Ir. S. (Sander) van Cranenburgh

Ir. S. (Sander) van Cranenburgh



I am assistant Professor of Choice modelling. My research aim is to develop new methods for enhancing our understanding of human choice behaviour. Understanding choice behaviour and being able to predict it is essential to the efficient functioning of society. For instance, it enables making appropriate provisions to accommodate travel demand when a new railway line is constructed, or when a new service is being introduced.


In my recent work I focus on new methods that bridge the gap between theory-driven discrete choice models and data-driven Artificial Intelligence (AI) models. Until recently, the discrete choice modelling field was almost exclusively based on theory-driven models. I try to push the frontier of this field by blending theory-driven discrete choice models with data-driven AI models. Doing so creates a whole new set of tools to investigate choice behaviour. Moreover, bringing together these modelling paradigms holds the potential to get the best of both: the flexibility and versatility of data-driven methods and the rigour and strong behavioural inference of theory-driven methods. See for the latest research on this fascinating topic by me and my co-workers.


In my Post-doc years (2013-2014) I have made a series of contributions to Random Regret Minimisation (RRM) based discrete choice models. RRM models are a behaviourally inspired counterpart of the classical Random Utility Maximisation model. I have developed: a new family of RRM models, new data collection methodology, and the world’s first RRM-based national transport model. See my personal website for the latest developments in Random Regret Minimization (RRM) modelling, experimental design software for RRM models (Ngene & MATLAB), and estimation codes for RRM models for Biogeme (Bison, Python & Pandas), R (Apollo)MATLAB, and LatentGold Choice.

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