We investigate the behavior of extended urban traffic networks within the framework of percolation theory by using real and synthetic traffic data. Our main focus shifts from the statistical properties of the cluster size distribution studied recently, to the spatial analysis of the clusters at criticality and to the definition of a similarity measure between whole urban configurations. We discover that the breakup patterns of the complete network, formed by the connected functional road clusters at criticality, show remarkable stability from one hour to the next, and predictability for different days at the same time. We prove this by showing how the average spatial distributions of the highest-rank clusters evolve over time, and by building a taxonomy of traffic states via dimensionality reduction of the distance matrix, obtained via a clustering similarity score. Finally, we show that a simple random percolation model can approximate the breakup patterns of heavy real traffic when long-ranged spatial correlations are imposed.

中文翻译：

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城市网络中交通中断模式的稳定性

我们通过使用真实和合成的交通数据，在渗透理论的框架内研究扩展城市交通网络的行为。我们的主要重点从最近研究的集群大小分布的统计特性转移到关键集群的空间分析以及整个城市配置之间相似性度量的定义。我们发现，由处于临界状态的连接的功能道路集群形成的完整网络的分裂模式，从一小时到下一小时表现出显着的稳定性，并且同时对不同的日子具有可预测性。我们通过展示最高等级集群的平均空间分布如何随时间演变，以及通过距离矩阵的降维来构建交通状态分类法来证明这一点，通过聚类相似度得分获得。最后，我们表明，当施加远程空间相关性时，一个简单的随机渗透模型可以近似模拟大量实际交通的分解模式。