Trajectory Data Mining

Yu Zheng

doi:10.1145/2743025

journal article May 12, 2015

Trajectory Data Mining

ACM Transactions on Intelligent Systems and Technology Vol. 6 No. 3 pp. 1-41 · Association for Computing Machinery (ACM)

View at Publisher Save 10.1145/2743025

Abstract

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into
trajectory data mining
, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of
trajectory data mining
, providing a quick understanding of this field to the community.

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