Abstract

Modeling human mobility is a critical task in fields such as urban planning, ecology, and epidemiology. Given the current use of mobile phones, there is an abundance of data that can be used to create models of high reliability. Existing techniques can reveal the macropatterns of crowd movement or analyze the trajectory of a person; however, they typically focus on geographical characteristics. This paper presents a graph-based approach for structuring crowd mobility transition over multiple granularities in the context of social behavior. The key to our approach is an adaptive data representation, the adaptive mobility transition graph (AMTG), which is globally generated from citywide human mobility data by defining the temporal trends of human mobility and the interleaved transitions between different mobility patterns. We describe the design, creation, and manipulation of the AMTG and introduce a visual analysis system that supports the multifaceted exploration of citywide human mobility patterns.

Original languageEnglish (US)
JournalIEEE Transactions on Computational Social Systems
DOIs
StateAccepted/In press - Aug 15 2018

Fingerprint

Epidemiology
Urban planning
Graph Representation
Ecology
Mobile phones
Trajectories
Graph in graph theory
Urban Planning
Social Behavior
systems analysis
Mobile Phone
social behavior
epidemiology
Systems Analysis
Granularity
urban planning
manipulation
ecology
Manipulation
Person

Keywords

  • Mobility
  • mobility patterns
  • mobility transition
  • timeline

ASJC Scopus subject areas

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction

Cite this

Structuring Mobility Transition With an Adaptive Graph Representation. / Gu, Tianlong; Zhu, Minfeng; Chen, Wei; Huang, Zhaosong; Maciejewski, Ross; Chang, Liang.

In: IEEE Transactions on Computational Social Systems, 15.08.2018.

Research output: Contribution to journalArticle

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