Ir. S. van Cranenburgh
Ir. S. 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 www.AI4ChoiceLab.com 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 www.advancedRRMmodels.com 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.
Data-driven assisted model specification for complex choice experiments data
Association rules learning and random forests for Participatory Value Evaluation experiments
Jose Ignacio Hernandez / Sander van Cranenburgh / Caspar Chorus / Niek Mouter
Choice modelling in the age of machine learning - Discussion paper
Sander van Cranenburgh / Shenhao Wang / Akshay Vij / Francisco Pereira / Joan Walker
Decision Field Theory
Equivalence with probit models and guidance for identifiability
Teodóra Szép / Sander van Cranenburgh / Caspar G. Chorus
Perceived challenges and opportunities of machine learning applications in governmental organisations
an interview-based exploration in the Netherlands
Jeroen Delfos / Anneke Zuiderwijk / Sander Van Cranenburgh / Caspar Chorus
Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands
Sanmay Shelat / Oded Cats / Sander van Cranenburgh
De homo economicus heeft twee gezichten
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