6 papers
Using machine learning metrics to provide deeper insights into the performance of choice models
Lorenzo Muñoz, Stephane Hess, Thomas O. Hancock +1
Machine learning (ML) techniques are increasingly drawing interest in the choice modelling (CM) field. The focus has primarily been on comparing the performance of these contrastin…
A Bayesian latent class reinforcement learning framework to capture adaptive, feedback-driven travel behaviour
Georges Sfeir, Stephane Hess, Thomas O. Hancock +5
Many travel decisions involve a degree of experience formation, where individuals learn their preferences over time. At the same time, there is extensive scope for heterogeneity ac…
Beyond utility: incorporating eye-tracking, skin conductance and heart rate data into cognitive and econometric travel behaviour models
Thomas O. Hancock, Stephane Hess, Charisma F. Choudhury
Choice models for large-scale applications have historically relied on economic theories (e.g. utility maximisation) that establish relationships between the choices of individuals…
Numerical evaluation of deliberative discussions of the UK food system: stimuli, demographics, and opinion reversion
John Buckell, Thomas Hancock
There is increasing acknowledgement - including from the UK government - of the benefit of employing deliberative processes (deliberative fora, citizens' juries, etc.). Evidence su…
A break from the norm? Parametric representations of preference heterogeneity for discrete choice models in health
John Buckell, Alice Wreford, Matthew Quaife +1
Background: Any sample of individuals has its own, unique distribution of preferences for choices that they make. Discrete choice models try to capture these distributions. Mixed l…
A generalised discrete mixture model to better capture preference heterogeneity in discrete choice data
Thomas O. Hancock, John Buckell
Arguably the key issue in modelling discrete choice data is capturing preference heterogeneity. This can be through observed characteristics, and/or using techniques for capturing…