## Evolutionary k-means Graph Clustering Method to Obtain the hub&spoke Structure for Warsaw Communication System

### Jarosław Stańczak, Jan Owsiński, Barbara Mażbic-Kulma, Aleksy Barski, Krzysztof Sęp

DOI: http://dx.doi.org/10.15439/2023F9176

Citation: Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 37, pages 295–300 (2023)

Abstract. The k-means method is one of the most frequently used clustering methods due to its efficiency and ease of modification. This paper presents its modification used for clustering in graphs. The method is presented on the example of generating the hub\&spoke structure in the graph of public transport connections in Warsaw. Optimization of the public transport is one of the most important task for large cities. An efficient transport system is very important for its inhabitants. One of possible solutions is introducing the idea of hub \& spoke to communication system. In this approach is important to detect main stations, called hubs, which will create axes of high-speed connections (city trains, metro, high-speed trams), from which passengers will transfer to slower local connections to get to their rather close destinations. In the presented approach we propose to find locations of such main changeover stations using an evolutionary k-means algorithm.

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