@conference {254,
	title = {Self-Adapting Trajectory Segmentation},
	booktitle = {3rd International Workshop on Big Mobility Data Analytics (BMDA)  2020},
	year = {2020},
	abstract = {Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, parameter-free and user-adaptive solution that flexibly adjusts the segmentation criteria to the specific user under study. Experiments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results.},
	keywords = {Mobility Data Mining, Segmentation, User Modeling},
	author = {Agnese Bonavita and Guidotti, Riccardo and Nanni,Mirco}
}
