Smart City Gnosys
Smart city article details
| Title | Dynamic Crowd Modeling And Anomalous Behavior Prediction Using Gmm And Time Series Analysis In Real-Time Smart City Environment |
|---|---|
| ID_Doc | 21252 |
| Authors | Kanchana R.; Fernandez F.M.H. |
| Year | 2025 |
| Published | 4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025 - Proceedings |
| DOI | http://dx.doi.org/10.1109/ICSADL65848.2025.10933196 |
| Abstract | The Research Concentrates on simulating and investigating dynamic crowd using Gaussian Mixture Models (GMM) and synthetic time-series data in real time smart city environments. A novel approach generates and designs various crowd behaviors, incorporating normal flow, high congestions, and erratic patterns, leveling to detect and predict anomalies that pose safety issues. Mock datasets, imitating real-world situations, are generated using spatial-temporal features such as crowd density, velocity, and movement direction. To cluster these behaviors into distinct operational states, the GMM is employed, while irregularities are recognized as outliers to the learned distributions. The simulation framework introduces temporal tendencies and real-time capabilities, aiding dynamic variance detection and proactive crowd organization. Investigational results prove the efficiency of the proposed approach in clustering behavioral states and detecting anomalies, providing a scalable and computationally efficient result for inner-city protection and resource optimization. The study implements a foundation for deploying real-time crowd monitoring systems in smart cities, addressing challenges in occasion management, transportation, and disaster awareness. © 2025 IEEE. |
| Author Keywords | Crowd Density; Gaussian Mixture Model (GMM); Learned Distributions; Proactive Crowd Organization; Resource Optimization; Spatial-Temporal Features; Synthetic Time-Series data; Temporal Tendencies |
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