It has been well known that properly coordinating travelers’ route choices will help mitigate urban traffic congestion. Accordingly, coordinated routing mechanisms (CRMs), functioned as coordinated network congestion management schemes, have been developed in recent years. They often employ game theory models to strategically coordinate the route decisions of a group of travelers en route with the aim to reduce network traffic congestion. However, existing studies do not provide the solution about how to form the traveler groups, such as who and how many travelers the CRMs should involve. This issue may jeopardize the efficiency and scalability of the CRMs in practical applications. To make up this gap, this study seeks to develop the clustering aided network modeling approaches to form the coordination groups (CGs), which have the proper members and sizes for supporting the merits of the congestion management schemes. Specifically, we define coordination potential as an index to indicate the potential benefit for coordinating travelers’ route decisions on network congestion reduction. Then we develop rigorous approaches to understand the mathematical features and further quantify the coordination potential between two travelers, and among multiple travelers. Built upon that, we form the CGs through a well-designed adaptive centroid-based clustering algorithm (ACCA). It secures a local optimal clustering solution, balancing the intra-cluster and inter-cluster CP, so that we can ensure a small system performance loss as we implement a CRM on each CG. The efficiency of the CG formation approach is evaluated by the CG-CRM scheme, which partitions the travelers into multiple CGs and independently coordinates the travelers’ route choices in each CG by a CRM. The numerical experiments built upon both Sioux Falls and Hardee city networks confirm the efficiency and effectiveness of the CG formation approach. Mainly, the CG-CRM outperforms the independent best response routing mechanism in system performance. As compared to the CRM working on a single group involving all travelers, the CG-CRM requires significantly less computation load with a minor compromise on the system performance. This merit becomes more apparent under high penetration and congested traffic condition. Therefore, we claim that the CG formation approach will significantly help the implementation of the coordinated congestion mitigation schemes in practice.

众所周知，适当协调旅行者的路线选择将有助于减轻城市交通拥堵。因此，近年来已经开发了用作协调网络拥塞管理方案的协调路由机制（CRM）。他们经常采用博弈论模型来策略性地协调一组旅客在途中的路线决策，以减少网络流量拥塞。但是，现有研究并未提供有关如何组成旅客群体的解决方案，例如CRM应当吸引谁和多少旅客。在实际应用中，此问题可能会危害CRM的效率和可伸缩性。为了弥补这一差距，本研究旨在开发聚类辅助网络建模方法以形成协调组（CG），具有适当的成员和规模以支持拥塞管理方案的优点。具体来说，我们将协调潜力定义为一个指标，以指示在减少网络拥塞方面协调旅行者的路线决策的潜在收益。然后，我们开发出严格的方法来理解数学特征，并进一步量化两个旅行者之间以及多个旅行者之间的协调潜力。在此基础上，我们通过精心设计的基于自适应质心的聚类算法（ACCA）形成CG。它确保了本地最佳集群解决方案，平衡了集群内和集群间CP，因此当我们在每个CG上实施CRM时，我们可以确保较小的系统性能损失。CG形成方法的效率通过CG-CRM方案进行评估，它将旅行者分为多个CG，并通过CRM在每个CG中独立地协调旅行者的路线选择。建立在苏福尔斯（Sioux Falls）和哈代（Harde）城市网络上的数值实验证实了CG形成方法的效率和有效性。CG-CRM主要在系统性能方面优于独立的最佳响应路由机制。与在一个由所有旅行者组成的小组中工作的CRM相比，CG-CRM所需的计算负载明显减少，并且对系统性能的影响很小。在高渗透率和拥挤的交通状况下，这一优点变得更加明显。因此，我们认为CG形成方法将在实践中极大地帮助实施协调的拥塞缓解方案。