疏散规划与评估中人群模型的定量比较
Quantitative Comparison Between Crowd Models for Evacuation Planning and Evaluation
摘要：
人群模拟正迅速成为疏散规划和评估的标准工具。然而，文献中的许多人群模型在结构上是不同的，很少有严格的校准对现实世界的出口数据，特别是在紧急情况下。在本文中，我们描述了一个过程，定量比较不同的人群模型或模型和现实世界的数据。我们模拟了三种模型：（1）格子气模型、（2）社会力模型、（3）RVO2模型，得到了（1）疏散时间、（2）分区疏散时间、（3）通道密度、（4）总行驶距离、（5）不便度、（6）流量等6个观测量的分布。然后，我们使用DISSTATIS程序来计算三个模型之间的统计距离的折衷矩阵。将这三种模型投影到折衷矩阵的前两个主成分上，我们发现晶格气和RVO2模型在疏散时间、通道密度和流速方面是相似的，而社会力和RVO2模型在总行进距离方面是相似的。最重要的是，我们发现，分区疏散时间的三个模型是非常不同的。因此，我们建议使用这个变量，如果它可以测量，作为不同模型之间的关键测试，也模型和现实世界之间。最后，我们比较了2008年5月四川地震期间的紧急疏散流量的模型流量，并发现社会力量模型与此实际数据吻合得最好。
英文摘要：
Crowd simulation is rapidly becoming a standard tool for evacuation planning and evaluation. However, the many crowd models in the literature are structurally different, and few have been rigorously calibrated against real-world egress data, especially in emergency situations. In this paper we describe a procedure to quantitatively compare different crowd models or between models and real-world data. We simulated three models: (1) the lattice gas model, (2) the social force model, and (3) the RVO2 model, and obtained the distributions of six observables: (1) evacuation time, (2) zoned evacuation time, (3) passage density, (4) total distance traveled, (5) inconvenience, and (6) flow rate. We then used the DISTATIS procedure to compute the compromise matrix of statistical distances between the three models. Projecting the three models onto the first two principal components of the compromise matrix, we find the lattice gas and RVO2 models are similar in terms of the evacuation time, passage density, and flow rates, whereas the social force and RVO2 models are similar in terms of the total distance traveled. Most importantly, we find that the zoned evacuation times of the three models to be very different from each other. Thus we propose to use this variable, if it can be measured, as the key test between different models, and also between models and the real world. Finally, we compared the model flow rates against the flow rate of an emergency evacuation during the May 2008 Sichuan earthquake, and found the social force model agrees best with this real data.
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