Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization
Volume 5, Issue 4, Page No 287-295, 2020
Author’s Name: Ilias Kalathasa), Michail Papoutsidakis, Chistos Drosos
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Department of Industrial Design and Production Engineering, University of West Attica, Athens, 15354, Greece
a)Author to whom correspondence should be addressed. E-mail: i.kalathas@uniwa.gr
Adv. Sci. Technol. Eng. Syst. J. 5(4), 287-295 (2020); 
DOI: 10.25046/aj050435
Keywords: Machine learning, Railway, Train Immobilization, Data mining, Predictive Model, Malfunctions, Diagnosis
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Information is the driving force of businesses because it can ensure the ability of knowledge and prediction. The railway industry produces a huge volume of data, with the proper processing of them and the use of innovative technology, there is the possibility of beneficial information to be provided which constitute the deciding factor for the correct decision making. Safety is the railway comparative advantage that has to be reinforced by each business administration while making the optimum decisions. The main purpose of this paper is the investigation of the most important dysfunctions that arise in a train and can cause its immobilization at the main passenger rail, resulting in huge delays of conducting the routes setting the passengers at risk. Afterwards the total of malfunctions is assessed and the most important, potentially, malfunction is assessed, so as the executives of the Greek Railway company to plan and redefine the processes and the initial plan of the predictive maintenance. This paper demonstrates the effort of implementing innovative applications by making use of methods from the rapidly developed field of Data Mining to the Greek Railway Company that uses obsolete procedures for the control of the trains’ functionality in order to investigate the data for the provision of specialized information which will be used as a tool for the faster, more accurate and precise decision making. This decision making approach is based on a specific algorithm’s design in order to automatically detect faults and make periodic maintenance of trains easier. Holistic approach is performed in the management of real data from the Greek railway industry and a predictive model of Machine Learning is developed, for the optimization of the management’s performance of the trains reinforcing the strategic target of the railway industry which is the transportation of citizens with safety and comfort.
Received: 17 June 2020, Accepted: 19 July 2020, Published Online: 28 July 2020
- Weske M. Business Process Modelling Foundation. In: Business Process Management. Published by Springer, 2019
- Aida-Maria P. ‘Business Intelligence Methods for Sustainable Development of the Railways’ Database Systems Journal vol. VI, Issue No. 2,2015.https://EconPapers.repec.org/RePEc:aes:dbjour:v:6:y:2015:i:23:p:48-55
- Kyrkos, E. ‘Business intelligence and data mining’ [eBook] Athens: Hellenic Academic Libraries Link. Chapter 4. Available Online at: http://hdl.handle.net/11419/1231,2015
- Lu Dai ‘A machine learning approach for optimization in railway planning, PhD Delft University of Technology, 2018
- Petri, M., Pratelli, A., & Fusco, G. ‘Data Mining and Big Freight Transport Database Analysis and Forecasting Capabilities’. Transactions on Maritimes Science, 2016. https://doi.org/10.7225/toms.v05.n02.001
- Wagenaar, J.C., Kroon, L.G., Schmidt, M. ‘Maintenance Appointments in Railway Rolling Stock Rescheduling’. Transportation Science, vol. 51 Issue 4 pp. 1138-1160., 2017. https://doi.org/10.1287/trsc.2016.0701
- Ronanki, S. A. Singh and S. S. Williamson, ‘Comprehensive Topological Overview of Rolling Stock Architectures and Recent Trends in Electric Railway Traction Systems,’ in IEEE Transactions on Transportation Electrification, vol. 3, no. 3, pp. 724-738, 2017. https://doi.org/ 10.1109/TTE.2017.2765518
- Edwin Bosshca, ‘Big Data in railway operation: using artificial neural networks to predict train delay propagation’, University of Twente, PhD Thesis, 2016
- Öztürk, Güner G., Tümer E. ‘The Root Causes of a Train Accident: Lac-Mégantic Rail Disaster’ Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). Advances in Intelligent Systems and Computing, vol 823. Published by Springer, 2018. https://doi.org/1007/978-3-319-96074-6_21
- Heidarysafa, K. Koesari, L. E. Barnes, Brown ‘Analysis of Railway Accidents-Narratives using Deep Learning’ IEEE International Conference on Machine Learning and Application., 2018. https://doi.org/. 10.1109/ICMLA.2018.00235
- JudithHurwitz, Daniel Kirsch ”Machine learning” IBM Limited Edition, Published by John Wiley & Sons, Inc., 2018.
