CFP last date
20 May 2024
Reseach Article

Machine Learning: Prospects, Opportunities and Benefits to the Greek Railways

by I. Kalathas, M. Papoutsidakis, D. Piromalis, L. Katsinoulas
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 24
Year of Publication: 2019
Authors: I. Kalathas, M. Papoutsidakis, D. Piromalis, L. Katsinoulas
10.5120/ijca2019919038

I. Kalathas, M. Papoutsidakis, D. Piromalis, L. Katsinoulas . Machine Learning: Prospects, Opportunities and Benefits to the Greek Railways. International Journal of Computer Applications. 178, 24 ( Jun 2019), 26-32. DOI=10.5120/ijca2019919038

@article{ 10.5120/ijca2019919038,
author = { I. Kalathas, M. Papoutsidakis, D. Piromalis, L. Katsinoulas },
title = { Machine Learning: Prospects, Opportunities and Benefits to the Greek Railways },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2019 },
volume = { 178 },
number = { 24 },
month = { Jun },
year = { 2019 },
issn = { 0975-8887 },
pages = { 26-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number24/30683-2019919038/ },
doi = { 10.5120/ijca2019919038 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:51:19.599731+05:30
%A I. Kalathas
%A M. Papoutsidakis
%A D. Piromalis
%A L. Katsinoulas
%T Machine Learning: Prospects, Opportunities and Benefits to the Greek Railways
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 24
%P 26-32
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the areas of Artificial Intelligence that inspired a keen interest on scientists, and not only, from the very beginning of its foundation in the 1980s, was the subject of Machine Learning. Machine Learning comprises a field of study quite extensive and of great interest which attracted many researchers. Its objective consists of the construction of programs capable of adapting automatically their operation in order to improve their performance, through the experience gained during their execution. It is about improving computer skills in specific areas, as well as improving human-computer connection and interaction. It can be applied in personal computers, everyday life and in key areas of life such as education, finance and transport. Implementing, planning and designing transport infrastructures and investments is a difficult task, whether it concerns an intergraded intervention or an effort for local improvement. The main advantage of the use of a railway network, is the transportation from one place to another within a minimum time spent. Rail transport infrastructure is part of the development field and is in line with the development of each region. Transport companies are very important to the economy and progress of each country, since they contribute significantly to all the services provided by the state. The services provided to the citizen must constantly be improved and upgraded with the primary objective of the safe transport of passengers and goods. This paper presents the technology of mechanical learning and its contribution to transport, especially on railway networks and railway undertakings. There is a theoretical introduction to mechanical learning and a brief review of emerging technology with a historical retrospection on the major milestones of its course. At the same time, its basic features, methods and algorithms are examined. Innovative applications used in transport, and in particular on railway networks are included. This paper highlights the problems of the Greek railways and records the needs and requirements of the transport companies. Finally, the conclusions and incentives of railway companies for the creation of engineering learning applications to be used as a tool for the development and continuous improvement of the quality of the services provided are presented.

References
  1. M. I. Jordan and T. M. Mitchell, ‘Machine learning: Trends, perspectives, and prospects,’ Science, Vol. 349, Issue No. 6245, pp. 255–260, (2015).
  2. R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Machine learning: An artificial intelligence approach. Published by Springer Science & Business Media, (2013).
  3. 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)
  4. Rajiv Chopra, ‘Machine Learning΄ Published by KHANNA PUPLISHING, (2018).
  5. Stalidis, G., Kardaras, ‘Data Management and Business Intelligence.’ [eBook] Athens: Hellenic Academic Libraries Link. Chapter 3. Available Online at: http://hdl.handle.net/11419/116 (2015).
  6. J.McCarthy, E. Feigenbaum. ‘Arthur Samuel: Pioneer in Machine Learning’, AI Magazine, Vol.11, Issue 3 pp.1 (2018)
  7. Olivier Marteaux, entries: ’Machine Learning and its value proposition for the rail industry’ Retrieved from https://www.rssb.co.uk/Pages/machine-learning-and-its-value-proposition-for-the-rail-industry.aspx
  8. Jagdeep Kaur, ‘Feature selection based efficient machine learning technique for email spam predict’, International Journal of Engineering Applied Sciences and Technology, Vol.2, Issue 12, ISSN No. 2455 – 2143, pp.13-19, (2018)
  9. Aikaterini Fountoulaki ‘Augmenting statistical quality control with machine learning techniques’ University of Patras, PhD (2011)
  10. Ioannis Mademlis, Machine learning and computer vision methods for intelligent video analysis, Aristotle University of Thessaloniki, PhD (2018)
  11. I. 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)
  12. Vlahavas, P. Kefalas, N. Bassiliades, F. Kokkoras, Ι. Sakellariou. ‘Artificial Intelligence’ - 3rd Edition, ISBN: 978-960-8396-64-7 Published by University of Macedonia Press / Greece, (2011).Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  13. Katerina Georgouli, ‘Artificial Intelligence’ Published by SEAV (2015)
  14. Qiu, Q. Wu, G. Ding, Y. Xu, and S. Feng, ‘A survey of machine learning for big data processing,’ EURASIP Journal on Advances in Signal Processing, Vol.67 ,Issue No. 1, pp. 1687-6180 ( 2016)
  15. Julie Chao , ‘Machine Learning to Help Optimize Traffic and Reduce Pollution’ ,Berkeley Institute of Transportation Studies, Retrieved from https://its.berkeley.edu/node/13327
  16. C. Xu, D. Zhang, Z. Zhang, and Z. Feng, ‘BgCut: Automatic Ship Detection from UAV Images,’ Thee Scientific World Journal, Vol. 2014, Issue No. 171978, (2014)
  17. Peter Grindrod , Coralia Cartis ,Christopher Lester ,Andrew Mellor, Stephen Roberts ,International Workshop ‘Network Science meets Matrix Functions’- Emirates Colloquium, Oxford-Emirates Data Science Lab, (2016)
  18. A. Thaduri, D. Galar, and U. Kumar, ‘Railway assets: a potential domain for big data Analytics’ Procedia Computer Science, Vol. 53, pp. 457–467, (2015)
  19. Andy Gosney, Billy Denyer, Robin Foreshew, Rebecca Hackett, International conference: Data Sandbox: Improving Network Performance Competition, Rail Research UK Association (RRUKA) (2017)
  20. C. Turner, A. Tiwari, A. Starr, and K. Blacktop, ‘A review of key planning and scheduling in the rail industry in Europe and UK,’ Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, Vol. 230, Issue No. 3, pp. 984-998(2016)
  21. Bliemer M, Raadsen M, Brederode L, Bell M, Wismans L, and Smith M ‘Genetics of traffic assignment models for strategic transport planning’ Transport Reviews, Vol.37 Issue No 1,pp.56-78.(2017)
  22. Lu Dai ‘A machine learning approach for optimization in railway planning, PhD Delft University of Technology, (2018)
  23. Lindfeld A., ‘Railway capacity analysis: Methods for simulation and evaluation of timetables, delays and infrastructure’ Stockholm: KTH Royal Institute of Technology PhD Thesis (2015)
  24. Hongfei Li, Dhaivat Parikh , Qing He , Buyue Qian , Zhiguo Li , Dongping Fang, Arun Hampapur ‘Improving rail network velocity: A machine learning approach to predictive maintenance’ Transportation Research Part C, Vol 45 pp 17–26 (2014).
Index Terms

Computer Science
Information Sciences

Keywords

Machine learning application railway rolling stock maintenance data knowledge mining