• Public transport: AI assesses resilience

    From ScienceDaily@1:317/3 to All on Thu Mar 17 22:30:44 2022
    Public transport: AI assesses resilience of timetables

    Date:
    March 17, 2022
    Source:
    Martin-Luther-Universita"t Halle-Wittenberg
    Summary:
    A brief traffic jam, a stuck door, or many passengers getting on
    and off at a stop - even small delays in the timetables of trains
    and buses can lead to major problems. A new artificial intelligence
    (AI) could help designing schedules that are less susceptible to
    those minor disruptions.



    FULL STORY ==========================================================================
    A brief traffic jam, a stuck door, or many passengers getting on and
    off at a stop -- even small delays in the timetables of trains and
    buses can lead to major problems. A new artificial intelligence (AI)
    could help designing schedules that are less susceptible to those
    minor disruptions. It was developed by a team from the Martin Luther
    University Halle-Wittenberg (MLU), the Fraunhofer Institute for Industrial Mathematics ITWM and the University of Kaiserslautern. The study was
    published in "Transportation Research Part C: Emerging Technologies."

    ==========================================================================
    The team was looking for an efficient way to test how well timetables can compensate for minor, unavoidable disruptions and delays. In technical
    terms, this is called robustness. Until now, such timetable optimisations
    have required elaborate computer simulations that calculate the routes
    of a large number of passengers under different scenarios. A single
    simulation can easily take several minutes of computing time. However,
    many thousands of such simulations are needed to optimise timetables. "Our
    new method enables a timetable's robustness to be very accurately
    estimated within milliseconds," says Professor Matthias Mu"ller-Hannemann
    from the Institute of Computer Science at MLU. The researchers from Halle
    and Kaiserslautern used numerous methods for evaluating timetables in
    order to train their artificial intelligence. The team tested the new
    AI using timetables for Go"ttingen and part of southern Lower Saxony
    and achieved very good results.

    "Delays are unavoidable. They happen, for example, when there is a
    traffic jam during rush hour, when a door of the train jams, or when
    a particularly large number of passengers get on or off at a stop," Mu"ller-Hannemann says. When transfers are tightly scheduled, even a few minutes of delay can lead to travellers missing their connections. "In
    the worst case, they miss the last connection of the day," adds co-author
    Ralf Ru"ckert. Another consequence is that vehicle rotations can be
    disrupted so that follow-on journeys begin with a delay and the problem continues to grow.

    There are limited ways to counteract such delays ahead of time: Travel
    times between stops and waiting times at stops could be more generously calculated, and larger time buffers could be planned at terminal stops
    and between subsequent trips. However, all this comes at the expense of economic efficiency. The new method could now help optimise timetables
    so that a very good balance can be achieved between passenger needs,
    such as fast connections and few transfers, timetable robustness against disruptions, and the external economic conditions of the transport
    companies.

    The study was supported by the Deutsche Forschungsgemeinschaft (DFG,
    German Research Foundation) within the framework of the research unit "Integrated Planning for Public Transport."

    ========================================================================== Story Source: Materials provided by Martin-Luther-Universita"t_Halle-Wittenberg. Note: Content may be edited
    for style and length.


    ========================================================================== Journal Reference:
    1. Matthias Mu"ller-Hannemann, Ralf Ru"ckert, Alexander Schiewe, Anita
    Scho"bel. Estimating the robustness of public transport schedules
    using machine learning. Transportation Research Part C: Emerging
    Technologies, 2022; 137: 103566 DOI: 10.1016/j.trc.2022.103566 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/03/220317094744.htm

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