  The rising availability of digital traces provides a fertile ground for new
solutions to both, new and old problems in cities. Even though a massive data
set analyzed with Data Science methods may provide a powerful solution to a
problem, its adoption by relevant stakeholders is not guaranteed, due to
adoption blockers such as lack of interpretability and transparency. In this
context, this paper proposes a preliminary methodology toward bridging two
disciplines, Data Science and Transportation, to solve urban problems with
methods that are suitable for adoption. The methodology is defined by four
steps where people from both disciplines go from algorithm and model definition
to the building of a potentially adoptable solution. As case study, we describe
how this methodology was applied to define a model to infer commuting trips
with mode of transportation from mobile phone data.
