A visual analytics design for studying crowd movement rhythms from public transportation data

Wei Zeng, Chi Wing Fu, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, Kwan-Liu Ma

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

1 Scopus citations

Abstract

Human lives involve various daily movements in a space-time context, which exhibit high regularity that typically forms circadian rhythms. Understanding the rhythms for human daily movements of massive crowds can be highly beneficial for a variety of applications, such as traffic demand management and urban planning. In this paper, we propose an interactive visual data analysis approach, which provides not only quantitative analyses, including frequent human movement rhythms identification, but also visualization supported with a family of user interactions. We also devise a set of interactive visual query methods for users to easily explore the movement rhythms over space and time. Case studies with real-world massive urban public transportation data in Singapore, and interviews with transportation researches are carried out to demonstrate the effectiveness and usefulness of our system.

Original languageEnglish (US)
Title of host publicationSA 2016 - SIGGRAPH ASIA 2016 Symposium on Visualization
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450345477
DOIs
StatePublished - Nov 28 2016
Event2016 SIGGRAPH ASIA Symposium on Visualization, SA 2016 - Macau, China
Duration: Dec 5 2016Dec 8 2016

Other

Other2016 SIGGRAPH ASIA Symposium on Visualization, SA 2016
CountryChina
CityMacau
Period12/5/1612/8/16

Keywords

  • Event sequence
  • Movement rhythm
  • Visual analytics

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Zeng, W., Fu, C. W., Arisona, S. M., Schubiger, S., Burkhard, R., & Ma, K-L. (2016). A visual analytics design for studying crowd movement rhythms from public transportation data. In SA 2016 - SIGGRAPH ASIA 2016 Symposium on Visualization [a4] Association for Computing Machinery, Inc. https://doi.org/10.1145/3002151.3002152