Imperial College London
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Dr Daniel Hörcher

Faculty of Engineering, Department of Civil and Environmental Engineering

Research Associate
 
 
 
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Contact

 

d.horcher

 
 
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Location

 

Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zhang:2022:10.1016/j.trc.2022.103880,
author = {Zhang, N and Graham, DJ and Bansal, P and Hörcher, D},
doi = {10.1016/j.trc.2022.103880},
journal = {Transportation Research Part C: Emerging Technologies},
pages = {1--19},
title = {Detecting metro service disruptions via large-scale vehicle location data},
url = {http://dx.doi.org/10.1016/j.trc.2022.103880},
volume = {144},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Zhang,N
AU  - Graham,DJ
AU  - Bansal,P
AU  - Hörcher,D
DO  - 10.1016/j.trc.2022.103880
EP  - 19
PY  - 2022///
SN  - 0968-090X
SP  - 1
TI  - Detecting metro service disruptions via large-scale vehicle location data
T2  - Transportation Research Part C: Emerging Technologies
UR  - http://dx.doi.org/10.1016/j.trc.2022.103880
UR  - https://www.sciencedirect.com/science/article/pii/S0968090X22002935?via%3Dihub
UR  - http://hdl.handle.net/10044/1/100212
VL  - 144
ER  -
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