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This paper presents a novel approach to distinguish driving styles withrespect to their energy efficiency. A distinct property of our method is thatit relies exclusively on Global Positioning System (GPS) logs of drivers. Thissetting is highly relevant in practice as these data can easily be acquired.  Relying on positional data alone means that all derived features will becorrelated, so we strive to find a single quantity that allows us to performthe driving style analysis. To this end we consider a robust variation of theso called jerk of a movement. We show that our feature choice outperforms othermore commonly used jerk-based formulations and we discuss the handling ofnoisy, inconsistent, and incomplete data as this is a notorious problem whendealing with real-world GPS logs.  Our solving strategy relies on an agglomerative hierarchical clusteringcombined 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-worlddata sets. Experiments show that our approach is robust against noise and ableto discern different driving styles.

Highly Robust Clustering of GPS Driver Data for Energy Efficient Driving Style Modelling