In the modern city, the utilization rate of public transportation attached importance to the efficiency of public traffic. However, the unreasonable distribution of transportation platforms results in a low utilization rate. In this paper, we researched and evaluated the distribution of platforms -- bus and subway -- and proposed a method, called "partial area cluster" (PAC), to improve the utilization by changing and renewing the original distribution. The novel method was based on the K-means algorithm in the field of machine learning. PAC worked to search the suitable bus platforms as the center and modified the original one to the subway. Experience has shown that the use of public transport resources has increased by 20%. The study uses a similar cluster algorithm to solve transport networks' problems in a novel but practical term. As a result, the PAC is expected to be used extensively in the transportation system construction process.

翻译：在现代城市,公共交通的利用率重视公共交通的效率,然而,运输平台的不合理分布导致利用率低。在本论文中,我们研究并评价了公共汽车和地铁等平台的分布情况,并提出了一个称为“局部区集群”的方法,以通过改变和更新原始分布来提高利用率。新颖的方法基于机器学习领域的K- means算法。PAC努力寻找合适的公共汽车平台作为中心,并将原来的平台改为地铁。经验显示,公共交通资源的使用增加了20%。研究用类似的集群算法解决运输网络的问题,这是一个新颖而实用的术语。因此,预计PAC将在运输系统建设过程中广泛使用。