Introduction
AI is a term that’s been all over the Internet and is hardly unheard of. It shouldn’t come as a surprise if your company or organization talks about using AI for functional operations. The fear of AI taking away jobs is one of the favourite topics that’s been looming over the internet. But do you think, it’s that easy for a machine language to take up such responsibilities? Frankly speaking, they can be very well trained to do human tasks, with utmost precision and accuracy, devoid of human errors, and providing exceptional solutions. The very essence of living beings is the ability to make mistakes, and it’s not a bad thing. But sometimes we do have to set the extent to which we should accept this error. As they can lead to hazardous consequences, that will eventually cost lives.
AI for Smarter Future
This is where AI and Machine Language models are being kept at the frontline with confidence for guidance and solutions. We are going to cover the importance of adopting AI for safety and reliability in Rail Operations. Railways are one of the most widely distributed networks around the globe. It’s natural for management operations of such vast entities to rely on smarter technologies as more and more rail networks are being adopted. Even though the railways have been keeping up with efficient operations up until now, opting for better and smarter technologies will only lead to faster growth, with fewer failures. With time, we have only seen growth in terms of technology, and AI, being in its infancy, has already won over the world, with its exceptional intelligence.
DATA and Models
Well, there’s no place where AI can’t work. It is literally designed to take up human tasks and make them better. We are relying on machine learning models, AI, and neuro-linguistic programming because they are designed to work like a human mind and produce multiple solutions based on data. One of the most important aspects of any computational model is its understanding of data. Be it old or real-time data, AI models are encoded to perform analytical functions and predict plausible solutions. In the Railway sector, every part of its operation works on data. Data like the number of passengers traveling on a day-to-day basis, on which train, from which station, the peak travel timings, seasonal variations, etc. This data concerns just about the passengers. Now imagine relating all these data, and every component would lead to humongous files of data collection. The more we progress, the more data is being collected, and due to human errors and our limitations, we can possibly miss out on some of the important pain points. But with AI and other neuro models, this error can be reduced without the worry of data being neglected. In fact, AI models are so well logically structured, that very few outliers may be neglected. These models and techniques represent themselves as powerful tools for making predictions, analyzing data, and identifying outliers across railway sectors. They leverage advanced statistical and machine learning methods to provide strong and reliable insights from complex datasets.
How is AI Going to Blend in Rail Operations?
When it comes to safety in rail operations, it is very important for us to consider it as the top priority.
As we have already seen the extensive collection of data, now we need to figure out what to do with this data. AI models can help to efficiently analyze and predict solutions that are safer and reliable to adopt. Railway infrastructure like tracks, stations, maintenance depots, signaling systems – traffic control, interlocking systems, etc, communications, power supply chains, bridges, and more, needs a regular civil inspection to check for their optimum functions. Similarly, the railway’s heart, its rolling stocks, which includes locomotives (engines), passenger trains, and freight wagons, needs to be in ideal working condition to operate. All these structural and technical operations are going on simultaneously, and minor breakdowns can happen anytime.
Predictive Maintainance
With AI, predictive models are built. These models or programs are responsible for analyzing the regular pain points in the railway operations, and predicting any anomalies that might occur due to failures or damages in the rail assets or the management depots. Implementing AI-powered predictive maintenance systems analyzes data from sensors installed on trains and tracks. Thus reducing unplanned downtimes, and preventing any accidents that might occur due to equipment failures. Since there’s timely awareness about the damages to rail assets by the AI models, maintenance teams can effectively resolve potential issues with brakes, tracks, tunnels, bogies, wheels, etc, thus helping extend the lifespan of rail assets.
Automated Train Operations
As we automate rail operations like acceleration control, braking systems, track interlocks, etc, with AI models, we are heading in the direction of precision and safety. Another benefit of automation is the reduction in human errors and adherence to consistent scheduling, which leads to enhanced operational efficiency.
Real-time Monitoring and Passenger Safety
AI-based surveillance system deployment receives data from various rail sources, helping to monitor tracks. They also detect any foreign objects or trespassers, thus ensuring timely interventions. Since AI models are working on real-time data, we can get early warnings to signal failures, unusual vibrations, and track or communication failures. AI has also been deployed for video analytics and facial recognition in real-time CCTV footage, to detect suspicious behavior, unattended items of baggage, and overcrowding, assisting in crowd management. With AI, officials can enhance their security measures and safely rely on them for accurate prompts.
Schedules and Traffic Loads
AI-driven traffic management models can be implemented to dynamically regulate train schedules and traffic flow based on real-time data. This will help to address delays and optimize track usage, hence minimizing traffic congestions we face, and improving the overall efficiency of train schedules.
The uses of AI models for operational functions are not limited but have far more applications, the more we think about it. We can use AI to build further models for solutions and predictions, that will help analyze the best energy-efficient ways for railways, which will help minimize environmental impact and improve sustainability. AI is also praised for quicker decision-making and coordination during incidents or accidents. They provide highly effective results concerning evacuation procedures, and dispatching emergency services for medical aid. The connection of AI with the Internet is commendable, especially for real-time data, which the management can use to upgrade the travel experience of passengers. The wide use of AI in the current scenarios can be observed in mobile apps and chatbots, which keep the passenger updated with train schedules, stations, routes, etc. All these developments are implemented to provide passengers with better travel journeys, all the while keeping in mind their safety. Hence relying on AI for safety measures is a way forward to walk along the present times, and adopt newer and smarter technologies for our growing demands.
Case Studies – GajRaj System and Eastern Railways Wheel Prediction System
The rail tracks are laid passing many forest areas and elephant fatalities due to train collisions were occurring frequently. To curb this, IR came up with an AI-based ‘Intrusion Detection System (IDS)’ solution. This 700 km system is laid in Assam, West Bengal, Orissa, parts of Chhattisgarh, and related regions with frequent movements of elephants. The OFC (Optical Fiber Cable) laid in association with the telecom tracks of IR, is capable of detecting the vibration of elephants within 5 meters of cable. As soon as the system detects a movement, it immediately alerts the loco pilot and other associated officials. On average 41 alerts have been received on a daily basis since the commencement of the system. And no fatalities have been reported since its installation.
Another visible upgrade in rail operations can be observed in the Eastern Railways initiative in opting for AI-based Wheel Prediction System for Locomotives for enhanced passenger Safety. CPRO Kaushik Mitra said, ‘This Software carefully monitors wheel dimensions and revolutionizes maintenance practices with reduced human error.’ The cloud-based AI system is made with Google Sheets, making it easy to input data from any rail personnel. The software can then make an analysis and predict the probable timeframe of wheel maintenance. The system is designed to streamline monitoring rail operations for enhanced efficiency, which will help in reducing manual errors and unnecessary expenditure.
AI Regulations
Like every product or service needs to keep up with safety-reliant measures, AI and other technologies are a bit difficult. Since AI is relatively new, we still have a long way to go in creating an environment for its safer use. The Internet is vast and information is immense, so do the dark lurkers, who can use this technology for the wrong purpose. And we have seen the dark usage of AI and deep fake models. So the government and higher regulating bodies should keep a stringent eye and measures on the safe usability of technologies like AI to not misuse the data against humanity.
Conclusion
In rail operations, we should definitely opt for AI models for safety, because human errors are inevitable. So, AI presents a valid and logical solution to our problem, which is not only cost-reliant but also a smarter technology. But we should take very careful steps about their usage. It being an infant technology, we are still unsure of the capabilities it holds, so moving carefully with AI and the Internet of Things would be a smart move.