- Devi, G.R. Karpagam, B. Vinoth Kumar, ‘A survey of machine learning techniques, Int. J. of Computational Systems Engineering Vol. 3,Issue No.4 pp. 203 – 212,2017. https://doi.org/10.1504/IJCSYSE.2017.089191
- Ravindra Changala, D.Rajeswara Rao, T Janardhana[ Rao, P Kiran Kumar, Kareemunnisa ‘Knowledge Discovery Process: The Next Step for Knowledge Search’ International Journal of Innovative Research in Computer and Communication Engineering Vol. 3, Issue 5, 2015. https://doi.org/10.15680/ijircce.2015.0305127
- Subhashini Sailesh Bhaskaran ‘An Investigation into the Knowledge Discovery and Data Mining (KDDM) process to generate course taking pattern characterized by contextual factors of students in Higher Education Institution (HEI) , PhD Thesis, Brunel University, London,2017
- Song, Yan-Yan, and Ying Lu. “Decision tree methods: applications for classification and prediction.” Shanghai archives of psychiatry vol. 27, Issue 2, 2015. https://doi.org/ 11919/j.issn.1002-0829.215044
- Ibomoiye Domor Mienyea, Yanxia Suna, Zenghui Wang ‘Prediction performance of improved decision tree-based algorithms: a review’ 2nd International Conference on Sustainable Materials Processing and Manufacturing, Published by Elsevier B.V.,2019. https://doi.org/ 1016/j.promfg.2019.06.011
- Batra M., Agrawal R. ‘Comparative Analysis of Decision Tree Algorithms. In: Panigrahi B., Hoda M., Sharma V., Goel S. (Eds) Nature Inspired Computing. Advances in Intelligent Systems and Computing, vol 652. Springer, Singapore,2018. https://doi.org/10.1007/978-981-10-6747-1_4
- S B Begenova, T V Avdeenko, ‘Building of fuzzy decision trees using ID3 algorithm’: Journal of Physics: International Conference Information Technologies in Business and Industry, 2018. https://doi.org/ 10.1088/1742-6596/1015/2/022002
- Anis Cherfi, Kaouther Nouira & Ahmed Ferchichi ‘ Very Fast C4.5 Decision Tree Algorithm’, Applied Artificial Intelligence, Vol. 32, Issue 2, 2018. https://doi.org/10.1080/08839514.2018.1447479
- Eshwari Girish Kulkarni and Raj B Kulkarni. Article: Weka Powerful Tool in Data Mining. IJCA Proceedings on National Seminar on Recent Trends in Data RTDM volume 2 pp.10-15, 2016.
- S Akinola, O. Oyabugbe, ‘Accuracies and Training Times of Data Mining Classification Algorithms: An Empirical Comparative Study’. Journal of Software Engineering and Applications, 8, 470-477, 2015. https://doi.org/ 4236/jsea.2015.89045
- Felipe Bravo Márquez ‘Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis’, University of Waikato, PhD Thesis, 2017
- Škegro, Frane and Zoroja, Jovana and Šimičević, Vanja, Credit Scoring Analysis: Case Study of Using Weka (September 7, 2017). 2017 ENTRENOVA Conference Proceedings, Available at SSRN: https://ssrn.com/abstract=3282504
- Lee, Jonguk et al. “Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis.” Sensors (Basel, Switzerland) vol. 16, Issue No 4, 549. 16,2016. https://doi.org/10.3390/s16040549
- Faisal Mohammed Nafie Ali & Abdelmoneim Ali Mohamed Hamed ‘Usage Apriori and clustering algorithms in WEKA tools to mining dataset of traffic accidents’, Journal of Information and Telecommunication, vol 2 Issue No 3, pp 231-245, 2018. https://doi.org/10.1080/24751839.2018.1448205
- Jiajia Li, Jie He*, Ziyang Liu, Hao Zhang , Chen Zhang MATEC Web of Conferences 272, 01035 ‘Traffic accident analysis based on C4.5 algorithm in WEKA’ School of Transportation, Southeast University, Nanjing 211189, Jiangsu,China,2019. https://doi.org/10.1051/matecconf/201927201035
- Tsaganos G, Papachristos D, Nikitakos D, Dalaklis D, A.I. Ölçer ‘Fault Detection and Diagnosis of Two-Stroke Low-Speed Marine Engine with Machine Learning Algorithms’ Conference: 3rd International Naval Architecture and Maritime SymposiumAt: Istanbul, Turkey,2018. https://www.researchgate.net/publication/324835430
- J. Morales, A. Reyes, N. Caceres, L. Romero, F. G. Benitez ‘ Automatic Prediction of Maintenance Intervention Types in Roads using Machine Learning and Historical Records’ Transportation Research Record Journal of the Transportation Research Board, Vol 2672, Issue 44, 2018. https://doi.org/10.1177/0361198118790624
- Khaksar , A. Sheikholeslami , ‘Airline delay prediction by machine learning algorithms’ International Journal of Science and Technology, Volume 26, Issue ?? 5, 2019. https://doi.org/ 10.24200/SCI.2017.20020
- Ahlgrenn Fredrik, ‘Reducing ships’ fuel consumption and emissions by learning from data’ Linnaeus University, PhD Dissertation, 2018.
- Barmpounakis N. Emmanouil ‘Investigating the decision-making process of drivers during overtaking by Powered Two Wheelers’ National Technical University of Athens ,Department of Transportation Planning & Engineering PhD Dissertation, 2017.
- Asha Kiranmai, S., Jaya Laxmi, A. Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy. Prot Control Mod Power Syst 3, 29 2018. https://doi.org/10.1186/s41601-018-0103-3
- J Shen, O Lederman, J Cao, F Berg, S Tang, A Pentland, ”Gina: Group gender identification using privacy-sensitive audio data”, IEEE International Conference on Data Mining (ICDM), 457-466 2018. https://doi.org/ 1109/ICDM.2018.00061