Unsupervised approach to bunching swings phenomenon analysis

Unsupervised approach to bunching swings phenomenon analysis

Degeler, V. (TU Delft Transport and Planning)
Heydenrijk-Ottens, L.J.C. (TU Delft Transport and Planning)
Luo, D. (TU Delft Transport and Planning)
van Oort, N. (TU Delft Transport and Planning)

2018

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.

Public transport
Machine learning
Clustering
Bunching
Passenger load
Bunching probability

http://resolver.tudelft.nl/uuid:a7dc130d-7b51-4d22-a6df-78e84dd87631

Proceedings of CASPT 2018: 23-25 July, Brisbane, Australia

Caspt 2018: 14th Conference on Advanced Systems in Public Transport and TransitData 2018, 2018-07-23 → 2018-07-25, Brisbane Convention and Exhibition Centre, Brisbane, Australia

Institutional Repository

conference paper

© 2018 V. Degeler, L.J.C. Heydenrijk-Ottens, D. Luo, N. van Oort