Chowell G, Hyman J M, Eubank S, Castillo-Chavez C
Center for Nonlinear Studies (MS B258), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Dec;68(6 Pt 2):066102. doi: 10.1103/PhysRevE.68.066102. Epub 2003 Dec 15.
Large scale simulations of the movements of people in a "virtual" city and their analyses are used to generate insights into understanding the dynamic processes that depend on the interactions between people. Models, based on these interactions, can be used in optimizing traffic flow, slowing the spread of infectious diseases, or predicting the change in cell phone usage in a disaster. We analyzed cumulative and aggregated data generated from the simulated movements of 1.6 x 10(6) individuals in a computer (pseudo-agent-based) model during a typical day in Portland, Oregon. This city is mapped into a graph with 181,206 nodes representing physical locations such as buildings. Connecting edges model individual's flow between nodes. Edge weights are constructed from the daily traffic of individuals moving between locations. The number of edges leaving a node (out-degree), the edge weights (out-traffic), and the edge weights per location (total out-traffic) are fitted well by power-law distributions. The power-law distributions also fit subgraphs based on work, school, and social/recreational activities. The resulting weighted graph is a "small world" and has scaling laws consistent with an underlying hierarchical structure. We also explore the time evolution of the largest connected component and the distribution of the component sizes. We observe a strong linear correlation between the out-degree and total out-traffic distributions and significant levels of clustering. We discuss how these network features can be used to characterize social networks and their relationship to dynamic processes.
对“虚拟”城市中人群移动进行大规模模拟并加以分析，以深入了解依赖于人与人之间互动的动态过程。基于这些互动的模型可用于优化交通流量、减缓传染病传播或预测灾难期间手机使用情况的变化。我们分析了在俄勒冈州波特兰市典型一天中，计算机（基于伪智能体）模型里1.6×10⁶个人的模拟移动所产生的累积和汇总数据。这座城市被映射到一个有181,206个节点的图中，这些节点代表诸如建筑物等物理位置。连接边模拟个体在节点之间的流动。边权重由在不同位置之间移动的个体的日常流量构建而成。离开一个节点的边的数量（出度）、边权重（出流量）以及每个位置的边权重（总出流量）都能很好地用幂律分布拟合。幂律分布也适用于基于工作、学校和社交/娱乐活动的子图。所得的加权图是一个“小世界”，并且具有与潜在层次结构一致的标度律。我们还探究了最大连通分量的时间演化以及分量大小的分布。我们观察到出度和总出流量分布之间存在很强的线性相关性以及显著程度的聚类。我们讨论了这些网络特征如何可用于刻画社会网络及其与动态过程的关系。
