A Markovian–Bayesian Network for Risk Analysis of High Speed and Conventional Railway Lines Integrating Human Errors

Castillo, Enrique; Calviño, Aida; Grande, Zacarías; Sánchez-Cambronero, Santos; Gallego, Inmaculada; Rivas, Ana; Menéndez, José María

doi:10.1111/mice.12153

Cited by 40 publications

(24 citation statements)

References 27 publications

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“…To reduce the complexity of the problem, Castillo et al. () proposed a time partitioning technique, which was successfully applied in line planning and timetable optimization. Similar research has been conducted by Niu and Zhou (), which focused on metro service optimization with oversaturated demand during peak hours.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Passenger Flow Control and Bus‐Bridging Service for Commuting Metro Lines

Yang

Jin

et al. 2017

Computer aided Civil Eng

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing Passenger Flow Control and Bus‐Bridging Service for Commuting Metro Lines

Yang

Jin

et al. 2017

Computer aided Civil Eng

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“…In railroads, safety is one of the most important topics and has attracted tremendous attention recently due to some reported accidents (Castillo et al., , b; Wang et al., ). Among all the causes of train accidents in the United States, track defects are one of the leading reasons.…”

Section: Introductionmentioning

confidence: 99%

A Customized Hybrid Approach to Infrastructure Maintenance Scheduling in Railroad Networks under Variable Productivities

Xie

Lei

Ouyang

2018

Computer aided Civil Eng

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Railroads are maintained routinely by using various types of rail‐bound machines so as to achieve the longest possible rail life and reduce the safety risks associated with unanticipated rail failures. The rail maintenance routing and scheduling problem (RMRSP), which involves routing of multiple maintenance vehicles and scheduling of hundreds of maintenance jobs over a large‐scale network, is usually subject to various types of complex constraints and extremely difficult to solve. This article proposes a vehicle routing problem with time windows (VRPTW) formulation for RMRSP and develops a customized stepwise algorithm to solve the problem. A series of numerical experiments are conducted to demonstrate that the proposed algorithm works very effectively, significantly outperforming the state‐of‐the‐art commercial solver. The results of two real‐world instances from a Class I railroad company show that the proposed model and solution algorithm enable the expensive maintenance vehicles to achieve a higher level of utilization, that is, spending more time on working and less time on deadhead traveling.

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“…Nonparametric methods were also developed in the context of Bayesian inference (Jiang et al., ; Jiang and Mahadevan, ; Jiang et al., ; Sankararaman and Mahadevan, ). Research has been undertaken regarding anomaly detection by integrating AE technique and Bayesian theorem (Hensman et al., ; Schumacher et al., ; Masoud and Mohammad, ), and the applications of Bayesian inference to railway safety are also available in the literature (Andrade and Teixeira, ; Lam et al., ; Hu et al., ; Castillo et al., , b; Jamshidi et al., ). However, none of the above studies were addressing AE‐based rail damage detection in the context of Bayesian inference.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Probabilistic Approach for Acoustic Emission‐Based Rail Condition Assessment

Wang

Liu

2017

Computer aided Civil Eng

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The investigation described in this article aims at developing a Bayesian‐based approach for probabilistic assessment of rail health condition using acoustic emission monitoring data. It comprises the following three phases: (i) formulation of a frequency‐domain structural health index (SHI), via a linear transformation method, tailored to damage‐sensitive frequency bandwidth; (ii) establishment of data‐driven reference models, using Bayesian regression about the real and imaginary parts of the SHI derived with monitoring data from the intact rail; and (iii) quantitative evaluation of discrimination between the new observations representative of current rail health condition and the baseline model predictions in terms of Bayes factor. If the deviation of the new observations from the predictions is within an acceptable tolerance, no damage is flagged, and the new data are further used to update and refine the reference models. If the observations deviate substantially from the model predictions in a probabilistic sense, damage is signaled, damage severity is quantified, and damage location determined. The proposed approach is examined by using field monitoring data acquired from an instrumented railway turnout, and the coincidence between the assessment results and the actual health conditions demonstrates its effectiveness in damage detection, localization, and quantification.

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A Markovian–Bayesian Network for Risk Analysis of High Speed and Conventional Railway Lines Integrating Human Errors

Abstract: The article provides a new MarkovianBayesian network model to evaluate the probability of accident associated with the circulation of trains along

Cited by 40 publications

References 27 publications

Optimizing Passenger Flow Control and Bus‐Bridging Service for Commuting Metro Lines

Optimizing Passenger Flow Control and Bus‐Bridging Service for Commuting Metro Lines

A Customized Hybrid Approach to Infrastructure Maintenance Scheduling in Railroad Networks under Variable Productivities

A Bayesian Probabilistic Approach for Acoustic Emission‐Based Rail Condition Assessment

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