Region representation learning via mobility flow

Hongjian Wang, Zhenhui Li

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

24 Scopus citations

Abstract

Increasing amount of urban data are being accumulated and released to public; this enables us to study the urban dynamics and address urban issues such as crime, traffic, and quality of living. In this paper, we are interested in learning vector representations for regions using the large-scale taxi flow data. These representations could help us better measure the relationship strengths between regions, and the relationships can be used to better model the region properties. Different from existing studies, we propose to consider both temporal dynamics and multi-hop transitions in learning the region representations. We propose to jointly learn the representations from a flow graph and a spatial graph. Such a combined graph could simulate individual movements and also addresses the data sparsity issue.We demonstrate the effectiveness of our method using three different real datasets.

Original languageEnglish (US)
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages237-246
Number of pages10
ISBN (Electronic)9781450349185
DOIs
StatePublished - Nov 6 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: Nov 6 2017Nov 10 2017

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
VolumePart F131841

Other

Other26th ACM International Conference on Information and Knowledge Management, CIKM 2017
CountrySingapore
CitySingapore
Period11/6/1711/10/17

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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  • Cite this

    Wang, H., & Li, Z. (2017). Region representation learning via mobility flow. In CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management (pp. 237-246). (International Conference on Information and Knowledge Management, Proceedings; Vol. Part F131841). Association for Computing Machinery. https://doi.org/10.1145/3132847.3133006