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: Hôtel-restaurant La Plage, Av. de la plage 5, Préverenges.
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	Elisabetta Cherchi
14:35	-	15:00	Candice Baud
15:00	-	15:25	Youssef El Yaakoubi
15:25	-	15:40	Simple break (no coffee)
15:40	-	16:05	André de Palma
16:05	-	16:30	Yasemin Ovali
16:30	-	16:55	Basil Schmid
16:55	-	17:10	Coffee break
17:10	-	17:35	Marco Kouwenhoven
17:35	-	18:00	Ashish Bhaskar

Friday morning
09:00	-	09:45	Keynote presentation: Prateek Bansal
09:45	-	10:00	Simple break (no coffee)
10:00	-	10:25	Nicola Ortelli
10:25	-	10:50	Pavel Ilinov
10:50	-	11:05	Coffee break
11:05	-	11:30	Mehdi Sheikh Zeinoddin
11:30	-	11:55	Anne Valérie Preto

Friday afternoon
13:30	-	13:55	Kokouvi Joseph Djafon
13:55	-	14:20	Marcel Seger
14:20	-	14:45	Luis Gomez
14:45	-	15:00	Coffee break
15:00	-	15:25	Bastian Henriquez-Jara
15:25	-	15:50	Siyu Wang
15:50	-	16:15	Nathalie Picard
16:15	-	16:20	Short break
16:20	-	16:50	Discussions

Saturday

Hike in the Jura.
Lunch: meat fondue in broth.
Dinner: raclette (melted cheese).

