Catégorie
Utilité (économie)
Publications associées (90)
OASIS: An integrated optimisation framework for activity scheduling
Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...
EPFL2024Stochastic pairwise preference convergence in Bayesian agents
Beliefs inform the behaviour of forward-thinking agents in complex environments. Recently, sequential Bayesian inference has emerged as a mechanism to study belief formation among agents adapting to dynamical conditions. However, we lack critical theory to ...
2024Generative AI-Enabled Conversational Interaction to Support Self-Directed Learning Experiences in Transversal Computational Thinking
Denis Gillet, Juan Carlos Farah, Adrian Christian Holzer, Abdessalam Ouaazki
As computational thinking (CT) becomes increasingly acknowledged as an important skill in education, self-directed learning (SDL) emerges as a key strategy for developing this capability. The advent of generative AI (GenAI) conversational agents has disrup ...
2024Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK
Daniel Gatica-Perez, Lakmal Buddika Meegahapola, Karim Assi
Smartphones enable understanding human behavior with activity recognition to support peoples daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous ...
New York2023Yanaka - Vers un renouvellement urbain socialement durable
À l’heure où la Bigness tokyoïte s’empare de la petite échelle et de toutes les potentialités qui lui sont rattachées, ce projet propose une solution de renouvellement urbain durable pour le quartier de Yanaka. Ayant largement survécu aux destructions mass ...
2023Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning
Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...
EPFL2023Deficit Round-Robin: A Second Network Calculus Analysis
Jean-Yves Le Boudec, Seyed Mohammadhossein Tabatabaee
Deficit Round-Robin (DRR) is a widespread scheduling algorithm that provides fair queueing with variable-length packets. Bounds on worst-case delays for DRR were found by Boyer et al., who used a rigorous network calculus approach and characterized the ser ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022Optimization and equilibrium problems with discrete choice models
Many transportation markets are characterized by oligopolistic competition. In these markets customers, suppliers and regulators make decisions that are influenced by the preferences and the decisions of all other agents. In particular, capturing and under ...
EPFL2022Capturing trade-offs between daily scheduling choices
Michel Bierlaire, Timothy Michael Hillel, Janody Pougala
We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The funda ...
ELSEVIER SCI LTD2022Citizen Bench. Seating needs and Preferences
The project "Citizen Bench" aimed a better understanding of the variety of urban seating-needs. This report summarises the results of the citizen engagement project on public benches in the City of Munich. In the framework of this pilot project, a method ...
EPFL2022