Large-scale urban networks are usually loaded heterogeneously with a polycentric congestion pattern, resulting in a highly scattered network macroscopic fundamental diagram (NMFD or MFD). Thus, researchers have tried to partition city networks into homogeneous subzones. In this study, a six-step partitioning algorithm is proposed. The framework allows the NMFD information to be used. It combines traffic variables and geographic connectivity to obtain partitions. The framework includes graph definition, data preprocessing, feature handling, clusters and partitions identification, and boundaries reshaping. Tests on a simplified grid network and the city of the Melbourne road network demonstrate the suitability of the framework for characterizing the traffic states by the partitions. The framework on the missing data scenarios and monocentric and polycentric traffic concentrations scenarios, as well as applying multiple data types, has been challenged. Thereafter, parameter impact analysis demonstrates that manipulation of the parameters enables users to find the desired partitions. Last but not least, a comparison with an existing method also implies the uniqueness and efficiency of the developed framework.

中文翻译：

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为交通管理政策划分具有多中心拥塞模式的城市网络：识别受保护的网络

大型城市网络通常以多中心拥塞模式异构加载，导致高度分散的网络宏观基本图（NMFD 或 MFD）。因此，研究人员试图将城市网络划分为同质子区域。在这项研究中，提出了一种六步划分算法。该框架允许使用 NMFD 信息。它结合了流量变量和地理连通性来获得分区。该框架包括图形定义、数据预处理、特征处理、集群和分区识别以及边界重塑。对简化的网格网络和墨尔本市道路网络的测试证明了该框架适用于通过分区来表征交通状态。关于缺失数据场景和单中心和多中心交通集中场景以及应用多种数据类型的框架受到了挑战。此后，参数影响分析表明，对参数的操作使用户能够找到所需的分区。最后但并非最不重要的一点是，与现有方法的比较也表明所开发框架的独特性和效率。