Microscopic Crowd Movement Modeling, Simulation, and Intervention Decision-making Framework Based on Physics-informed Machine Learning
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摘要: 人群移动是影响城市公共安全及应急管理的重要因素, 如何对其进行高精度的建模仿真和有效干预是亟待解决的问题. 为此, 提出一种物理信息机器学习驱动的微观人群移动建模仿真与干预决策框架. 基于平行智能思想, 该框架构建“数据感知−融合建模−动态仿真−智能干预”四层闭环结构, 形成从建模仿真到策略生成、执行及反馈修正的完整链路. 针对人群的移动仿真与引导决策问题, 分别提出基于物理信息时空图卷积网络的导航势能场模型和物理信息多智能体深度确定性策略梯度算法, 有效解决了传统方法中模型准确性较差、仿真与干预孤立以及决策依赖人工经验的问题. 最后, 基于真实数据集开展仿真实验验证了所提框架的有效性.Abstract: 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.
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图 5 物理信息机器学习驱动的微观人群移动建模仿真与干预决策框架
Fig. 5 A PIML-driven framework for microscopic crowd movement modeling, simulation, and intervention decision-making
图 6 基于物理信息时空图卷积的人群移动导航势能场模型
Fig. 6 The navigation potential field model for crowd movement based on physics-informed spatiotemporal graph convolutional network
图 9 仿真移动距离和平均速度的核密度分布与真实数据对比
Fig. 9 Comparison of simulated movement distance and average velocities kernel density distributions with real data
图 10 五个场景仿真人群流量变化与真实流量变化情况比较
Fig. 10 Comparison of simulated and real crowd flow variations across five scenarios
图 11 eth场景中3个行人的仿真轨迹与真实轨迹比较
Fig. 11 Comparison of simulated and real trajectories for three pedestrians in the eth scenario
图 12 单引导目标和多引导目标任务平均奖励训练曲线
Fig. 12 Average reward training curves for single-target and multi-target guidance tasks
图 13 单引导目标和多引导目标任务成功率与碰撞次数训练曲线
Fig. 13 Training curves of task success rate and collision frequency for single-target and multi-target guidance tasks
图 14 行人在引导干预下的移动瞬时状态及完整轨迹
Fig. 14 Instantaneous states and complete trajectories of pedestrian movement under guidance intervention
图 15 行人移动过程中周围人群密度变化
Fig. 15 Variation of local crowd density during pedestrian movement
图 16 单向通道人群流动场景示意
Fig. 16 Illustration of a unidirectional passage crowd flow scenario
图 17 疏散过程中通道内人群密度变化曲线
Fig. 17 Crowd density variation curve in the passage during the evacuation process
图 18 不同人群规模下的仿真与引导干预计算时间花销
Fig. 18 Computation time cost of simulation and guidance intervention under different crowd scales
B1 不同$ \omega_{p} $取值下PI-STGCN的ADE性能
B1 ADE performance of PI-STGCN with different $ \omega_{p} $ values
表 1 人群移动仿真实验agent信息
Table 1 Agent information for crowd movement simulation experiments
场景 eth hotel univ zara1 zara2 agent数量 255 112 364 139 185 平均速度(m/s) 1.63 1.01 0.89 1.24 1.19 
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B1 人群移动仿真实验参数
B1 Crowd movement simulation experiment parameters
参数名称 值 $ P\big(t\big) $ 阶次 4 $ \omega_{p} $ 0.10 $ \omega_{c} $ [0.01, 5] $ k_{g_{i}} $ 10 $ \lambda_{o} $ 25 $ \lambda_{h} $ 4 $ \Delta t $ 0.4 s $ T_{d} $ 12
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B2 人群引导干预实验参数
B2 Crowd guidance intervention experiment parameters
参数名称 值 $ l $ 阶次 6 $ \left(\beta_{{}_{obs}},\;\beta_{{}_{ped}}\right) $ $ (5,\;1.2) $ $ {w}_{p} $ 15 $ {w}_{c} $ −5 $ {w}_{d} $ 5 $ {w}_{t} $ −2 $ {w}_{s} $ 25 $ \xi_A $ $ 1 \times 10^{-4} $ $ \xi_C $ $ 1 \times 10^{-3} $ $ \tau $ 0.01
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