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We perform an analysis of public transport data from The Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location and automated fare collection data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques, namely clustering. We also show the correlation between bunching rate and passenger load, and bunching probability patterns for working days and weekends. Wedoi:10.1007/s12469-020-00251-z fatcat:cptcjwhctvgy7k5o6hnfdyi7km