STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation

• URL: http://arxiv.org/abs/2604.02756v1
• Date: Fri, 03 Apr 2026 06:06:04 GMT
• Title: STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation
• Abstract summary: We propose a novel framework that guides microscopic trajectory prediction with macroscopic physics.<n>A Neural Ordinary Differential Equation is employed to model the macroscopic density evolution driven by individual movements.<n>The proposed STDDN method has demonstrated significantly superior simulation performance compared to state-of-the-art methods on long-term tasks.
• Score: 13.228132733021376
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate crowd simulation is crucial for public safety management, emergency evacuation planning, and intelligent transportation systems. However, existing methods, which typically model crowds as a collection of independent individual trajectories, are limited in their ability to capture macroscopic physical laws. This microscopic approach often leads to error accumulation and compromises simulation stability. Furthermore, deep learning-driven methods tend to suffer from low inference efficiency and high computational overhead, making them impractical for large-scale, efficient simulations. To address these challenges, we propose the Spatio-Temporal Decoupled Differential Equation Network (STDDN), a novel framework that guides microscopic trajectory prediction with macroscopic physics. We innovatively introduce the continuity equation from fluid dynamics as a strong physical constraint. A Neural Ordinary Differential Equation (Neural ODE) is employed to model the macroscopic density evolution driven by individual movements, thereby physically regularizing the microscopic trajectory prediction model. We design a density-velocity coupled dynamic graph learning module to formulate the derivative of the density field within the Neural ODE, effectively mitigating error accumulation. We also propose a differentiable density mapping module to eliminate discontinuous gradients caused by discretization and introduce a cross-grid detection module to accurately model the impact of individual cross-grid movements on local density changes. The proposed STDDN method has demonstrated significantly superior simulation performance compared to state-of-the-art methods on long-term tasks across four real-world datasets, as well as a major reduction in inference latency.

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