Scalable Unsupervised Multi-Criteria Trajectory Segmentation and Driving Preference Mining
This work addresses the challenge of analyzing massive trajectory data for applications like personalized routing, though it appears incremental in applying existing mining concepts to driving data.
The paper tackles the problem of extracting semantic understanding from large vehicle trajectory datasets by developing techniques for trajectory segmentation and driving preference mining, achieving evaluation on via-point identification and personalized routing using over 1 million trajectories from Denmark.
We present analysis techniques for large trajectory data sets that aim to provide a semantic understanding of trajectories reaching beyond them being point sequences in time and space. The presented techniques use a driving preference model w.r.t. road segment traversal costs, e.g., travel time and distance, to analyze and explain trajectories. In particular, we present trajectory mining techniques that can (a) find interesting points within a trajectory indicating, e.g., a via-point, and (b) recover the driving preferences of a driver based on their chosen trajectory. We evaluate our techniques on the tasks of via-point identification and personalized routing using a data set of more than 1 million vehicle trajectories collected throughout Denmark during a 3-year period. Our techniques can be implemented efficiently and are highly parallelizable, allowing them to scale to millions or billions of trajectories.