Forecasting the effect of traffic control strategies in railway systems: A hybrid machine learning method

Luo, Jie; Wen, Chao; Peng, Qiyuan; Qin, Yong; Huang, Ping

doi:10.1016/j.physa.2023.128793

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…In [61][62][63], the authors use models to assess the reliability of the railway system and improve the accuracy of the failure prediction to plan the optimal time for maintenance activities. The models developed and the methodologies used are characterized by a different approach to solving problems and are based on the mathematical methods of the Markov theory, the Poisson theory, the Monte Carlo modeling approach, the Bayesian theory and the analysis of failure modes and effects using the Taylor series or hybrid models [64][65][66][67][68]. The work [69] presents a study of equipment reliability based on analytical models using dependencies of reliability on the example of Wind Farm Equipment.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on and Assessment of the Reliability of Railway Transport Systems with Induction Motors

Gubarevych,

Duer,

Melkonova

et al. 2023

Energies

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Increasing the efficiency and reliability of modern railway transport is accompanied by an increase in monitoring and diagnostic systems for the current state of electric drives. Modern railway transport contains a large number of induction motors to ensure the operation of the drives of various mechanisms. In the article, based on the operational statistics of engine failures and the proposed scheme for diagnosing them, studies were carried out and a model was developed for assessing the reliability of a transport system equipped with an on-board diagnostic system for the current state. When building the models, the Markov method was used, including the construction of graphs for the five most relevant states of the induction electric motor during operation. The results obtained are relevant for evaluating the effectiveness of using the built-in diagnostic system and scheduling routine maintenance, which will affect the efficiency of railway transport. Based on the process of the diagnosis of railway transport systems with induction motors, five operating states of the object studied were interpreted: the state of full operation, state “S0”; the state of incomplete serviceability, state “S1”; critical serviceability, state “S2”; the state of the pre-damage condition, state “S3”; the state of unserviceability (defect), state “S4”. Subsequently, a five-state model of the operation process of railway transport systems with induction motors was developed. This model is also described by equations of state: Kolmogorov–Chapman equations. The reliability quantities determined form the basis for simulation reliability studies. The effect of the simulation study is the reliability quantities determined in the form of reliability functions and probabilities of the occurrences of the operating states of railway transport systems with induction motors; an important part of the reliability study of the system examined is to estimate the times of the occurrences in the object studied of the operating states in the future.

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Section: Literature Reviewmentioning

confidence: 99%

Research on and Assessment of the Reliability of Railway Transport Systems with Induction Motors

Gubarevych,

Duer,

Melkonova

et al. 2023

Energies

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Demand forecasting of shared bicycles based on combined deep learning models

Ma,

Liu

2024

Physica A: Statistical Mechanics and its Applications

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Retrospective assessment and forecast of the level of development of intelligent transport infrastructure (using the example of JSC Russian Railways)

Egorov,

Fedorova

2024

E3S Web of Conf.

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Objective: conduct a retrospective assessment and obtain a forecast of the level of development of the intellectual transport infrastructure of JSC Russian Railways in 2004-2027. Methods: econometric modeling, analysis, comparative approach, statistical method, generalization. Results: the system of indicators for assessing the level of development of an organization’s intelligent transport infrastructure has been developed; using the developed system of indicators, the level of development of the intellectual transport infrastructure of JSC Russian Railways was assessed retrospectively and dynamically during 2004-23; forecasts of these indicators for 2024-27 has been built on the basis of models of autoregression with their accuracy assessment; also the comparison has been made of the level of development of JSC Russian Railways intellectual transport infrastructure with the similar levels for BNSF railway company (USA) and CN railway company (Canada). Main conclusions: the results obtained can be used in further research of the development of the organization’s intelligent transport infrastructure.

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Forecasting the effect of traffic control strategies in railway systems: A hybrid machine learning method

Cited by 5 publications

References 40 publications

Research on and Assessment of the Reliability of Railway Transport Systems with Induction Motors

Research on and Assessment of the Reliability of Railway Transport Systems with Induction Motors

Demand forecasting of shared bicycles based on combined deep learning models

Retrospective assessment and forecast of the level of development of intelligent transport infrastructure (using the example of JSC Russian Railways)

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