  Driving risk prediction has been a topic of much research over the past few
decades to minimize driving risk and increase safety. The use of demographic
information in risk prediction is a traditional solution with applications in
insurance planning, however, it is difficult to capture true driving behavior
via such coarse-grained factors. Therefor, the use of telematics data has
gained a widespread popularity over the past decade. While most of the existing
studies leverage demographic information in addition to telematics data, our
objective is to maximize the use of telematics as well as contextual
information (e.g., road-type) to build a risk prediction framework with
real-world applications. We contextualize telematics data in a variety of
forms, and then use it to develop a risk classifier, assuming that there are
some weak risk labels available (e.g., past traffic citation records). Before
building a risk classifier though, we employ a novel data-driven process to
augment weak risk labels. Extensive analysis and results based on real-world
data from multiple major cities in the United States demonstrate usefulness of
the proposed framework.
