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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Proceedings ArticleDOI
Privacy-preserving trajectory classification of driving trip data based on pattern discovery techniques
Gene P. K. Wu,Keith C. C. Chan +1 more
TL;DR: The result indicates the information theoretic approach to classify the privacy-preserving driving trips in a set of recorded GPS tracks is effective and efficient in achieving a good accuracy in the prediction of the class labels of the different driving trips based on the transformed set of attributes.
Journal ArticleDOI
Learning the micro-environment from rich trajectories in the context of mobile crowd sensing
Hafsa El Hafyani,Mohammad Aly Mohammad Abboud,Jingwei Zuo,Karine Zeitouni,Yehia Taher,Basile Chaix,Limin Wang +6 more
TL;DR: In this article , a multi-view learning approach was proposed to detect the human's micro-environment in an environmental mobile crowd sensing scenario, and the proposed approach was extended to a hybrid method that employs trajectory segmentation to bring the best of both methods.
Journal ArticleDOI
Online detection of anomaly behaviors based on multidimensional trajectories
TL;DR: Experiments in both simulated military scenario and realistic civilian scenario show the presented algorithm has a good performance to online detect anomaly behaviors and would have a wide prospect in early warning surveillance systems.
Affinity-based human mobility pattern for improved region function discovering
TL;DR: The findings on the interaction between the mobility pattern and the regional functions can capture the city dynamics efficiently and provide a valuable reference for urban planners.
VTSV: A Privacy-Preserving Vehicle Trajectory Simulation and Visualization Platform Using Deep Reinforcement Learning
Jinmeng Rao,Song Gao,Xiaojin Zhu +2 more
TL;DR: Wang et al. as discussed by the authors presented a privacy-preserving vehicle trajectory simulation and visualization (VTSV) web platform which automatically generates navigation routes between given pairs of origins and destinations and employs a deep reinforcement learning model to simulate vehicle trajectories with customized driving behaviors such as normal driving, overspeed, aggressive acceleration, and aggressive turning.
References
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Journal ArticleDOI
Anomaly detection: A survey
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Journal ArticleDOI
Algorithms for the reduction of the number of points required to represent a digitized line or its caricature
TL;DR: In this paper, two algorithms to reduce the number of points required to represent the line and, if desired, produce caricatures are presented and compared with the most promising methods so far suggested.
Book ChapterDOI
Efficient Similarity Search In Sequence Databases
TL;DR: An indexing method for time sequences for processing similarity queries using R * -trees to index the sequences and efficiently answer similarity queries and provides experimental results which show that the method is superior to search based on sequential scanning.
Proceedings ArticleDOI
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
TL;DR: This work proposes a novel sequential pattern mining method, called Prefixspan (i.e., Prefix-projected - Ettern_ mining), which explores prejxprojection in sequential pattern Mining, and shows that Pre fixspan outperforms both the Apriori-based GSP algorithm and another recently proposed method; Frees pan, in mining large sequence data bases.
Proceedings ArticleDOI
Mining interesting locations and travel sequences from GPS trajectories
TL;DR: This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location.