Analysis and Prediction of Disruptions in Metro Networks

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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 languageEnglish
Title of host publication2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)9781538694848
ISBN (Print)978-1-5386-9485-5
DOIs
Publication statusPublished - 2019
Event6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019 - Krakow, Poland
Duration: 5 Jun 20197 Jun 2019

Conference

Conference6th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2019
CountryPoland
CityKrakow
Period5/06/197/06/19

Keywords

  • clustering
  • exposure
  • prediction
  • vulnerability

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