Introduction: Implementation of AI in the Railway Infrastructure(s).
The objective is to understand the implications of AI in transport via railway.
Further concern being the prevention of accidents & mechanical failures.
Relevant references to mechanisms have been made to gain a grasp on the practical aspects.
A doctrinal approach is taken to achieve the aforementioned objectives.
Artificial intelligence (AI) is having a profound effect on our personal and professional lives alike, including across the transportation sector. In particular, powerful new AI-enabled applications are helping boost and revive the global rail industry, which has suffered for decades due to lack of innovation. Railway sector is not any different from such innovation. This has led to Railways being again looked in a favourable manner due to its environmental friendliness, efficiency, and cost-competitiveness relative to other modes of transport. ARC Advisory Group expects to see the digitalization of the railways happening at a rapid pace. However, for railways to reap the full benefits of digitalization, it must expand its use of AI across both rail operations and rail infrastructure. A.I has been in transportation sector for past 50 years however it has come to a wide use in railways in pass few years and in the transportation sector as a whole it has made all transport modes safer, cleaner, smarter, and more reliable. AI can help reduce traffic congestion, identify risks, manage transport, analyze travel demands, and even reduce greenhouse gas emissions. Today, we’re seeing AI being used in rail applications to improve train scheduling, manage train speeds, avoid accidents, predict delays, enhance asset management, and more. These AI applications help ensure public safety, deliver customer value, and optimize overall rail management and operations. In this manner, the technology is helping reverse the trend for rail transport to lose market share to other modes of transportation. Artificial Intelligence is being applied to improve efficiencies and reduce costs across a variety of train control system, safety, supervision, and asset management systems, one of the effective examples of uses of AI in rail technology is its contribution to the automation of train operation (ATO). ATO is a technology used in train which is used to transfers responsibility of managing operations from the driver of train to the Artificial intelligence to control the trainsystem, these system has varying degrees of autonomy which is similar to different autonomy in artificial intelligence driven car.
The International Electrotechnical Commission has established four standard grades of train automation:
First one is where the artificial intelligence in train only advises the driver about any defect or detachment of rails
The second one is where the Artificial intelligence is used to close the door of the train and has all the features of first one.
The third grade corresponds to driverless operations (with crew members present on board) and
The fourth grade to autonomous and unattended train operations. These examples are already visible in light rail and urban transit systems.
The Dubai International Airport operates a fully autonomous train to transfer passengers from one terminal to another in a defined track system. It uses SelTrac, an automatic railway signaling technology to control the train autonomously. The Singapore Mass Rapid Transit Lines, currently the world’s longest automated metro system, is another example. This urban transit system is at fourth-grade automation. In the EU, the first key step towards the introduction of ATO and AI solutions in rail transport is the deployment of the European Rail Traffic Management System (ERTMS), which provides trains with a driver assistance system.
In India also the Artificial intelligence in provided for railways by a company called Gaia which provides A real-time Onboard Housekeeping Service (OBHS) management that not only manages people onboard but also manages operation processes. The system collects data via IoT sensors as well as users’ devices in real-time, with time and location stamps to ensure data efficacy and accountability of the entire working in Indian Railways. The IoT devices are paired with sensors to enable last-mile monitoring of parameters such as temperature, pressure, humidity, power usage, faults, proximity, footfall, and other metrics to understand site and asset performance in real-time. It then uses AI algorithms for allocation, optimisation, and task orchestration. A comprehensive action and workflow engine complement the platform for managing alerts and incidents, triggering workflows, and enabling closed-loop response management. The solution is a multi-tenanted platform that allows role-based access for users – from line staff and managers to senior management and board-level executives – on a unified platform. These kinds of technology are most use in the Tejas express which runs in India. Similar kind of technology was being to be used in Delhi metro to make it driverless however due to an human error the Delhi metro crashed in the wall just after the a station.
From the above incidence it is clear that though Artificial intelligence is being used to make our work easy however there are still a lot of improvement that has to happen such as to easily distinguish between a human and an unidentical item so as to prevent what has happened in the uber driver case where a car thought women was unidentified object and ran over her and taking away her life secondly there should be a training program to make sure that the user of the transport knows how to operate a vehicle so as to prevent the accident that happened with Boeing 737 Max and lastly there should be awareness among humans to prevent the king of accident that happened with Delhi metro if these things are taken into account then Artificial Intelligence would be one of the most useful thing which can make our life much more easier than it is today however it would take almost a decade or two to reach that perfection level.
Name of Author: Ansh Kumar.
Date of Publication: 08 April, 2021.
Indicative Code: ILAIL-0000-CS-08-04-2021-00006
Style: Typical; Influential; Most Similar;
 Artificial Intelligence in Rail, WASAY RASHID, ARC Advisory Group, ( Mar. 26, 2021, 22:40) https://www.arcweb.com/blog/artificial-intelligence-rail#:~:text=AI%20can%20help%20reduce%20traffic,enhance%20asset%20management%2C%20and%20more.
 Supra at 36  Supra at 36  Supra at 36  Supra at 36  Supra at 36  Supra at 30  How Indian Railways Uses AI: A Comprehensive Case Study, Sejuti Das, Analytics India mag, (Mar. 26, 2021, 23:07) https://analyticsindiamag.com/how-indian-railways-uses-ai-a-comprehensive-case-study/  Ibid  Supra at 46  Delhi Metro's Driverless Magenta Line Train Crashes Days Before Launch By PM Modi, Mukesh Singh Sengar, NDTV India, (Mar. 26, 2021, 23:12) https://www.ndtv.com/india-news/delhi-metros-magenta-line-crashes-into-wall-during-test-run-1789709