BaG: Behavior-aware Group Detection in Crowded Urban Spaces using WiFi Probes

Jiaxing SHEN , Jiannong CAO, Xuefeng LIU

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

11 Citations (Scopus)


Group detection is gaining popularity as it enables various applications ranging from marketing to urban planning. The group information is an important social context which could facilitate a more comprehensive behavior analysis. An example is for retailers to determine the right incentive for potential customers. Existing methods use received signal strength indicator (RSSI) to detect co-located people as groups. However, this approach might have difficulties in crowded urban spaces since many strangers with similar mobility patterns could be identified as groups. Moreover, RSSI is vulnerable to many factors like the human body attenuation and thus is unreliable in crowded scenarios. In this work, we propose a behavior-aware group detection system (BaG). BaG fuses people's mobility information and smartphone usage behaviors. We observe that people in a group tend to have similar phone usage patterns. Those patterns could be effectively captured by the proposed feature: number of bursts (NoB). Unlike RSSI, NoB is more resilient to environmental changes as it only cares about receiving packets or not. Besides, both mobility and usage patterns correspond to the same underlying grouping information. The latent associations between them cannot be fully utilized in conventional detection methods like graph clustering. We propose a detection method based on collective matrix factorization to reveal the hidden associations by factorizing mobility information and usage patterns simultaneously. Experimental results indicate BaG outperforms baseline approaches by 3.97% ∼ 15.79% in F-score. The proposed system could also achieve robust and reliable performance in scenarios with different levels of crowdedness.

Original languageEnglish
Title of host publicationProceedings of the 2019 World Wide Web Conference (WWW ’19)
EditorsLing LIU, Ryen WHITE
PublisherAssociation for Computing Machinery, Inc
Number of pages10
ISBN (Electronic)9781450366748
Publication statusPublished - May 2019
Externally publishedYes
Event2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
Duration: 13 May 201917 May 2019

Publication series

NameWWW International World Wide Web Conference


Conference2019 World Wide Web Conference, WWW 2019
Country/TerritoryUnited States
CitySan Francisco

Bibliographical note

We sincerely appreciate the efforts of the anonymous reviewers and their insightful comments for improving this manuscript. The presented work was supported by National Key R&D Program of China (2018 YFB1004801). It was also partially supported by Shen-zhen Basic Research Funding Scheme (JCYJ20170818104222072), and NSFC with project No. 61332004 and 61572218.


  • Collective matrix factorization
  • Group detection
  • Probe request
  • WiFi


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