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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    Title :

    Analysis and Prediction of Disruptions in Metro Networks


    Contributors:
    Yap, Menno (author) / Cats, Oded (author)


    Publication date :

    2019-06-01


    Size :

    813649 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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