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
A Comparative Study of Urban Mobility Patterns Using Large-Scale Spatio-Temporal Data
Jodi Chiam,Ying Li +1 more
TL;DR: An anonymized spatio-temporal data from telco networks is studied to understand the variability in human mobility behavior across different geographical regions, and a mobility index is defined to assess the mobility level of individuals and compare it among different regions and demographic groups.
Journal ArticleDOI
An intelligent linear time trajectory data compression framework for smart planning of sustainable metropolitan cities
TL;DR: A compressed lookup lexicon is created to store the PoIs of dynamically selected region of interests (ROI) based on intelligent mining paradigm, which compresses trajectories in linear time, making it feasible for mission critical real world applications.
Posted Content
Decentralized Trajectory Tracking Using Homology and Hodge Decomposition in Sensor Networks.
TL;DR: The topological representation, which records how a target moves around the natural obstacles in the underlying environment, is proposed, which can be sufficiently descriptive for many applications and efficient enough for storing, comparing and classifying these natural human trajectories.
Proceedings ArticleDOI
Trajectory-User Linking via Graph Neural Network
TL;DR: Wang et al. as discussed by the authors proposed a novel end-to-end model called GNNTUL, composed of a graph neural network (GNN) module and a classifier, to effectively and efficiently learn human mobility and associate the traces to the users in online social networks.
Proceedings ArticleDOI
The Context Matters: Predicting the Number of In-game Actions Using Traces of Mobile Augmented Reality Games
TL;DR: This paper analyzes a comprehensive dataset that contains players' actions of one of the most popular mobile AR games, Ingress, and shows a highly significant relationship between context factors and in-game actions, which explains large parts of users' behavior in terms of when, where and how often they play the game.
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