Detecting metro service disruptions via large-scale vehicle location data
File(s)
Published version
Author(s)
Zhang, Nan
Graham, Daniel J
Bansal, Prateek
Hörcher, Daniel
Type
Journal Article
Abstract
Urban metro systems are often affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. The crucial prerequisite of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. To pursue this goal, we detect the abnormal deviations in trains’ headway relative to their regular services by using Gaussian mixture models. Our method is a unique contribution in the sense that it proposes a novel, probabilistic, unsupervised clustering framework and it can effectively detect any type of service interruptions, including minor delays of just a few minutes. In contrast to traditional manual inspections and other detection methods based on social media data or smart card data, which suffer from human errors, limited monitoring coverage, and potential bias, our approach uses information on train trajectories derived from automated vehicle location (train movement) data. As an important research output, this paper delivers innovative analyses of the propagation progress of disruptions along metro lines, which enables us to distinguish primary and secondary disruptions as well as effective recovery interventions performed by operators.
Date Issued
2022-11
Date Acceptance
2022-09-02
Citation
Transportation Research Part C: Emerging Technologies, 2022, 144, pp.1-19
ISSN
0968-090X
Publisher
Elsevier BV
Start Page
1
End Page
19
Journal / Book Title
Transportation Research Part C: Emerging Technologies
Volume
144
Copyright Statement
© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
(http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.sciencedirect.com/science/article/pii/S0968090X22002935?via%3Dihub
Subjects
Logistics & Transportation
08 Information and Computing Sciences
09 Engineering
15 Commerce, Management, Tourism and Services
Publication Status
Published
Article Number
103880
Date Publish Online
2022-09-27