5 papers
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 the discrete choice model specification process. Delphos aims to support the modeller by providing autom…
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…
Understanding the decision-making process of choice modellers
Gabriel Nova, Sander van Cranenburgh, Stephane Hess
Discrete Choice Modelling serves as a robust framework for modelling human choice behaviour across various disciplines. Building a choice model is a semi structured research proces…
A Unified Probabilistic Approach to Traffic Conflict Detection
Yiru Jiao, Simeon C. Calvert, Sander van Cranenburgh +1
Traffic conflict detection is essential for proactive road safety by identifying potential collisions before they occur. Existing methods rely on surrogate safety measures tailored…