Bonjour à tous,

Vous êtes tous et toutes cordialement invité.es à assister à la présentation de projet du prédoc III de Ben Hudson, le 28 août à 13h30. La présentation sera en anglais.

Titre : User behaviour model learning for network design with decision-dependent uncertainty

Date: jeudi 28 août à 13h30

Location: Pavillon André-Aisenstadt, salle 5441, 2920 Ch. de la tour

Jury

Résumé

We desire to optimize the efficiency of a transportation network where the optimal design depends on how travel demand distributes itself across the network. However, the travel demand itself is shaped by the network design. This introduces a complex feedback loop: decisions influence the probability distribution of outcomes, and those outcomes in turn affect the optimality of the decisions. This interaction, referred to as decision-dependent or endogenous uncertainty, significantly complicates solving the network design problem.

Solving this problem requires modelling traveller behaviour and leveraging those models to predict how travel patterns will shift in a redesigned network. The first challenge relates to the topics of route choice and traffic equilibrium modelling; the second corresponds to a stochastic network design problem with endogenous uncertainty. From the gaps in the literature, we identify three primary research questions: (i) How can we learn the full perturbation distribution in perturbed utility models and decision-focused learning, beyond the expected utility? (ii) How can we learn the link cost (disutility) function in Markovian traffic equilibrium models from observational data? (iii) How can we solve stochastic network design problems where user behaviour is both uncertain and influenced by the design itself?

To address the first question, we propose an estimation approach for perturbed utility models based on stochastic smoothing. Exploratory results suggest that our model may capture the complex relationship between traveller behaviour and network design more effectively than existing approaches. For the second question, we outline initial ideas developed in collaboration with researchers at EPFL. Finally, for the third question, we describe how predictions from these models of user behaviour might be adapted to tackle the stochastic network design problem with decision-dependent uncertainty.

The ultimate goal of this research is to equip transportation planners with tools to better
model traveller behaviour and implement network improvements, thereby enabling transportation systems that are more efficient, reliable, and equitable for all users.