Unsupervised approach to bunching swings phenomenon analysis
Viktoriya Degeler (TU Delft - Transport and Planning)
Léonie Heydenrijk-Ottens (TU Delft - Transport and Planning)
D. Luo (TU Delft - Transport and Planning)
N. van Oort (TU Delft - Transport and Planning)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.