Structuring Mobility Transition with an Adaptive Graph Representation

Gu, T; Zhu, M; Chen, W; Huang, Z; MacIejewski, R; Chang, L

Structuring Mobility Transition with an Adaptive Graph Representation

Gu, T Zhu, M

Chen, W Huang, Z MacIejewski, R Chang, L

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Computational Social Systems, 2018, 5 (4), pp. 1121 - 1132
Issue Date:: 2018-12-01

Closed Access

	Filename	Description	Size
	08438320.pdf	Published Version	2.44 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Gu, T	en_US
dc.contributor.author	Zhu, M https://orcid.org/0000-0002-6711-3099	en_US
dc.contributor.author	Chen, W	en_US
dc.contributor.author	Huang, Z	en_US
dc.contributor.author	MacIejewski, R	en_US
dc.contributor.author	Chang, L	en_US
dc.date.issued	2018-12-01	en_US
dc.identifier.citation	IEEE Transactions on Computational Social Systems, 2018, 5 (4), pp. 1121 - 1132	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132505
dc.description.abstract	© 2014 IEEE. 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.	en_US
dc.relation.ispartof	IEEE Transactions on Computational Social Systems	en_US
dc.relation.isbasedon	10.1109/TCSS.2018.2858439	en_US
dc.title	Structuring Mobility Transition with an Adaptive Graph Representation	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	5	en_US
utslib.for	0804 Data Format	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	5	en_US

Abstract:

© 2014 IEEE. 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/132505