Imperfect rail-track inspection scheduling with zero-inflated miss rates

Altay, Ayça; Baykal‐Gürsoy, Melike

doi:10.1016/j.trc.2022.103608

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…In general, there are some problems in the preprocessing and analysis of text data [5,6]. One of them is the sparsity problem with zero-inflated data [8,10,13,14,31]. For text data analysis using statistics and machine learning algorithms, we have to transform the text documents into a document-word matrix.…”

Section: Related Workmentioning

confidence: 99%

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

Park

Jun

2023

Applied Sciences

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A large part of big data consists of text documents such as papers, patents or articles. To analyze text data, we have to preprocess the text documents and build a structured data based on a document-word matrix using various text mining techniques. This is because statistics and machine learning algorithms used in text analysis require structured train data. The row and column of the matrix are document and word, respectively. The element of the matrix represents the frequency value of the word occurring in each document. In general, because the number of words is much larger than the number of documents, most elements have zero values. Due to the sparsity problem caused by inflated zeros, the performance of the predictive model has decreased. In this paper, we propose a method to solve the sparsity problem and improve the model performance in text data analysis. We perform compound Poisson linear modeling to make the proposed method. To show the performance of our proposed method, we collect and analyze the patent documents from patent databases. In our experimental results, we compared the value of the Akaike information criterion (AIC) of the proposed model with traditional models, such as linear model, generalized linear model and zero-inflated Poisson model. Additionally, we illustrated that the AIC value of our proposed model is smaller than others. Therefore, we verify the validity of this paper.

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Section: Related Workmentioning

confidence: 99%

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

Park

Jun

2023

Applied Sciences

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“…Assume that this random variable is a counting process that obeys Poisson distribution and possesses two states (yes and no). Given that the average number of disruption occurrences per unit time is λ, the probability that disruption occurs n times is [46]:…”

Section: Unknown External Shockmentioning

confidence: 99%

An integrated resilience assessment methodology for emergency response systems based on multi-stage STAMP and dynamic Bayesian networks

Yin²,

Tong

et al. 2023

Reliability Engineering & System Safety

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“…The group of factors that affect the railway maintenance planning processes were presented by Kovenkin and Podverbnyy [32]. The authors in [6] used Bayesian approach to build a model that can expose the actual defect rate and the probability of not locating any defects in order to optimize the railway track maintenance process. A different approach was presented by Movaghar and Mohammadzadeh [38] where the authors utilize Bayesian framework to generate data regarding uncertainties in railway track degradation model, the results presented by the authors reveal how the proposed model can affect the restricted budget and the limited resources for preventive maintenance processes scheduling.…”

Section: Related Studiesmentioning

confidence: 99%

Real time read-frequency optimization for railway monitoring system

2022

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Trains have a key role in transporting people and goods with the option of moving from source to destinations by passing through several stations, with time-based features like date scheduling and known arrival times, which makes time a critical factor. The main challenge here, is to ensure that the train trip or train schedules are not affected or delayed in any way during the whole train trip; by giving the control unit in the railway system, the required time to process requests regarding all collected data. This an NP-hard problem with an optimal solution of handling all collected data and all service requests by the control unit of the railway system. Operational research will be used to solve this problem by developing many heuristics to deal with tasks of real-time systems, to produce a significant time optimization in the railway systems. To solve this problem, the proposed approach employs optimization by adapting 22 heuristics based on two categories of algorithms, the separated blocks category algorithm and the blocks interference category algorithm. The proposed approach receives data from many different sources at the same time, then collects the received data and save it to a data base in the railway system control unit. Experimental results showed the effectiveness of the developed heuristics, more over the proposed approach minimized the maximum completion time that was elapsed in handling the received requests.

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Imperfect rail-track inspection scheduling with zero-inflated miss rates

Cited by 6 publications

References 47 publications

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

Zero-Inflated Patent Data Analysis Using Compound Poisson Models

An integrated resilience assessment methodology for emergency response systems based on multi-stage STAMP and dynamic Bayesian networks

Real time read-frequency optimization for railway monitoring system

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