Journal ArticleDOI
Trajectory Data Mining: An Overview
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TLDR
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, and introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors.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.read more
Citations
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Journal ArticleDOI
Trajectory-as-a-Sequence: A novel travel mode identification framework
TL;DR: In this article , a sequence-to-sequence (seq2seq) model was proposed to construct a feature sequence for each GPS trajectory and sent it to a sequence2seq model to obtain the corresponding travel mode label sequence, named Trajectory-as-a-sequence.
DissertationDOI
From spatio-temporal trajectories to succinct and semantically meaningful patterns
TL;DR: Within the trajectory data mining and knowledge discovery process I have identified four challenges that hinder the discovery of succinct and semantically meaningful trajectory patterns: spatial uncertainty, trajectory complexity, pattern complexity, and semantic meaning.
Proceedings ArticleDOI
A Real-Time User Mobility Pattern Modeling and Similarity Measurement for Mobile Social Networks
TL;DR: This paper develops a complete data-driven framework involving real-time user mobility pattern modeling and a novel user similarity measurement based on both spatial and temporal information and verified that the proposed Mobility pattern modeling method and similarity measurement can deliver excellent performance.
Proceedings ArticleDOI
CORE: Connectivity Optimization via REinforcement Learning in WANETs
TL;DR: In this article, an adaptive distributed solution for device-to-device Connectivity Optimization via reinforcement learning (CORE) in wireless ad hoc networks is designed for collaborative distributed agents with intermittent connectivity and limited battery power, but predictable mobility within short temporal horizons.
Journal ArticleDOI
Reliable Collaborative Filtering on Spatio-Temporal Privacy Data
TL;DR: A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper and experiments results indicate that compared with the existing algorithms, the proposed L GP-CF algorithm can make recommendations more accurately.
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