Dang Huu-Tu, Gaudou Benoit, Verstaevel Nicolas
UMR 5505 IRIT, Université Toulouse Capitole, 31000 Toulouse, France.
Sensors (Basel). 2024 Mar 2;24(5):1639. doi: 10.3390/s24051639.
Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based model to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data are extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimises the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimising computational performance compared to other models.
大规模人群现象的建模很复杂，因为行人的行为需要在战略、战术和操作层面进行描述，并且会受到人群密度的影响。微观模型能够模拟低密度情况下的动态，而介观模型在密集情况下表现更佳。本文提出并评估了一种新颖的基于智能体的模型，使智能体能够根据局部密度动态改变其操作模型。通过法国里昂灯光节行人的案例研究，探讨了将微观和介观模型结合用于多尺度模拟的能力。行人流出数据从节日期间人群退场的视频记录中提取。使用遗传算法对混合模型进行校准和验证，该算法优化模拟流出数据与观测流出数据之间的匹配。此外，随后进行局部敏感性分析，以确定模型中最敏感的参数。最后，在密度图和计算时间方面，将混合模型的性能与不同模型进行比较。结果表明，与其他模型相比，混合模型有能力在优化计算性能的同时，有效模拟不同密度场景下的行人。
