  This paper presents a novel approach to distinguish driving styles with
respect to their energy efficiency. A distinct property of our method is that
it relies exclusively on Global Positioning System (GPS) logs of drivers. This
setting is highly relevant in practice as these data can easily be acquired.
  Relying on positional data alone means that all derived features will be
correlated, so we strive to find a single quantity that allows us to perform
the driving style analysis. To this end we consider a robust variation of the
so called jerk of a movement. We show that our feature choice outperforms other
more commonly used jerk-based formulations and we discuss the handling of
noisy, inconsistent, and incomplete data as this is a notorious problem when
dealing with real-world GPS logs.
  Our solving strategy relies on an agglomerative hierarchical clustering
combined with an L-term heuristic to determine the relevant number of clusters.
It can easily be implemented and performs fast, even on very large, real-world
data sets. Experiments show that our approach is robust against noise and able
to discern different driving styles.
