CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities
Summary
CommuniWave is a machine learning model introduced to quantify the Degree of Informal Behavior (DIB) in urban communities using behavior capture, YOLOv10, and random forest, enabling dynamic monitoring for urban managers.
View Cached Full Text
Cached at: 07/10/26, 06:09 AM
# CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities Source: [https://arxiv.org/abs/2607.08554](https://arxiv.org/abs/2607.08554) [View PDF](https://arxiv.org/pdf/2607.08554) > 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\. ## Submission history From: Hongye Yang \[[view email](https://arxiv.org/show-email/546bfeb8/2607.08554)\] **\[v1\]**Thu, 9 Jul 2026 14:45:38 UTC \(870 KB\)
Similar Articles
Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning
Introduces Discrete-WAM, a unified discrete latent vision-action world policy that enables compositional causal reasoning and counterfactual reasoning in autonomous driving through aligned discrete tokens and a shared discrete diffusion framework.
Learn to Quantify Social Interaction with Constraints for Pedestrian Walking
This paper introduces a method called 'Learn to Cluster' to quantify and interpret social interactions among pedestrians for better trajectory prediction. It uses probabilistic latent variable generative learning to cluster social interactions without labels, improving robustness for autonomous driving and social robots.
MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation
Introduces MobiDiff, an end-to-end discrete diffusion framework for generating human mobility data by denoising multi-channel semantic skeletons, achieving faster inference and competitive fidelity on real-world datasets.
Building Social World Models with Large Language Models
The paper introduces the Social World Model (SWM) framework, which uses large language models to model the dynamics of social beliefs in response to events, without explicit annotations. It also presents a benchmark SWM-bench derived from prediction markets and shows state-of-the-art results.
Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signal
Dywave is a dynamic tokenization framework for IoT sensing signals that uses wavelet-based hierarchical decomposition to align tokens with semantic events, achieving up to 12% higher accuracy and 75% reduction in input token length on five real-world datasets.