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
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Tracking random walks
TL;DR: This work considers the important case of movements that consist of alternating rests and moves of random durations and study how the estimate of their statistical properties is affected by the way the authors measure them, and provides an exact analytical calculation of the fraction of correctly sampled trajectories.
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An improved adaptive equivalent consumption minimization strategy for parallel plug-in hybrid electric vehicle:
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Integrating tourist packages and tourist attractions for personalized trip planning based on travel constraints
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GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent Encoder-Decoder Architecture with Attention Mechanism.
TL;DR: This work proposed deep learning based bidirectional convolutional recurrent encoder-decoder architecture to generate the missing points of GPS trajectories over occupancy grid-map that achieved better results in terms of average displacement error as compared to the state-of-the-art benchmark methods.
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A Deep Learning Approach for Identifying User Communities Based on Geographical Preferences and Its Applications to Urban and Environmental Planning
TL;DR: In this article, the authors proposed a novel approach that leverages image processing techniques to represent user geographical preferences as images and then apply deep convolutional autoencoders to extract latent spatio-temporal mobility features from these images.
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Proceedings ArticleDOI
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