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In this study, a combination of well-established algorithms and real-world data was implemented for the forward-looking problem of future vertiport network design in a large metropolitan city. The locations of vertiports were selected to operate urban air mobility (UAM) in the Seoul metropolitan area based on the population of commuters, and a noise priority route was created to minimize the number of people affected by noise using Aviation Environmental Design Tool (AEDT) software. Demand data were analyzed using survey data from the commuting population and were marked on a map using MATLAB. To cluster the data, the K-means algorithm function built in MATLAB was used to select the center of the cluster as the location of the vertiports, and the accuracy and reliability of the clustering were evaluated using silhouette techniques. The locations of the selected vertiports were also identified using satellite image maps to ensure that the location of the selected vertiports were suitable for the actual vertiport location, and if the location was not appropriate, final vertiports were selected through the repositioning process. A helicopter model was then used to analyze the amount of noise reduction achieved by the noise priority route, which is the route between the selected K-UAM vertiports compared to the shortest distance route. As a result, it was shown that the noise priority route that minimized the amount of noise exposure was more efficient than the business priority routes.

Selection of Vertiports Using K-Means Algorithm and Noise Analyses for Urban Air Mobility (UAM) in the Seoul Metropolitan Area