Complex network model for railway timetable stability optimisation

Meng, Xuelei; Jia, Limin; Xiang, Wei

doi:10.1049/iet-its.2018.5257

Cited by 14 publications

(5 citation statements)

References 33 publications

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“…en h t−1′ is stitched with the input x t . A tanh function is used to shrink the data to the range [−1, 1], that is, h t′ , as shown in equation (13). h t′ mainly contains the current input x t .…”

Section: Internal Structure Of Grumentioning

confidence: 99%

“…Although researchers have conducted extensive research on train timetable in the past few decades, trains in metro systems are still often subject to unexpected disturbances, such as a sudden increase in passenger flow, unexpected accidents, and unplanned parking [13,14]. To solve this kind of problem, researchers have proposed many train timetable rescheduling (TTR) methods [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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

Liao

Zhang

et al. 2020

Journal of Advanced Transportation

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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.

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Section: Internal Structure Of Grumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liao

Zhang

et al. 2020

Journal of Advanced Transportation

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“…Their study took metro lines as subsystems and further simplified them as nodes, providing perspectives for complex network research. Meng et al [9] employed the Complex Network Theory to study the optimization of train timetable, and proposed practical methods to improve the stability of train timetable. Zhang et al [10] analyzed the stability of urban public transport network based on the Complex Network Theory, and provided some valuable insights for future public transport network planning.…”

Section: Introductionmentioning

confidence: 99%

The analysis and solution for intercity travel behaviors during holidays in the post-epidemic era based on big data

Zhang

Gao

2023

PLoS ONE

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The COVID-19 had a huge impact on the transportation industry. In the post-epidemic stage, intercity transportation will face great challenges as places are unsealed, tourism and other service industries begin to recover, and residents’ travel demand gradually increases. An in-depth study of residents’ intercity travel behavior during holidays in the post-epidemic era will help restore public trust in public transportation and improve the quality of public transportation services. Based on traditional research on ways of travelling, the study adopted the Complex Network Analysis Theory. The city clusters of Shandong Peninsula were taken as the research region. The research studied the impact of the differences in regional attributes of the cities in Shandong Peninsula on residents’ intercity travel in the post-epidemic times. A dynamic evolution model of how residents choose to travel was built to simulate the changes to their ways of traveling in the post-epidemic era under two conditions, which are: traveling under the government’s supervision of intercity travel and traveling under the government’s optimization of intercity travel conditions. The conclusions drawn from the analyses of Complex Network Theory and Evolutionary Game Theory are as follows. First, in the holiday intercity travel in the post-epidemic times, the neighboring cities of Shandong Peninsula are closely connected, thus traveling between neighboring cities dominates intercity travel. Second, the travel network concentration of residents on long-term holidays is lower than that on short-term holidays, and the migration intensity of residents is higher than that on short-term holidays, while the willingness of residents’ migration on short-term holidays is higher than that on long-term holidays. The willingness to migrate on holidays is generally lower than that before the epidemic. Third, in a normal intercity travel network, the travel between two cities with medium and long distances is mainly by public transport. However, the dominance of public transport will be affected under the impact of the epidemic. In short-distance travel between two cities, private transport is in an advantageous position, and under the impact of the epidemic, this advantage will become more significant. The government can improve the position of public transport in short-distance travel by making optimizations.

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“…Hence we only consider the trains running in one direction. Various results have been developed for the problem of train delay propagation through the time supplement and buffer time reserved in scheduled timetables [14–19]. For instance, Liebchen

e t

a l

.…”

Section: Introductionmentioning

confidence: 99%

Scheduling‐measure dependent modelling of delay propagation on a single railway line: A matrix transformation approach

et al. 2021

IET Intelligent Trans Sys

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This paper proposes an analytical delay propagation model for single railway lines based on the max-plus algebra theory. The scheduling measures taken by dispatchers, including re-timing and re-ordering, will be incorporated into our delay propagation model using a matrix transformation method. An analysis of delay propagation under some typical emergencies such as segment blockages and train speed limitation is performed. Numerical simulations show that the proposed train delay propagation model can predict emergencyinduced train delays under different scheduling strategies, thus may give a guidance to improve the traffic management. In the high-speed railway train system, the scheduling measures taken by dispatchers, such as re-timing and re-ordering, can be formulated as a delay propagation model using a matrix transformation method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Complex network model for railway timetable stability optimisation

Cited by 14 publications

References 33 publications

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

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

The analysis and solution for intercity travel behaviors during holidays in the post-epidemic era based on big data

Scheduling‐measure dependent modelling of delay propagation on a single railway line: A matrix transformation approach

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