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DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis
TLDR
An unsupervised neural approach for mobility behavior clustering, called the Deep Embedded TrajEctory ClusTering network (DETECT), which learns a powerful representation of trajectories in the latent space of behaviors, thus enabling a clustering function (such as k-means) to be applied.Abstract:
Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence. Existing work on trajectory clustering often relies on similarity measurements that utilize raw spatial and/or temporal information of trajectories. These measures are incapable of identifying similar moving behaviors that exhibit varying spatio-temporal scales of movement. In addition, the expense of labeling massive trajectory data is a barrier to supervised learning models. To address these challenges, we propose an unsupervised neural approach for mobility behavior clustering, called the Deep Embedded TrajEctory ClusTering network (DETECT). DETECT operates in three parts: first it transforms the trajectories by summarizing their critical parts and augmenting them with context derived from their geographical locality (e.g., using POIs from gazetteers). In the second part, it learns a powerful representation of trajectories in the latent space of behaviors, thus enabling a clustering function (such as $k$-means) to be applied. Finally, a clustering oriented loss is directly built on the embedded features to jointly perform feature refinement and cluster assignment, thus improving separability between mobility behaviors. Exhaustive quantitative and qualitative experiments on two real-world datasets demonstrate the effectiveness of our approach for mobility behavior analyses.read more
Citations
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Video trajectory analysis using unsupervised clustering and multi-criteria ranking
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CGC: Contrastive Graph Clustering forCommunity Detection and Tracking
Namyong Park,Ryan A. Rossi,Eunyee Koh,Iftikhar Ahamath Burhanuddin,Sungchul Kim,Fan Du,Nesreen K. Ahmed,Christos Faloutsos +7 more
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STENet: A hybrid spatio-temporal embedding network for human trajectory forecasting
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SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation
TL;DR: This work proposes a novel spatial language model, SPABERT, which provides a general-purpose geo-entity representation based on neighboring entities in geospatial data, and extends BERT to capture linearized spatial context, while incorporating a spatial coordinate embedding mechanism to preserve spatial relations of entities in the 2-dimensional space.
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Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics
TL;DR: Wang et al. as discussed by the authors proposed a self-supervised trajectory representation learning framework with temporal regularities and travel semantics, which can be applied to various downstream tasks, such as trajectory classification, clustering, and similarity computation.
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