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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