Unsupervised approach towards analysing the public transport bunching swings formation phenomenon
Document Type
Journal Article
Publication Date
2021
Subject Area
place - europe, place - urban, mode - tram/light rail, technology - automatic vehicle monitoring, planning - methods
Keywords
Public transport, Machine learning, Clustering, Bunching, Passenger load, Bunching probability
Abstract
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.
Rights
Permission to publish the abstract has been given by SpringerLink, copyright remains with them.
Recommended Citation
Degeler, V., Heydenrijk-Ottens, L., Luo, D., van Oort, N., & van Lint, H. (2021). Unsupervised approach towards analysing the public transport bunching swings formation phenomenon. Public Transport, Vol. 13, pp. 533–555.
