基于PIML的微观人群移动建模仿真与干预决策框架OA

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 frame-work establishes a four-layer closed-loop architecture comprising data perception,fusion modeling,dynamic simula-tion,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 de-terministic policy gradient algorithm.These methods effectively resolve issues prevalent in conventional methodolo-gies,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.