Ir. S. (Sander) van Cranenburgh

Ir. S. (Sander) van Cranenburgh

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Biography

I’m an Associate Professor of Choice modelling. My research aim is to develop new models for enhancing our understanding of human choice behaviour. Understanding choice behaviour better and being able to accurately 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.

My recent works have focused on developing new choice models that can effectively handle unstructured data, such as images and text. After all, images and text are indispensable to many real-world decision situations but are ignored in traditional choice models. In today's digital age, it is hard to imagine searching for a house (on a real estate website), a hotel room, or a tourism destination without access to images.


I also lead TUD’s CityAI Lab. At this AI lab, we aim to unravel how the urban environment and human behaviour dance a tango. In our work, we capitalise on advances in machine learning and on the wealth of data available now at a city level. For instance, we use AI to study the evolution of residential segregation, uncover people's perceptions of the urban space and develop smart noise sensors to detect sources of urban noise pollution. Ultimately, we hope to contribute to the development of more attractive and liveable cities.

In my Postdoc years (2013-2014), I made a series of contributions to random regret minimisation (RRM) based on 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, a new data collection methodology, and the world’s first RRM-based national transport model. See my website, www.advancedRRMmodels.com, for these developments in Random Regret Minimization (RRM) modelling, experimental design software for RRM models (Ngene & MATLAB), and estimation codes for RRM models for Biogeme, R (Apollo), MATLAB, and LatentGold Choice.

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