Review and classification of trajectory summarisation algorithms: From compression to segmentation

Daniel Amigo, David Sánchez Pedroche, Jesús García and José Manuel Molina

International Journal of Distributed Sensor Networks, 2021, vol. 17, issue 10, 15501477211050729

Abstract: 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.

Keywords: Trajectory summarisation; trajectory segmentation; trajectory compression; data compression; Douglas–Peucker; spatial data analysis; trajectory partitioning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:17:y:2021:i:10:p:15501477211050729

DOI: 10.1177/15501477211050729

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