Analysis and Prediction of Disruptions in Metro Networks

Analysis and Prediction of Disruptions in Metro Networks

Yap, M.D. (TU Delft Transport and Planning)
Cats, O. (TU Delft Transport and Planning)

2019

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.

clustering
exposure
prediction
vulnerability

http://resolver.tudelft.nl/uuid:85a06633-6b8b-47a5-be3c-9d258b6e63f5

https://doi.org/10.1109/MTITS.2019.8883320

Institute of Electrical and Electronics Engineers (IEEE)

2020-04-28

978-1-5386-9485-5

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

6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019, 2019-06-05 → 2019-06-07, Krakow, Poland

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.

Institutional Repository

conference paper

© 2019 M.D. Yap, O. Cats