List of participants

Name	First name	Institution	Title	Abstract	Slides
Pavel	Ilinov	StGallen U	A Simple Numerical Method for Rational Inattention with Posterior-Separable Costs	View abstract Posterior-separable cost formulations of rational inattention provide a flexible framework for modeling costly information acquisition. While these models have appealing theoretical properties, numerical methods for computing exact solutions remain less developed. We use convex duality to reformulate the problem as an unconstrained convex optimization problem. This allows the solution to be computed by standard numerical methods, without directly optimizing over information structures. We show how to recover the primal solution and illustrate the algorithm in an investor application with common and idiosyncratic uncertainty.
Schmid	Basil	Swiss Federal Office for Spatial Development (ARE)	Intertemporal VTT changes in Switzerland	View abstract The Swiss value of travel time (VTT) is estimated from a series of national stated preference (SP) surveys conducted since 2010. While pooling these surveys increases sample size and enables more robust model estimation, substantial differences in estimated VTT have been observed across survey waves. This research investigates whether these differences reflect genuine changes in preferences or methodological artifacts. Particular attention is given to changes in the calculation of travel costs, which directly affect the estimated time-money trade-offs. Building on reference-dependent choice theory, a novel utility specification is proposed in which respondents evaluate travel costs relative to the average cost level presented in the SP experiment. This formulation separates behavioral parameter estimation from the subsequent application of VTT to representative travel demand data. Using Swiss national SP route choice data from 2010 to 2025, the proposed model substantially improves comparability and model fit for car route choice and reduces intertemporal inconsistencies in estimated VTT. The results provide strong evidence for reference-dependent valuation of car travel costs, whereas little support is found for public transport. The proposed framework offers a promising approach for improving the behavioral consistency and transferability of VTT estimates across surveys and over time.	830_DCM_Workshop_2026_red.pdf
Ortelli	Nicola	Transports publics genevois (TPG)	The transfer penalty is (mostly) not about transfers: mode-specific discomfort in intermodal urban trips	View abstract	831_mode-specific_discomfort_Ortelli.pdf
Ovali	Yasemin	Koç University	Crowdsourcing for Omnichannel Fulfillment: Integrating Task Assignment and Discrete Choice Models of Customer Participation	View abstract Customer crowdsourcing has emerged as a promising fulfillment approach that leverages in-store customers to perform online order picking activities during their regular shopping trips. While existing studies primarily focus on operational efficiency, successful implementation also depends on customers’ willingness to participate. This research investigates customer crowdsourcing in omnichannel grocery retail from both operational and behavioral perspectives. First, we develop a real-time crowd-picking framework that combines order decomposition and learning-based task assignment to manage fulfillment operations dynamically. Using real-world data, we show that customer-based picking can substantially improve fulfillment efficiency and reduce operational costs. Second, we extend this perspective by examining customer acceptance of crowd-picking tasks through stated preference experiments and discrete choice models. We identify how task characteristics, incentive mechanisms, and customer heterogeneity influence participation decisions. Together, these studies provide a broader understanding of customer crowdsourcing by addressing both the operational feasibility and behavioral acceptance of crowd-picking systems and offer insights for designing scalable customer-based fulfillment models.	834_Crowdsourcing_for_Omnichannel_Fulfillment.pdf
Prateek Bansal	Prateek	National University of Singapore	Integrating Attention and Response Time Data into Cognitive Psychology Models to Understand Discrete Choices	View abstract 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	836_Response_time_and_eye-tracking.pdf
Picard	Nathalie	BETA, Unistra	Migration as an adaptation to climate change	View abstract Global warming induces heat waves, floods and other natural disasters, which makes it more and more difficult to leave in some places, especially urban, and especially in flats (by contrast to houses), and especially for older people. Rather than relying on international migration to "solve the global warming problem" in the very long run (Cruz & Rossi-Hansberg, RES, 2024), thanks to improved productivity and local amenities in Northern countries such as Canada), we study within-country migrations, focusing on heterogeneity in households’ reactions to climate change. We find that households significantly react to climate events (heatwaves, floods and other natural disasters) in their (re-)location decisions. Moreover, the magnitude of the reactions vary significantly over the life cycle, and across tenure status, dwelling types and urban/rural environment. This opens avenues for research on the redistributive and behavioural effects policies related to adaptation to climate change.
El Yaakoubi	Youssef	BETA, Unistra	Negotiation of joint discrete decisions in dual-earner couples	View abstract 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.	434_Youssef_El_Yaakoubi_DCA2026.pdf
DJAFON	Kokouvi Joseph	University of Strasbourg	Whose Marriage Is It Anyway? Intergenerational Preference Gaps, Mutual Misunderstanding, and Norm Misperception in Senegalese Marriage Markets	View abstract When parents hold authority over their daughters’ marriages but preferences diverge, the resulting match fails to maximise the welfare of the woman whose life is at stake. We study this conflict using the largest paired Discrete Choice Experiment on marriage preferences conducted to date. We interview 978 parent–daughter pairs in four Senegalese regions. Each pair independently evaluates 12 hypothetical marriage scenarios that vary on eight attributes (education, employment,husband income, family wealth, polygamy, and endogamy). Combining a stacked conditional logit, a bilateral projection module, and a norm-misperception analysis, we find that preference weights are broadly similar across generations. Both strongly value female education and husband income. Parents are, however, significantly more accepting of polygamous marriage as a second wife. Mutual understanding is imperfect. Parents correctly predict their daughter’s gender-attitude responses only 57% of the time, despite a self-projection rate of 87%, consistent with egocentric imperfect empathy. Within-household divergence in perceived community norms independently predicts DCE disagreement, showing that pluralistic ignorance compounds preference conflict.
