CommuniWave：一种用于量化城市社区临时非正式行为程度的机器学习模型
CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities
AI总结：
研究针对城市社区规划缺乏量化居民非正式行为指标的问题，引入CommuniWave模型，它整合BCN、YLX和BEM，通过生成DIB波动图实现动态监测，助力城市管理者提升社区整体韧性。
摘要：
对于城市管理者和设计者而言，提升城市社区功能属性以增强面对复杂性和不确定性时的地域韧性至关重要。当前社区规划常采用自上而下方法，缺乏量化居民非正式行为的有效指标，导致与原计划频繁冲突。本研究引入CommuniWave，一种旨在有效检测和量化城市社区非正式行为程度（DIB）的机器学习模型。该模型整合了基于mmaction2的行为捕获网络（BCN）、自研的YOLOv10模型（YLX）以及使用随机森林的行为评估模型（BEM）。最终，通过从街道视频生成DIB波动图，该模型便于动态监测，支持城市管理者做出精准决策以增强社区整体韧性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
