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

Jinlin Liao,Shiwen Zhang,Feng Zhang,Cheng Gong,Petr Dolezel

doi:10.1155/2020/8882554

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.

Highlights

Compared with ground transportation such as buses and taxis, urban metro systems have achieved rapid development worldwide due to the advantages of no traffic jams, large capacity, and high safety [1]
The Compensational Driving Strategy Algorithm (CDSA) proposed by Gong et al only rearranges the coasting speed of the disturbed trains, which does not adjust other trains’ coasting speeds and all trains’ dwell time. In response to these problems, this paper proposes a timetable rescheduling (TTR) method based on deep learning, called Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) by combining the modified Genetic Algorithm (MGA) with the Gate Recurrent Unit (GRU) network
In order to optimize the net traction energy consumption in real time after a dwell disturbance occurs, this paper proposes an MGA-GRU method based on deep learning. is method combines the modified Genetic Algorithm (MGA)

Summary

Introduction

Compared with ground transportation such as buses and taxis, urban metro systems have achieved rapid development worldwide due to the advantages of no traffic jams, large capacity, and high safety [1]. E experimental results show that the solutions of Q-learning are at least equivalent and generally superior to simple first-in-first-out (FIFO) and random walk methods that do not rely on learning agents Different from these two types of traditional methods, the TTR method studied in this paper aims to reschedule train timetable after disturbances occur to reduce traction energy consumption. E results show that compared with not using CDSA after a disturbance occurs, using CDSA can save 1.86% of energy on average These optimization methods (EBF, ACO, and GA) implemented by Zhao et al are not suitable for solving the TTR problem in real time due to the long calculation time.

Modeling

10 Reduce travel time

Experimental Verification

Findings

Conclusion