  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.
