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

Abstract

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 in F-score. The proposed system could also achieve robust and reliable performance in scenarios with different levels of crowdedness.

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