Dynamic Crowd Management Through Potential Field-Based Simulations

This study introduces an innovative approach to crowd management through advanced computational modeling and visualization techniques. By leveraging the capabilities of Python and Matplotlib, we developed a comprehensive framework that segments a defined area into a grid, simulating crowd density through variable point distributions. This method incorporates realistic scenarios by integrating weighted regions to represent areas of attraction and avoidance, facilitating a detailed and nuanced analysis of crowd dynamics. Our approach systematically categorizes spatial zones based on density levels and evaluates the impact of attractors and repellers, providing valuable insights into potential congestion points and optimal crowd flow patterns. Visualization is achieved by overlaying quiver plots on heat maps, effectively highlighting spatial distributions and movement trends within the crowd. The proposed model has significant implications for urban planning, event management, and public safety, offering a robust tool for data-driven decision-making in crowd management. Preliminary assessments indicate its potential in predicting and mitigating crowd-related challenges, laying a solid foundation for future research and practical applications in the field.