Unsupervised approach towards analysing the public transport bunching swings formation phenomenon

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. We present the approach for extracting isolated bunching swings formations (BSF) and show different cases of BSFs, some of which can persist for a considerable time. We applied our approach to the tram line 1 of The Hague, and computed and presented four different patterns of BSFs, which we name high passenger load, whole route, evening, end of route, long duration. We analyse each bunching swings formation type in detail.

Automated summary

The study analyzed public transport data from The Hague, Netherlands, focusing on the impact of vehicle bunching and its correlation with passenger load. Using unsupervised machine learning techniques, the phenomenon of vehicle bunching was extracted, revealing patterns in bunching probabilities for weekdays and weekends.