Comparing Simple-radius and Doughnut Models of Collective Crowd Motion
Comparing Simple-radius and Doughnut Models of Collective Crowd Motion
复制标题
群体集体运动的简单半径模型和环形模型的比较
DOI：
10.1167/18.10.1036
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发表时间：
2018
影响因子：
1.8
通讯作者：
Gregory C. Dachner
中科院分区：
文献类型：
--
作者：
W. Warren;Gregory C. Dachner
在之前的研究中，我们开发了一种实验驱动的人群集体运动模型（Warren，CDPS，待出版）。行为动力学模型结合了局部“对齐”交互，其中行人将邻居的速度和方向与邻域模型相匹配（Rio、Rhea 和 Warren，2014），该模型计算多个邻居的加权平均值，权重随着距离达到 4m 而呈指数衰减（Warren 和 Dachner，VSS 2017；参见 Cuker 和 Smale，2007）。此外，我们发现随着到最近邻居的距离达到 11m，权重逐渐减小（Wirth, Warren (& Richmond), VSS 2016），形成更大的甜甜圈形邻域。在这里，我们探索多智能体模拟中的模型，以确定它产生集体运动的条件，并比较简单半径和甜甜圈邻域。每 20 秒运行一次，模拟 30 个具有人类参数的交互代理，并同步更新。他们在 5x6 网格上的初始位置是抖动的，初始条件是参数变化的：人际距离（IPD= 1-10m）、航向范围（±10 至±90）和速度范围（±0.1 至±0.9 m/s）。每个条件运行 20 次，并测量最终航向和速度的 SD。该模型在较宽的初始航向和速度范围内收敛到相干运动，但随着 IPD 的增加，收敛程度会降低。此外，随着初始条件的变化，智能体簇的数量 k 趋于增加。值得注意的是，甜甜圈模型比简单半径模型在更大范围的条件下收敛，为不依赖距离的“拓扑”邻域提供了稳健的替代方案（Wirth & Warren，VSS 2018）。我们目前正在将此物理模型与由光学变量驱动的基于视觉的模型进行比较（Dachner & Warren，VSS 2017，2018）。因此，甜甜圈模型生成的集体运动对于初始条件的变化具有鲁棒性。
In previous research, we developed an experiment-driven model of collective motion in human crowds (Warren, CDPS, in press). The behavioral dynamics model combines a local'alignment'interaction, in which a pedestrian matches the speed and heading of a neighbor (Rio, Rhea, & Warren, 2014) with a neighborhood model, which computes a weighted average of multiple neighbors, with weights that decay exponentially with distance out to 4m (Warren & Dachner, VSS 2017; cf. Cuker & Smale, 2007). In addition, we found that the weight decreases more gradually with the distance to the nearest neighbor out to 11m (Wirth, Warren (& Richmond), VSS 2016), forming a larger doughnut-shaped neighborhood. Here we explore the model in multi-agent simulations, to determine the conditions under which it generates collective motion and to compare the simple-radius and doughnut neighborhoods. 30 interacting agents, with human parameters, were simulated on each 20s run, with synchronous updating. Their initial positions on a 5x6 grid were jittered, and initial conditions were parametrically varied: interpersonal distance (IPD= 1-10m), heading range (±10 to±90), and speed range (±0.1 to±0.9 m/s). There were 20 runs per condition, and the SD of final heading and speed were measured. The model converges to coherent motion over a wide range of initial headings and speeds, but less so as IPD increases. In addition, the number k of clusters of agents tends to increase with variation in initial conditions. Notably, the doughnut model converges over a larger range of conditions than the simple-radius model, providing a robust alternative to a'topological'neighborhood that is not distance-dependent (Wirth & Warren, VSS 2018). We are currently comparing this physical model with a vision-based model driven by optical variables (Dachner & Warren, VSS 2017, 2018). Thus, the doughnut model generates collective motion that is robust to variation in initial conditions.