CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities
2026-07-09 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors created a system called CommuniWave to help city planners understand how people behave informally in neighborhoods, which often conflicts with official plans. Their method uses machine learning tools to watch videos of streets and measure these informal behaviors, giving a score called the Degree of Informal Behavior (DIB). This helps managers keep track of changes over time and make better decisions to improve how communities handle challenges. Overall, the study focuses on using AI to better capture real human activity in urban areas for planning purposes.
urban resiliencecommunity planningmachine learninginformal behaviormmaction2YOLOv10random forestbehavior detectiondynamic monitoringurban management
Authors
Hongye Yang, Shien Liu, Zhihao Xie
Abstract
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.