A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances
Joint Authors
Zhang, Shiwen
Gong, Cheng
Liao, Jinlin
Zhang, Feng
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-12
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Considering that uncertain dwell disturbances often occur at metro stations, researchers have proposed many methods for solving the train timetable rescheduling (TTR) problem.
This paper proposes a Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) method, which is a real-time TTR method based on deep learning.
The proposed method takes the Gate Recurrent Unit (GRU) network as the decision network and uses the results produced by the Modified Genetic Algorithm (MGA) as the training set of the decision network.
A well-trained decision network can provide effective solutions in real time after random disturbances occur, in order to optimize the net traction energy consumption of trains in metro systems.
Based on the Shanghai Metro Line One (SML1) pilot network, this paper establishes a comprehensive model of the metro system as a training and testing environment to verify the energy-saving effect and real-time performance of the proposed method in solving the TTR problem.
The experimental results show that in the two-train metro system, the three-train metro system, and the five-train metro system, the MGA-GRU method can save an average of energy by 4.45%, 6.16%, and 7.19%, while the average decision time is only 0.15 s, 0.27 s, and 0.33 s, respectively.
American Psychological Association (APA)
Liao, Jinlin& Zhang, Feng& Zhang, Shiwen& Gong, Cheng. 2020. A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1180881
Modern Language Association (MLA)
Liao, Jinlin…[et al.]. A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances. Journal of Advanced Transportation No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1180881
American Medical Association (AMA)
Liao, Jinlin& Zhang, Feng& Zhang, Shiwen& Gong, Cheng. A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1180881
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1180881