Microscopic Crowd Movement Modeling, Simulation, and Intervention Decision-making Framework Based on Physics-informed Machine Learning

Crowd movement is a critical factor influencing urban public safety and emergency management. How to achieve high-precision modeling, simulation and effective intervention is an urgent issue to be solved. To address these challenges, a physics-informed machine learning-driven framework for microscopic crowd movement modeling, simulation, and intervention decision-making is proposed. Based on the concept of parallel intelligence, the framework establishes a four-layer closed-loop architecture comprising data perception, fusion modeling, dynamic simulation, and intelligent intervention. This architecture forms a complete chain from modeling and simulation to strategy generation, execution, and feedback refinement. For crowd movement simulation and guidance decision-making problems, two novel methodologies are introduced in the framework: A physics-informed spatiotemporal graph convolutional network-based navigation potential field model and a physics-informed multi-agent deep deterministic policy gradient algorithm. These methods effectively resolve issues prevalent in conventional methodologies, namely, the insufficient model accuracy, disjointedness between simulation and intervention, and reliance on human experience for decision-making. Finally, simulation experiments conducted on real-world datasets confirm the effectiveness of the framework.