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

• URL: http://arxiv.org/abs/2607.08554v1
• Date: Thu, 09 Jul 2026 14:45:38 GMT
• Title: CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities
• Abstract summary: This study introduces CommuniWave, a machine learning model designed to efficiently detect and quantify the Degree of Informal Behavior (DIB) in urban communities.<n>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.
• Score: 2.007262412327553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Scaling Behavior Foundation Model for Humanoid Robots [77.31115920959364]
  Behavior Foundation Models (BFMs) have emerged as a promising solution to address challenges by leveraging large-scale behavioral data.<n>We show that our approach yields significant improvements in control fidelity and task generalization.<n>These results establish BFM as a principled and effective foundation for scalable and general-purpose humanoid control.
  arXiv  Detail & Related papers  (2026-07-16T16:08:27Z)
• Group Effect Enhanced Generative Adversarial Imitation Learning for Individual Travel Behavior Modeling under Incentives [8.12279767264402]
  We propose a group-effect-enhanced generative adversarial imitation learning (gcGAIL) model that improves the individual behavior modeling efficiency.<n>We validate the gcGAIL model using a public transport fare-discount case study and compare against state-of-the-art benchmarks.<n>Results demonstrate that gcGAIL outperforms these methods in learning individual travel behavior responses to incentives over time.
  arXiv  Detail & Related papers  (2025-09-08T13:14:28Z)
• DynamiX: Large-Scale Dynamic Social Network Simulator [101.65679342680542]
  DynamiX is a novel large-scale social network simulator dedicated to dynamic social network modeling.<n>For opinion leaders, we propose an information-stream-based link prediction method recommending potential users with similar stances.<n>For ordinary users, we construct an inequality-oriented behavior decision-making module.
  arXiv  Detail & Related papers  (2025-07-26T12:13:30Z)
• CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation [1.2430809884830318]
  We envision an urban simulator (CitySim), capitalizing on breakthroughs in human-level intelligence exhibited by large language models.<n>In CitySim, agents generate realistic daily schedules using a value-driven approach that balances mandatory activities, personal habits, and situational factors.<n>CitySim exhibits closer alignment with real humans than prior work, both at micro and macro levels.
  arXiv  Detail & Related papers  (2025-06-26T23:11:42Z)
• Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation [0.04517077427559345]
  This study presents an innovative approach to urban mobility simulation by integrating a Large Language Model (LLM) with Agent-Based Modeling (ABM)<n>Unlike traditional rule-based ABM, the proposed framework leverages LLM to enhance agent diversity and realism.<n>Using real-world data, the simulation models individual behaviors and large-scale mobility patterns in Taipei City.
  arXiv  Detail & Related papers  (2025-05-28T01:54:28Z)
• MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework [53.82097200295448]
  Mean-Field LLM (MF-LLM) is first to incorporate mean field theory into social simulation.<n>MF-LLM models bidirectional interactions between individuals and the population through an iterative process.<n> IB-Tune is a novel fine-tuning method inspired by the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-04-30T12:41:51Z)
• Collaborative Imputation of Urban Time Series through Cross-city Meta-learning [54.438991949772145]
  We propose a novel collaborative imputation paradigm leveraging meta-learned implicit neural representations (INRs)<n>We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning.<n>Experiments on a diverse urban dataset from 20 global cities demonstrate our model's superior imputation performance and generalizability.
  arXiv  Detail & Related papers  (2025-01-20T07:12:40Z)
• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• Mixing Individual and Collective Behaviours to Predict Out-of-Routine Mobility [4.442030973972382]
  This study introduces an approach that dynamically integrates individual and collective mobility behaviours. We demonstrate its superior performance in predicting out-of-routine mobility, surpassing even advanced deep learning methods. By bridging the gap between individual and collective behaviours, our approach offers transparent and accurate predictions.
  arXiv  Detail & Related papers  (2024-04-03T13:38:49Z)
• Safety-compliant Generative Adversarial Networks for Human Trajectory Forecasting [95.82600221180415]
  Human forecasting in crowds presents the challenges of modelling social interactions and outputting collision-free multimodal distribution. We introduce SGANv2, an improved safety-compliant SGAN architecture equipped with motion-temporal interaction modelling and a transformer-based discriminator design.
  arXiv  Detail & Related papers  (2022-09-25T15:18:56Z)
• Dynamic Urban Planning: an Agent-Based Model Coupling Mobility Mode and Housing Choice. Use case Kendall Square [5.620109882646823]
  This paper presents an agent-based model that characterizes citizens' behavioural patterns with respect to housing and mobility choice. The realistic identification and representation of the criteria that affect this decision-making process will help understand and evaluate the impacts of potential housing incentives.
  arXiv  Detail & Related papers  (2021-06-28T10:54:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.