Applying K-means Clustering to the QGIS pluging that Assists in Planning New Service Area for On-demand Traffic Service in Sanyo-Onoda City

Abstract

In the previous research, we have developed the QGIS plugin to assist in planning a new service area for on-demand traffic service in Sanyo-Onoda City, mainly to assist in finding an optimal path that could be the longest with registered addresses, applying the idea of hop in the computer networking field. The result was considered valuable, but there was still a problem that it takes some time to find a path. In addition, it is not easy to set a parameter for finding a path. Therefore, applying an algorithm with a more effortless parameter setting and faster calculation is needed. In this research, we selected the k-means algorithm and applied it to the plugin.