VAMBC: A Variational Approach for Mobility Behavior Clustering

Mingxuan Yue, Yao Yi Chiang, Cyrus Shahabi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Many domains including policymaking, urban design, and geospatial intelligence benefit from understanding people’s mobility behaviors (e.g., work commute, shopping), which can be achieved by clustering massive trajectories using the geo-context around the visiting locations (e.g., sequence of vectors, each describing the geographic environment near a visited location). However, existing clustering approaches on sequential data are not effective for clustering these context sequences based on the contexts’ transition patterns. They either rely on traditional pre-defined similarities for specific application requirements or utilize a two-phase autoencoder-based deep learning process, which is not robust to training variations. Thus, we propose a variational approach named VAMBC for clustering context sequences that simultaneously learns the self-supervision and cluster assignments in a single phase to infer moving behaviors from context transitions in trajectories. Our experiments show that VAMBC significantly outperforms the state-of-the-art approaches in robustness and accuracy of clustering mobility behaviors in trajectories.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationApplied Data Science Track - European Conference, ECML PKDD 2021, Proceedings
EditorsYuxiao Dong, Nicolas Kourtellis, Barbara Hammer, Jose A. Lozano
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783030865139
StatePublished - 2021
Externally publishedYes
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021 - Virtual, Online
Duration: Sep 13 2021Sep 17 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12978 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2021
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


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