A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances

Abstract

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.

Energy Efficiency in Electric Transportation Systems

“…Solving the train-timetable-rescheduling problem is the objective of the research of Liao et al in [13]. By using a modified genetic algorithm-gate recurrent unit real-time method based on deep learning, it is shown that a well-trained decision network can provide effective solutions after random disturbances occur, so that the net traction energy consumption of trains is optimized.…”

Joint Optimal Train Rescheduling and Passenger Flow Control for Speed Limit and High-Demand Scenarios of Urban Rail Transits

“…Yin et al [13] discussed a rescheduling method considering fexible addition of backup trains and the train operation efciency. Liao et al [14] proposed a deep learning-based real-time rescheduling method considering energy saving. Yin et al [15] established an optimization model aiming at saving energy and passenger waiting time.…”