  Since its inception, the choice modelling field has been dominated by
theory-driven models. The recent emergence and growing popularity of machine
learning models offer an alternative data-driven approach. Machine learning
models, techniques and practices could help overcome problems and limitations
of the current theory-driven modelling paradigm, e.g. relating to the
ad-hocness in search for the optimal model specification, and theory-driven
choice model's inability to work with text and image data. However, despite the
potential value of machine learning to improve choice modelling practices, the
choice modelling field has been somewhat hesitant to embrace machine learning.
The aim of this paper is to facilitate (further) integration of machine
learning in the choice modelling field. To achieve this objective, we make the
case that (further) integration of machine learning in the choice modelling
field is beneficial for the choice modelling field, and, we shed light on where
the benefits of further integration can be found. Specifically, we take the
following approach. First, we clarify the similarities and differences between
the two modelling paradigms. Second, we provide a literature overview on the
use of machine learning for choice modelling. Third, we reinforce the strengths
of the current theory-driven modelling paradigm and compare this with the
machine learning modelling paradigm, Fourth, we identify opportunities for
embracing machine learning for choice modelling, while recognising the
strengths of the current theory-driven paradigm. Finally, we put forward a
vision on the future relationship between the theory-driven choice models and
machine learning.
