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
Where Urban Youth Work and Live: A Data-Driven Approach to Identify Urban Functional Areas at a Fine Scale
TL;DR: Food delivery data is introduced as a new data source in urban functional zone detection and a time-series-based clustering approach is proposed to discover the urban hotspot areas of young people to help urban industrial and residential planning and young population management.
Journal ArticleDOI
Constrained trajectory simplification with speed preservation
TL;DR: This study designed a data model, proposed error measurements, and developed a two-component method to simplify constrained trajectories with a guaranteed position error bound in network space, which provides an approach to simplifying trajectory data that guarantees error bounds in both location and speed.
Proceedings ArticleDOI
Mining friendships through spatial-temporal features in mobile social networks
Jianwei Niu,Danning Wang,Jie Lu +2 more
TL;DR: This paper introduces a set of spatial-temporal features, including the encounter entropy, which measures the probability of encounters between different mobile users and provides a novel model to infer user friendship by analyzing the social context of users and their encounters.
Posted ContentDOI
The identification of temporal communities through trajectory clustering correlates with single-trial behavioural fluctuations in neuroimaging data
William Hedley Thompson,William Hedley Thompson,Jessey Wright,James M. Shine,Russell A. Poldrack +4 more
TL;DR: A new method, Temporal Communities through Trajectory Clustering (TCTC), is presented that derives time-varying communities directly from time-series data collected from the nodes in a network and identifies robust communities revealing ongoing spatiotemporal community configurations during task performance.
Journal ArticleDOI
Origin-Aware Location Prediction Based on Historical Vehicle Trajectories
TL;DR: In this article, a common approach to next location prediction is proposed, which provides essential intelligence to various businesses by predicting the next location of a user in a location-based application.
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