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 practi tioners 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 parame ters 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. Experi ments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results.

Self-Adapting Trajectory Segmentation

Bonavita, Agnese
;
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 practi tioners 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 parame ters 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. Experi ments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results.
2020
Settore INF/01 - Informatica
3rd International Workshop on Big Mobility Data Analytics (BMDA) 2020
Copenaghen
Marzo 2020
Big Mobility Data Analytics
Mobility Data Mining, Segmentation, User Modeling
   Big Data for Mobility Tracking Knowledge Extraction in Urban Areas
   Track and Know
   European Commission
   Horizon 2020 Framework Programme
   780754
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/135022
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