CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Hongye Yang, Shien Liu, Zhihao Xie
• Venue: arXiv: cs.AI
• Published: 2026-07-09
• Collected: 2026-07-11
• URL: http://arxiv.org/abs/2607.08554v1
• PDF: https://arxiv.org/pdf/2607.08554v1
• DOI: UNKNOWN

Plain-Language Brief

Researchers have developed a machine learning model named CommuniWave. This model aims to help urban managers and designers by efficiently detecting and quantifying temporary informal behaviors in urban communities, thus providing them with dynamic monitoring and aiding in making informed decisions to enhance community resilience.

What the Document Says

• The study presents a machine learning model named CommuniWave.
• CommuniWave is designed for urban managers and designers.
• The primary goal of CommuniWave is to quantify the Degree of Informal Behavior (DIB) in urban communities.
• CommuniWave utilizes a Behavior Capture Net (BCN) based on mmaction2, a self-developed YOLOv10 model (YLX), and a Behavior Eval Model (BEM) using random forest.
• The generated DIB fluctuation charts from street videos facilitate dynamic monitoring for urban managers.

Key Concepts Mentioned

• CommuniWave
• Degree of Informal Behavior (DIB)
• urban communities
• machine learning model
• urban managers
• dynamic monitoring

Claims Made by the Document

• CommuniWave can help urban managers and designers.
• CommuniWave can efficiently detect and quantify DIB.

Evidence Used

• Integration of Behavior Capture Net, self-developed YOLOv10 model, and Behavior Eval Model using random forest.
• Generation of DIB fluctuation charts from street videos

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

For urban managers and designers, improving the functional attributes of urban communities to enhance territorial resilience in the face of complexity and uncertainty is crucial. Currently, community planning often follows a top-down approach and lacks effective metrics to quantify informal behaviors of residents, leading to frequent conflicts with original plans. This study introduces CommuniWave, a machine learning model designed to efficiently detect and quantify the Degree of Informal Behavior (DIB) in urban communities. The model integrates a Behavior Capture Net (BCN) based on mmaction2, a self-developed YOLOv10 model (YLX), and a Behavior Eval Model (BEM) using random forest. Ultimately, by generating DIB fluctuation charts from street videos, the model facilitates dynamic monitoring, supporting urban managers in making refined decisions to enhance the overall resilience of communities.