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

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.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-

STMaker-A system to make sense of trajectory data

Widely adoption of GPS-enabled devices generates large amounts of trajectories every day. The raw trajectory data describes the movement history of moving objects by a sequence of 〈 longitude, latitude, time-stamp 〉 triples, which are nonintuitive for human to perceive the prominent features of the trajectory, such as where and how the moving object travels. In this demo, we present the STMaker system to help users make sense of individual trajectories. Given a trajectory, STMaker can automatically extract the significant semantic behavior of the trajectory, and summarize the behavior by a short human-readable text. In this paper, we first introduce the phrases of generating trajectory summarizations, and then show several real trajectory summarization cases