Unsupervised approach to bunching swings phenomenon analysis
V. Degeler (TU Delft - Transport and Planning)
L.J.C. Heydenrijk-Ottens (TU Delft - Transport and Planning)
Ding Luo (TU Delft - Transport and Planning)
Niels van Van Oort (TU Delft - Transport and Planning)
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Abstract
We perform analysis of public transport data from March 2015 from The
Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location (AVL) and automated fare collection (AFC) data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques, namely clustering. We show different cases of bunching swings, some of which can persist for a considerable time. We also show the correlation of bunching rate with passenger load, and bunching probability patterns for working days and weekends. We show, how formations of bunching swings can be extracted, and clustered into four different types, which we name "high passenger load", "whole route", "evening late route", "long duration". We analyse each bunching swings formation type in detail.