4 papers · 1 filter
Flexibility without foresight: the predictive limitations of mixture models
Stephane Hess, Sander van Cranenburgh
Models allowing for random heterogeneity, such as mixed logit and latent class, are generally observed to obtain superior model fit and yield detailed insights into unobserved pref…
Can large language models assist choice modelling? Insights into prompting strategies and current models' capabilities
Georges Sfeir, Gabriel Nova, Stephane Hess +1
Large Language Models (LLMs) are becoming widely used to support various workflows across different disciplines, yet their potential in discrete choice modelling remains relatively…
Delphos: A reinforcement learning framework for assisting discrete choice model specification
Gabriel Nova, Stephane Hess, Sander van Cranenburgh
We introduce Delphos, a deep reinforcement learning framework for assisting discrete choice model specification process. Delphos aims to support the modeller by providing automated…
Combine and conquer: model averaging for out-of-distribution forecasting
Stephane Hess, Sander van Cranenburgh
Travel behaviour modellers have an increasingly diverse set of models at their disposal, ranging from traditional econometric structures to models from mathematical psychology and…