Visualizing the Relationship Between Human Mobility and Points of Interest

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

Research output: Contribution to journalArticle

28 Scopus citations

Abstract

In transportation studies, one fundamental problem is to analyze the departures and arrivals at locations in order to predict the travel demands for urban planning and traffic management. These movements can relate to many factors, e.g., activity distributions and household demographics. This paper presents how we use visualization to explore the relationship between people movements and activity distributions that are characterized by the points of interest (POIs). To effectively model and visualize such relationship, we introduce POI-mobility signature, a compact visual representation with two main components. 1) A mobility component to present major people movements information across temporal dimension. 2) A POI component to present the activity context over an area of interest in spatial domain. To derive the signature, we study assorted analytical tasks after discussing with transportation researchers, consider essential design principles, and apply the representation to study a real-world dataset, which is the massive public transportation data in Singapore with over 30 million trajectories and crowd-sourcing POIs retrieved from Foursquare. Finally, we conduct three case studies and interview three transportation experts to verify the efficacy of our method.

Original languageEnglish (US)
Article number7817741
Pages (from-to)2271-2284
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Volume18
Issue number8
DOIs
StatePublished - Aug 1 2017

Keywords

  • data-driven intelligent transportation system
  • Human mobility
  • point-of-interest
  • visual analytics

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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