power. The key identification challenge is that this weight and individual utility scales are not separately identified without additional structure. We show that combining egotistic preferences, the private-good structure of individual-specific attributes, and a money-metric normalization delivers point identification of the baseline level of bargaining power, rather than only its variations across households as in existing collective models. The framework requires no observations on prices, income, or budget shares, which opens it to a wide range of empirical settings beyond traditional consumption data. We embed the collective structure in a mixed logit with shared error components, estimated by Bayesian methods, and illustrate the framework on joint commuting and vehicle ownership choices of dual-earner couples from the Paris region.
DJAFON	Kokouvi Joseph	University of Strasbourg	Trading Off Autonomy: How Girls and Parents Weight Education, Work, and Marriage? Evidence from a Discrete Choice Experiment in Senegal.
We study how parents and daughters in Senegal evaluate trade-offs between girls’ education, employment, and marriage timing. Drawing on qualitative fieldwork, we design a Discrete Choice Experiment and estimate mixed logit models to capture unobserved taste variations. We find that while girls’ human capital and labor market participation are highly valued, these preferences weaken when the prospective husband is economically advantaged, suggesting a substitution logic. Preferences vary by region, gender, household structure, and economic vulnerability. The findings reveal that social norms and economic constraints jointly shape aspirations and behaviors. Even when aspirations for education and employment are high, immediate marriage-market opportunities can lead families to deprioritize long-term investments in girls’ autonomy. The paper contributes to understanding how households navigate normative and economic pressures and calls for policies that go beyond supply-side access, addressing the demand-side logic of marriage decisions.
Gomez	Luis	BETA, Unistra	TBA

de Palma	andré	CYU CERGY PARIS UNIVERSITE	TBA
TBA
Seger	Marcel	University of Oxford	Commuter preferences for smart and bidirectional EV workplace charging: Evidence from a stated choice experiment in the UK and Germany

Cherchi	Elisabetta	New York University Abu Dhabi

Mehdi	Sheikh Zeinoddin	Unidistance Suisse	The Value of Time: a Dynamic Approach to Passenger Expectations
TBA
Bierlaire	Michel	EPFL

Preto	Anne-Valérie	EPFL	Heterogeneity in Mobility: Latent Classes from Long-Term Tracking Data
TBD
Baud	Candice	EPFL	Longitudinal Synthetic Population Generation : A Unified Framework
Most methods for generating synthetic populations rely on cross-sectional snapshots or pseudo-panels, which do not track individuals consistently over time. This paper proposes a general framework for constructing synthetic populations whose panel structure is specified by design. Individuals are represented through life-based trajectories defined independently of calendar time, and a deterministic mapping recovers their state at any time t, allowing the reconstruction of population distributions at arbitrary points in time. The framework is model-agnostic and enforces internal consistency through structural constraints embedded in the life representation. We further introduce a Bayesian updating mechanism that incorporates information from observed cross-sectional datasets. When data are available, the synthetic population is sampled from the posterior distribution, combining prior knowledge with the evidence contained in the observations. This allows cross-sectional information, such as census data, to inform the generation of coherent longitudinal populations.
Wang	Siyu	EPFL	A Transformer-Based Activity Schedule Generation Framework
The generative modelling of activities requires joint representation of multiple dimensions, including activity type, frequency, start time, and sequence, to capture individuals’ daily activity schedules. Transformers based on self-attention mechanisms have shown strong capabilities in modeling complex nonlinear dependencies in sequential data, making activity sequence generation a natural application scenario and new insights into travel demand modelling. This study incorporates the OASIS (Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions) framework as a complementary section. We first evaluate the predictive performance of the Transformer model for key scheduling dimensions, including activity type and start time-of-day, and compare the modelling result with OASIS. The sample data from the 2021 Swiss Mobility and Transport Microcensus (MTMC) of non-commuters is utilized. Simulation results are assessed at the group-level aggregated distribution, examining the ability to reproduce observed activity-travel patterns. This framework provides another perspective for data-driven activity schedule generation and insight into the mechanisms underlying activity-travel behavior.
Paschalidis	Evangelos	EPFL

Henriquez-Jara	Bastian	Universidad de Chile	Identification of Q-Learning models under incomplete data and myopic specifications
The implementation of reinforcement learning (RL) models requires very specific datasets containing the entire choice history of participants, in addition to the outcomes/rewards obtained after each choice. However, typical datasets collected outside controlled laboratory conditions do not satisfy these require- ments. Usually, either the choice history is incomplete, or there is a partial or total lack of information about the obtained outcomes. In this paper, we explore how a simple RL algorithm, multiattribute Q-learning, can be identified under incomplete data conditions, accounting for panel structure. Then, we explore model simplifications that allow us to retrieve consistent parameters or parameter ratios. Ongoing results show that: 1) myopic specifications (i.e., ignoring the dynamic Markov structure of Q-learning) result in inconsistent parameter estimates and parameter ratios; and 2) spurious state dependence (Heckman, 1981) is found with MNL models that account for inertia when the data-generating process is Q-learning.
Kouwenhoven	Marco	Significance	(will follow)
(will follow)

The workshop is organized by the Transport and Mobility Laboratory, EPFL.

17th workshop on