K-means clustering method in organizing passenger transportation in a smart city

An analysis of a heterogeneous data set on the duration of electric transport races in an average-sized city was conducted.The possibilities of using the K-means clustering method in organizing passenger transportation in a smart city were studied, including the analysis of passenger flows by passenger types, identification of transport hotspots, identification of inefficient routes or their sections, and construction of dynamic models for predicting changes in flows, as well as the features of its application for optimizing the operation of the transport system were determined.Data analysis revealed sections of routes with different intensity of transport flows, depending on their location in urban areas, seasonality, events in the city, changes in transport flows due to detours, repair work, etc.An algorithm for selecting a clustering method was proposed based on clustering quality assessment metrics, including the elbow method, the silhouette method, and the Calinski-Harabasz index.It is recommended to use clustering to create routes with reduced waiting times, fewer transfers, and compliance with passenger needs.