RT Journal Article
T1 Review and classification of trajectory summarisation algorithms: From compression to segmentation
A1 Amigo Herrero, Daniel
A1 Sánchez Pedroche, David
A1 García Herrero, Jesús
A1 Molina López, José Manuel
AB With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.
PB Sage Journals
SN 1550-1329
YR 2021
FD 2021-10-30
LK https://hdl.handle.net/10016/37673
UL https://hdl.handle.net/10016/37673
LA eng
NO The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), reference TEC2017-88048-C2-2-R
DS e-Archivo
RD 17 jul. 2024