Optimising Indian Railways Infrastructure by AI
DOI:
https://doi.org/10.13052/jmm1550-4646.17138Keywords:
Indian railway, signalling, artificial intelligence, signal maintenanceAbstract
The pressure on the Indian railway (IR) networks has increased due to higher demand for mobility and growth in India’s population over past several decades. In order to meet the higher demand, IR has put priority in capacity building by increasing the number of coaches per train, running more trains, and building more tracks. Building more tracks or increasing the number of coaches or increasing the number of trains have the potential to solve the problem with high infrastructure cost. Unfortunately, it also comes with added vulnerability in safety in running the system. IR with its investment of over 5,00,000 Cr is presently struggling to make its Operating ratio (expenditure / earning) below 100 %. During the last 166 years of its operation, much technological input has been made on its Infrastructure, Locomotives and Rolling stock but its Train Control practices have remained Conventional – locally controlled and experience-based. The developments in the area of signal processing, communication systems, and artificial intelligence (AI) etc. have great potential for applications in Indian Railway right from ticketing to movement of trains, maintenance etc. The potential of AI has been felt in different applications like predicting delays, preventive maintenance of tracks and rolling stocks, forecasting algorithm for the railway systems. The use of AI in the operation of IR will improve performance by using clever algorithms with efficient software and hardware. This in turn will provide lower latency with information sharing and the use of AI in rail operation will surely improve the efficiency in train operation. This paper highlights the potential contributions of AI in improving the operation of India’s railway system and how the application of recent technological advancement in Information Science and Artificial Intelligence can bring a change in the train operation scenario at a railway station and Control Centre and add to the profitability of Indian Railways.
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References
Ministry of Railway. Indian Railways Statistical Publications 2016.
Ministry of Railway. Indian Railways Budget Document 2018–19.
Indian Railways general and Subsidiary Rules.
Indian Railways Station Working Rules.
Indian Railways Signal Engineering Manual.
Indian Railways Telecommunication Manual.
G. Raghuram, ‘Turnaround’ of Indian Railways: A Critical Appraisal of Strategies and Processes (PDF), Working Paper No.2007-02-03, Ahmedabad, India: Indian Institute of Management, (February 2007).
G Bocchetti, F Flammini, C Pragliola, and A Pappalardo. Dependable integrated surveillance systems for the physical security of metro railways. In 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), pages 1–7. IEEE, 2009.
R Borndörfer, M Reuther, T Schlechte, K Waas, and S Weider, Integrated optimization of rolling stock rotations for intercity railways. Transportation Science, 50(3):863–877, 2015.
A Caprara, M Fischetti, P Toth, D Vigo, and P Guida. Algorithms for railway crew management, Mathematical programming, 79(1–3):125–141, 1997.
F Flammini, A Gaglione, F Ottello, A. Pappalardo, C Pragliola, and A Tedesco, Towards wireless sensor networks for railway infrastructure monitoring. In Electrical Systems for Aircraft, Railway and Ship Propulsion, pages 1–6. IEEE, 2010.
H Khadilkar. A scalable reinforcement learning algorithm for scheduling railway lines. IEEE Transactions on Intelligent Transportation Systems, 20(2):727–736,
http://www.indianrail.gov.in, “Indian Railway’s zone’s & their divisions with heaquarters” (pdf), Archived (PDF) from the original on 17 April 2012. Retrieved 23 February 2018.
https://www.linkedin.com/pulse/artificial-intelligence-railway-applications-alexandre-pires-pmp- Alexandre Pires, Artificial Intelligence in Railway Applications.
Derel Wust, Using Machine Learning for Safer and More Efficient Train Operations, NVIDIA GTC 2018, San Jose, CA 2018.
Intelligent Infrastructure for Next-Genration Rail Systems – COGNIZANT 20-20 insights:August 2013.
Mingijia Yin, Kang Li, Xiaoqing Cheng, A review on artificial intelligence in high speed rail, Transpotation Safety and Envioronment, 2020, Vol. 0, No. 0, pp. 1–13 (https://doi.org/10.1093/tse/tdaa022).