A Formal Approach to Designing Reliable Advisory Systems

Martin, Luke J. W.; Romanovsky, Alexander

doi:10.1007/978-3-319-45892-2_3

Cited by 2 publications

(2 citation statements)

References 12 publications

(39 reference statements)

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“…The proposed model on overage can achieve over 80% accuracy in predictions within a 60-min horizon [109]. Of course, the joint method with Bayesian Reasoning and Markov model can be used to predict the delay state in different station [110], [111] c: ML Kecman and Goverde [97] proposed a statistical learning method that combines SM and ML methods. The modeling is divided into three steps, namely, least-trimmed squares robust linear regression, regression trees, and random forests.…”

Section: B: Gmmentioning

confidence: 99%

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Wen

Huang

et al. 2019

IEEE Access

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Train dispatching (TD) is at the forefront of all rail operations that transport passengers or goods. Recent technological advances and the explosion of digital data have introduced data-driven methods (DDMs) in rail operations. In this study, DDMs on the TD problem are briefly explored, focusing on relevant studies on delay distribution, delay propagation, and timetable rescheduling. Data-driven TD methods, including statistical methods (SM), graphical models (GM), and machine learning (ML) methods are reviewed. Then, key issues in establishing different data-driven models for the TD problem are addressed. Subsequently, ML methods are considered to be among the most promising DDMs that lead to innovative TD methods, relying on rich data obtained from train operations. This study emphasizes the potentials for designing new alternatives in the three key fields of interest and provides directions for further research on TD. Future research, including the ML-driven TD and intelligent TD, were discussed in this study. INDEX TERMS Data-driven, delay distribution, delay propagation, timetable rescheduling, train dispatching, machine learning.

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Section: B: Gmmentioning

confidence: 99%

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Wen

Huang

et al. 2019

IEEE Access

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“…Additionally, hybrid models are also widely used in train operation modeling. A joint method employing Bayesian reasoning and Markov model can be used to predict the delay state at different stations (Martin, 2016;Martin & Romanovsky, 2016). It has also been used to establish a delay prediction model (Corman & Kecman, 2018;Kecman & Goverde, 2015b) that utilizes robust linear regression, regression tree, and random forest models to predict the train running time and dwell time.…”

Section: Literature Reviewmentioning

confidence: 99%

A predictive model of train delays on a railway line

Wen

Mou

Huang

et al. 2019

Journal of Forecasting

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Delay prediction is an important issue associated with train timetabling and dispatching. Based on real-world operation records, accurate forecasting of delays is of immense significance in train operation and decisions of dispatchers. In this study, we established a model that illustrates the interaction between train delays and their affecting factors via train describer records on a Dutch railway line. Based on the main factors that affect train delay and the time series trend, we determined the independent and dependent variables. A long short-term memory (LSTM) prediction model in which the actual delay time corresponded to the dependent variable was established via Python.Finally, the prediction accuracy of the random forest model and artificial neural network model was compared. The results indicated that the LSTM model outperformed the other two models.

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A Formal Approach to Designing Reliable Advisory Systems

Abstract: This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License Newcastle University ePrints -eprint.ncl.ac.uk

Cited by 2 publications

References 12 publications

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

A predictive model of train delays on a railway line

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