Analysis and Prediction of Disruptions in Metro Networks

Menno Yap; Oded Cats

doi:10.1109/MTITS.2019.8883320

Analysis and Prediction of Disruptions in Metro Networks

Transport and Planning

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

7 Citations (Scopus)

21 Downloads (Pure)

Abstract

Public transport disruptions can result in major impacts for passengers and operator. Our study objective is to predict disruption exposure at different stations, incorporating their location-specific characteristics. Based on a 13-month incident database for the Washington metro network, we successfully develop a supervised learning model to predict the expected number of disruptions, per type, station and time of day. This supports public transport authorities and operators to prioritize what type of disruptions at what location to focus on, to potentially achieve the largest reduction in disruption exposure. Our clustering results show that start/terminal and transfer stations are most susceptible to disruptions, mainly due to operations-and vehicle-related disruptions.

Original language	English
Title of host publication	2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	7
ISBN (Electronic)	9781538694848
ISBN (Print)	978-1-5386-9485-5
DOIs	https://doi.org/10.1109/MTITS.2019.8883320
Publication status	Published - 2019
Event	6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019 - Krakow, Poland Duration: 5 Jun 2019 → 7 Jun 2019

Conference

Conference	6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019
Country/Territory	Poland
City	Krakow
Period	5/06/19 → 7/06/19

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

clustering
exposure
prediction
vulnerability

Access to Document

10.1109/MTITS.2019.8883320

08883320Final published version, 937 KB

Cite this

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title = "Analysis and Prediction of Disruptions in Metro Networks",

abstract = "Public transport disruptions can result in major impacts for passengers and operator. Our study objective is to predict disruption exposure at different stations, incorporating their location-specific characteristics. Based on a 13-month incident database for the Washington metro network, we successfully develop a supervised learning model to predict the expected number of disruptions, per type, station and time of day. This supports public transport authorities and operators to prioritize what type of disruptions at what location to focus on, to potentially achieve the largest reduction in disruption exposure. Our clustering results show that start/terminal and transfer stations are most susceptible to disruptions, mainly due to operations-and vehicle-related disruptions.",

keywords = "clustering, exposure, prediction, vulnerability",

author = "Menno Yap and Oded Cats",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019 ; Conference date: 05-06-2019 Through 07-06-2019",

year = "2019",

doi = "10.1109/MTITS.2019.8883320",

language = "English",

isbn = "978-1-5386-9485-5",

booktitle = "2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

}

Yap, M & Cats, O 2019, Analysis and Prediction of Disruptions in Metro Networks. in 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)., 8883320, Institute of Electrical and Electronics Engineers (IEEE), 6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019, Krakow, Poland, 5/06/19. https://doi.org/10.1109/MTITS.2019.8883320

Analysis and Prediction of Disruptions in Metro Networks. / Yap, Menno ; Cats, Oded.
2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). Institute of Electrical and Electronics Engineers (IEEE), 2019. 8883320.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Analysis and Prediction of Disruptions in Metro Networks

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PY - 2019

Y1 - 2019

N2 - Public transport disruptions can result in major impacts for passengers and operator. Our study objective is to predict disruption exposure at different stations, incorporating their location-specific characteristics. Based on a 13-month incident database for the Washington metro network, we successfully develop a supervised learning model to predict the expected number of disruptions, per type, station and time of day. This supports public transport authorities and operators to prioritize what type of disruptions at what location to focus on, to potentially achieve the largest reduction in disruption exposure. Our clustering results show that start/terminal and transfer stations are most susceptible to disruptions, mainly due to operations-and vehicle-related disruptions.

AB - Public transport disruptions can result in major impacts for passengers and operator. Our study objective is to predict disruption exposure at different stations, incorporating their location-specific characteristics. Based on a 13-month incident database for the Washington metro network, we successfully develop a supervised learning model to predict the expected number of disruptions, per type, station and time of day. This supports public transport authorities and operators to prioritize what type of disruptions at what location to focus on, to potentially achieve the largest reduction in disruption exposure. Our clustering results show that start/terminal and transfer stations are most susceptible to disruptions, mainly due to operations-and vehicle-related disruptions.

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KW - exposure

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KW - vulnerability

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DO - 10.1109/MTITS.2019.8883320

M3 - Conference contribution

SN - 978-1-5386-9485-5

BT - 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019

Y2 - 5 June 2019 through 7 June 2019

ER -

Analysis and Prediction of Disruptions in Metro Networks

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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