Automated offline detection of disruptions using smart card data

A case study of the metro network of Washington DC

Master thesis (2020)

Authors

F.Z.G. Jasperse Civil Engineering & Geosciences

Contributors

O. Cats Transport and Planning - CEG (supervisor 1)
M. Snelder Transport and Planning - CEG (supervisor 1)
Yilin Huang System Engineering - TPM (supervisor 1)
P.K. Krishnakumari Transport and Planning - CEG (supervisor 1)
Faculty
Civil Engineering & Geosciences
Copyright: © 2020 Faye Jasperse
Machine learning Public transport Hierarchical clustering Offline disruption detection Smart card data Probabilistic classification
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Published Date

30-01-2020

Language

English

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Faculty

Civil Engineering & Geosciences

Abstract

Service reliability is one of the most important performance measures to public transport users. Detecting disruptions helps to measure service reliability, which can be used by public transport operators to improve this reliability. In this thesis, a methodology is described to automatically detect disruptions offline, using smart card data. The day-to-day regularity of delays is investigated using hierarchical clustering on a training set, to distinguish between regular and irregular delays. The clustering result is used to create a probabilistic classifier. This classifier is applied to the test set to find days that do not correspond to a regular pattern: irregular days. After that, disruptions are detected within the irregular days. The outcomes of this study can be applied in multiple ways. Locations where disruptions have occurred can be found and the related passenger delay can be calculated. This can help public transport operators to prioritise which locations to focus on to reduce passenger delays. Furthermore, not only public transport networks, but also other networks can benefit from the outcomes of this study. Speed data of road networks could be used to find disruptions that are caused by accidents, instead of regular traffic jams. On top of that, this study could be used as a step towards real-time disruption detection, for both public transport and road networks.