CellEVAC：基于行为优化的人群疏散自适应引导系统
CellEVAC: An adaptive guidance system for crowd evacuation through behavioral optimization
摘要：
人群疏散过程的一个关键方面是个人决策的动态性。在这里，我们研究如何有利于一个协调的群体动态通过最佳的出口选择指令，使用行为策略优化。我们提出并评估了一个自适应引导系统（基于细胞的人群疏散，CellEVAC），动态分配颜色的细胞在一个基于细胞的行人定位基础设施，以提供有效的出口选择指示。CellEVAC的操作模块实现了一个优化的离散选择模型，该模型集成了使疏散人员适应其退出选择的影响因素。为了优化模型，我们使用了一个仿真优化建模框架，该框架集成了基于经典社会力模型的微观行人仿真。我们通过使用模拟出口门动态的行人基本图来特别关注安全性。CellEVAC已经在模拟真实场景（马德里竞技场）中进行了测试，在不同的外部行人流量模式下模拟复杂的行人互动。结果表明，CellEVAC优于不使用该系统的疏散过程，随着相互作用变得复杂，其性能呈指数级提高。我们比较了我们的系统与现有的方法的基础上笛卡尔遗传编程。我们的系统表现出更好的整体性能，在安全性，疏散时间，和出口选择决策的修订数量。进一步的分析还显示，笛卡尔遗传编程产生的自然行人反应和运动比CellEVAC少。决策逻辑模块是建立在行为模型上的，这一事实似乎有利于更自然和有效的反应。我们还发现，我们的建议对疏散有积极的影响，即使遵守率很低（40%）。
英文摘要：
A critical aspect of crowds' evacuation processes is the dynamism of individual decision making. Here, we investigate how to favor a coordinated group dynamic through optimal exit-choice instructions using behavioral strategy optimization. We propose and evaluate an adaptive guidance system (Cell-based Crowd Evacuation, CellEVAC) that dynamically allocates colors to cells in a cell-based pedestrian positioning infrastructure, to provide efficient exit-choice indications. The operational module of CellEVAC implements an optimized discrete-choice model that integrates the influential factors that would make evacuees adapt their exit choice. To optimize the model, we used a simulation-optimization modeling framework that integrates microscopic pedestrian simulation based on the classical Social Force Model. We paid particular attention to safety by using Pedestrian Fundamental Diagrams that model the dynamics of the exit gates. CellEVAC has been tested in a simulated real scenario (Madrid Arena) under different external pedestrian flow patterns that simulate complex pedestrian interactions. Results showed that CellEVAC outperforms evacuation processes in which the system is not used, with an exponential improvement as interactions become complex. We compared our system with an existing approach based on Cartesian Genetic Programming. Our system exhibited a better overall performance in terms of safety, evacuation time, and the number of revisions of exit-choice decisions. Further analyses also revealed that Cartesian Genetic Programming generates less natural pedestrian reactions and movements than CellEVAC. The fact that the decision logic module is built upon a behavioral model seems to favor a more natural and effective response. We also found that our proposal has a positive influence on evacuations even for a low compliance rate (40%).
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