  With the exponential advancement of business technology in recent years,
data-driven decision making has become the core of most industries. With the
rise of new privacy regulations such as the General Data Protection Regulation
in the European Union and the California Consumer Privacy Act in the United
States, companies dealing with personal data had to conform to these changes
and adapt their processes accordingly. This obviously included the
transportation industry with their use of location data. At the other side of
the spectrum, users still expect a form of personalization, without having to
compromise on their privacy. For this reason, companies across the industries
started applying privacy-enhancing or preserving technologies at scale in their
products as a competitive advantage. In this paper, we describe how Federated
Machine Learning can be applied to the transportation sector. We present
use-cases for which Federated Learning is beneficial in transportation and the
new product lifecycle that is required for using such a technology. We see
Federated Learning as a method that enables us to process privacy-sensitive
data, while respecting customer's privacy and one that guides us beyond
privacy-regulations and into the world of ethical data-usage.