Gomez	Luis	BETA, Unistra	You cannot drive my car without me: Car ownership and mode choice within couples	View abstract This paper analyzes the interactions between car ownership and commuting mode choices within couples. We develop a hierarchical discrete choice model based on a non-cooperative Stackelberg framework, where household decisions are modeled as a sequence of interconnected choices. At the first level, the probability that each partner assumes the role of leader or follower within the household is estimated. At the second level, vehicle acquisition decisions are modeled sequentially, with the first vehicle being acquired by the leader and the second by the follower. At the final level, partners jointly choose their modes of transport for commuting to work through a strategic interaction process consistent with a non-cooperative Stackelberg framework. The model is estimated using 2021 population census data from the Île-de-France region provided by the INSEE.	837_Presentation_DCM.pdf
de Palma	andré	CYU CERGY PARIS UNIVERSITE	TBA	View abstract 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	View abstract	433_talk_final.pdf
Cherchi	Elisabetta	New York University Abu Dhabi	Choices under time pressure. An application to the choice of automated taxis.	View abstract
Mehdi	Sheikh Zeinoddin	Unidistance Suisse	The Value of Time: a Dynamic Approach to Passenger Expectations	View abstract With the rise of ride-hailing platforms, rich transaction-level data and high-frequency passenger decisions provide a new opportunity to study the value of time. This paper asks why some passengers revise their choices after requesting a ride, for example by cancelling the request or paying to receive faster service. We argue that these decisions can be understood as responses to passengers’ evolving beliefs about how long they will have to wait before being assigned a driver. We first develop a model of ride assignment that maps how long the passenger has waited for a ride to how much longer he expects to wait. We then embed these expectations in a dynamic discrete choice model in which passengers choose whether to continue waiting, upgrade, or cancel. Using detailed data from the Iranian ride-hailing platform Tapsi, we estimate the ride-assignment component and outline how the resulting expected waiting times can be embedded in a dynamic discrete choice framework to recover passengers’ value of time.	826_3_Jul_2026-_Sheikh_Zeinoddin.pdf
Bierlaire	Michel	EPFL		View abstract
Preto	Anne-Valérie	EPFL	Heterogeneity in Mobility: Latent Classes from Long-Term Tracking Data	View abstract TBD	665_CMP_tourmodel.pdf
Baud	Candice	EPFL	Longitudinal Synthetic Population Generation : A Unified Framework	View abstract 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.	442_DCM_26.pdf
Wang	Siyu	EPFL	A Transformer-Based Activity Schedule Generation Framework	View abstract 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		View abstract
Henriquez-Jara	Bastian	Universidad de Chile	Identification of Q-Learning models under incomplete data and myopic specifications	View abstract 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.	841_Multiattribute_Q_learning.pdf
Kouwenhoven	Marco	Significance	How effective were the electric car purchase subsidies in the Netherlands?	View abstract This study employs discrete choice analysis within a comprehensive modeling framework to assess how effective purchase subsidies have been in driving electric vehicle (EV) adoption in the Netherlands. Central to the methodology is SPARK – a new national car fleet model – which integrates multiple discrete choice models (estimated from full-population vehicle transaction data and EV-focused stated preference surveys) to simulate consumer vehicle choice under varying conditions. The model also incorporates Bass diffusion theory to represent network effects and distinguishes between private and corporate car markets, reflecting their different drivers and sensitivities. To validate SPARK’s behavioral realism, we compare its implied price elasticities with international findings and conduct an independent longitudinal vehicle-type choice analysis using a multinomial logit model on historical data to gauge past policy impacts. By synthesizing these perspectives, the analysis disentangles key drivers of the EV share surge from 5% of new cars in 2018 to 34% in 2024 – including falling EV prices, improved range, and government incentives (purchase subsidies and tax breaks). The results suggest that while incentives did contribute, their role in the EV transition has been more modest than some policymakers expected.	842_Kouwenhoven_-_How_effective_were_purchase_subsidies_for_electric_cars.pdf
Bhaskar	Ashish	Queensland University of Technology	Real time simulation of large urban networks: State-of- the-practice and research needs	View abstract Real-time simulation is becoming a key enabler of intelligent traffic management, allowing agencies to move beyond monitoring towards prediction, optimisation, and proactive network operations. Significant advances in sensing technologies, connected vehicle data, cloud computing, and artificial intelligence have made city-scale digital twins increasingly feasible. However, deploying and maintaining real-time simulation for large urban networks remains a complex scientific and engineering challenge. In this presentation we discuss the current state of practice in real time simulation of large urban networks, with a case study on the development of Aimsun Live model for the city of Logan, Queensland, Australia. The framework integrates multiple components including, heterogeneous data sources, automated data processing, and dynamic demand estimation. The presentation discusses key research challenges that continue to limit widespread deployment, including scalable calibration, uncertainty quantification, data fusion, computational efficiency, behavioral adaptation, and the integration of AI with simulation for network-wide prediction and control. The presentation aims to stimulate discussion on future research directions in advancing real-time digital twins for intelligent urban mobility.
Borboën	Nicolas	EPFL	Test	View abstract Test

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

17th workshop on

Discrete Choice Models