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 perspec tive, looking for a better understanding of the problem and for an automatic, user-adaptive
and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the
specific user under study and to the geographical areas they traverse. Experiments over
real data, and comparison against simple and state-of-the-art competitors show that the
flexibility of the proposed methods has a positive impact on results
