Print Email Facebook Twitter Reinforcement Learning in Railway Timetable Rescheduling Title Reinforcement Learning in Railway Timetable Rescheduling Author Zhu, Y. (TU Delft Transport and Planning) Wang, H. (TU Delft Railway Engineering) Goverde, R.M.P. (TU Delft Transport and Planning) Date 2020 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. Subject Railway traffic managementTimetable reschedulingReinforcement learning To reference this document use: http://resolver.tudelft.nl/uuid:671ed708-5fb7-4a2a-94ec-3fec866b0936 DOI https://doi.org/10.1109/ITSC45102.2020.9294188 Publisher IEEE Embargo date 2021-03-23 ISBN 9781728141497 Source 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 Event The 23rd IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2020), 2020-09-20 → 2020-09-23, Rhodes, Greece Series 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2020 Y. Zhu, H. Wang, R.M.P. Goverde Files PDF 09294188.pdf 1.05 MB Close viewer /islandora/object/uuid:671ed708-5fb7-4a2a-94ec-3fec866b0936/datastream/OBJ/view