Reinforcement Learning in Railway Timetable Rescheduling

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Abstract

Real-time railway traffic management is important for the daily operations of railway systems. It predicts and resolves operational conflicts caused by events like excessive passenger boardings/alightings. Traditional optimization methods for this problem are restricted by the size of the problem instances. Therefore, this paper proposes a reinforcement learning-based timetable rescheduling method. Our method learns how to reschedule a timetable off-line and then can be applied online to make an optimal dispatching decision immediately by sensing the current state of the railway environment. Experiments show that the rescheduling solution obtained by the proposed reinforcement learning method is affected by the state representation of the railway environment. The proposed method was tested to a part of the Dutch railways considering scenarios with single initial train delays and multiple initial train delays. In both cases, our method found high-quality rescheduling solutions within limited training episodes.
Original languageEnglish
Title of host publication23rd International Conference on Intelligent Transportation Systems (ITSC)
PublisherIEEE
Number of pages6
DOIs
Publication statusPublished - 2020
EventThe 23rd IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2020) - Rhodes, Greece
Duration: 20 Sep 202023 Sep 2020
https://www.ieee-itsc2020.org/

Conference

ConferenceThe 23rd IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2020)
CountryGreece
CityRhodes
Period20/09/2023/09/20
Internet address

Keywords

  • Railway traffic management
  • Timetable rescheduling
  • Reinforcement learning

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