  Telematics data is becoming increasingly available due to the ubiquity of
devices that collect data during drives, for different purposes, such as usage
based insurance (UBI), fleet management, navigation of connected vehicles, etc.
Consequently, a variety of data-analytic applications have become feasible that
extract valuable insights from the data. In this paper, we address the
especially challenging problem of discovering behavior-based driving patterns
from only externally observable phenomena (e.g. vehicle's speed). We present a
trajectory segmentation approach capable of discovering driving patterns as
separate segments, based on the behavior of drivers. This segmentation approach
includes a novel transformation of trajectories along with a dynamic
programming approach for segmentation. We apply the segmentation approach on a
real-word, rich dataset of personal car trajectories provided by a major
insurance company based in Columbus, Ohio. Analysis and preliminary results
show the applicability of approach for finding significant driving patterns.
