Artificial IntelligenceTechnology

How the Railway Industry is Using AI to Tackle Long Wait Lists and Improve Passenger Experience & Safety?


There is no longer a way around artificial intelligence (AI) and machine learning. Particularly so in the rail industry. The challenges in rail operations are increasing. Growing number of passengers, tight schedules, complex route networks, and the coexistence of passenger and freight traffic side by side.

At the same time, there is a growing demand for safety, expertise, capacity, sustainability, and comfort. Artificial intelligence (AI) with its subdivisions of machine learning and inference currently offers the greatest leverage to meet these challenges. This is much more than automating processes.

AI is much more capable of raising the safety, expertise, efficiency, and flexibility of rail operations to an unprecedented level. So, today in this blog, we will explain how the railway industry is using artificial intelligence to tackle long waiting lists and improve passenger safety with an amazing experience.

A glimpse of Interesting Stats on AI Revolutionizing Railway Industry

  • By 2026, the US AI market is expected to be worth $299.64 billion.
  • According to reports, global public bus and commuter rail services will be worth more than $400 billion by 2025 compared to $285 billion in 2021.
  • The value of the AI industry is projected to increase more than 13 times in the next 7 years.
  • The AI market is expanding at a CAGR of 38.1% between 2022 and 2030.

Artificial Intelligence in the Railway Industry

For the railway industry, managing all aspects: of operation, maintenance, scheduling, repair, or supervision has always been a difficult task for the railway industry. The task is difficult due to the problem of integration of various systems such as signaling, telecommunications, operation, rolling stock, electrical, information technology, traffic, infrastructure, etc., and the involvement of human factors.

One way to try to meet the demand is to improve the rail infrastructure. Infrastructure has been the key feature of current projects introducing a bullet train. But the question is why overlook the increase in performance to meet the growing demand by incorporating artificial intelligence, machine learning, and self-monitoring systems?

Artificial Intelligence in the Railway Industry

These systems are designed to improve the reliability of existing infrastructure and offset the heavy one-time investment. The large investment can eliminate the need for human interference and provide the required level of security and acceleration of gradation. If we can make the software work better, we should be able to do more with the hardware. Having a more efficient software solution includes more information sharing, lower latency, and smart algorithms. That is why many OEMs and startups are now investigating the feasibility of AI-based services around the world.

Now, let’s look at how AI helps the rail industry to improve the experience and safety of passengers. 

AI Helps Railway Improve Passenger Experience & Safety – Understand in Depth

AI In Pre-boarding Experience

There has been a lot written about booking scams, but less about the difficulties travelers face when planning their trips. Now that we think about it, the train reservation process requires you to be educated and familiar with technology, something that most of the population does not have.

For starters, NLP can be used to make sure people can book their tickets by speaking their language over the phone. Given the number of regional languages, dialects, and the strong influence of the mother tongue, it may sound ambitious; but that’s where the AI helps.

As a digitized booking platform, the railway website and app can do much more. For instance, depending on the purpose of the trip, the final destination, the probability of delay, and other factors, railways may recommend the best possible train to the user during booking.

Another aspect of the pre-boarding experience that causes a lot of inconvenience for seniors is the boarding platform. If a higher percentage of elderly people or pregnant women make a trip, the boarding of the train must be on a platform that is easily accessible for said people. AI can decide the arrival/departure platform, based on the passenger data of various trains arriving or leaving a station at a certain time.

Additionally, AI can assist in better platform crowd management. It can notify authorities if the crowd exceeds the number of tickets issued for the ride and the platform by using image recognition. After that, appropriate actions can be taken.

Onboarding Experience

Despite the view outside the window, the peddlers on the platform, and other nostalgic references, train travel bring with it its own share of anxieties and inconveniences.

The most common issues include berth assignments on different coaches, missed destinations early in the morning, and of course, security concerns arising from ticketless travelers claiming their entitlement to their berth.

While the railways are implementing some solutions like destination alerts. With AI at work, most of these issues can be accurately diagnosed and patterns can be established. Some problems of systemic and cultural origin, such as unauthorized travelers and street vendors entering the train, seem intractable. However, AI could help figure out patterns and handle the problem if it doesn’t completely solve it.

Onboarding Experience

For instance, Image recognition systems can be utilized to identify source stations and calculate the lost revenue that results. Supported by facial recognition evidence, the amount of punishment/penalty for traveling without a ticket may be disproportionately increased in select segments to deter such behavior.

AI In Post-Deboarding Experience

First-time visitors’ experience at major stations can be fraught with confusion, chaos, and harassment from taxi drivers. The rail industry has tons of user data, it can implement AI to ensure passengers reach their final destination comfortably after disembarking. Booking a taxi and a hotel is outdated. By knowing the age, the final destination of passengers, and the amount of luggage, the app can automate post-trip arrangements, with little or no human effort, for a price included in the ticket.

Various services can be provided to the user upon request, such as the assignment of porters for luggage, and the reservation of transport through the metro or taxis, based on the information of your reservation. Post-disembarkation services not only make travel convenient for passengers but also add revenue channels to rail.

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How AI Tools Revolutionize the Railway Industry?

In this section, we will explain the ways in which AI tools are configured to help revolutionize the rail industry.


AI-embedded networks can act as intrusion alert systems and if someone other than authorized personnel accesses a restricted area, the system could alert life security teams or deny access/act as a tripwire to prevent hackers.

AI Tools Revolutionize the Railway Industry

Monitoring of Restricted Areas and Access Control

AI tools can not only track the number of people visiting a rail facility, but also those entering, existing in, or loitering in restricted access areas.

For example, railway control rooms, maintenance and storage areas, or project sites under construction can be effectively monitored 24/7 through automated CCTV integrated with analytics. of AI. The cameras work with facial identification and vehicle license plate identification parameters to identify whether a person or vehicle entering/exiting facilities or restricted areas is authorized or not.

Furthermore, not only, it would also have the ability to identify and alert via integrated PAS or report suspicious loitering persons or illegally parked vehicles.

Tunnel Security

Tunnels play a critical role in driving railways through mountainous terrain and, if not properly secured, can also be a safety risk. That is where the AI surveillance helps to identify if there are people in the tunnel, their exact location and the movement of the train, etc.

For example, the system can track the speed and direction of train movement inside tunnels and report if the train moves above or below prescribed speeds or stops inside tunnels.

It can also identify if there is a theft or theft of objects that could jeopardize the safety of train movements in the area under surveillance. Video cameras are able to identify an occurrence of smoke or fire and notify security personnel in order to stop the fire from growing out of control.

Manipulation of CCTV systems

AI integration with CCTV systems can improve their surveillance capabilities on several levels and also allows them to self-diagnose and report an operational problem or attempt system moderation. 

For example, AI-embedded video analytics could report if the video signal from a camera is lost, the view is blocked, the angle of view is changed, or the camera is out of focus/blurred, causing an interruption in the video. Therefore, technicians and security teams can actively respond to such issues and quickly rectify them.

Manipulation of CCTV systems

How is Artificial Intelligence Transforming the Indian Railway?

According to a case study, with Gaia’s SmartFeedback-based CleanRail solution, Indian Railways has seen a massive transformation to the existing system. Some of the key achievements were: Gaining real-time OBHS data collection that improved overall operations accountability, along with introducing big data analytics and cognitive AI facial recognition and GIS dashboards that delivered real-time insights.

This, in turn, improved service assurance with a timely response to passenger complaints based on alerts and status updates. Indian Railways has seen a 100% increase in ecosystem digitization, accountability, and transparency, among other things. Performance against SLAs has increased by approximately 30-50% thanks to the availability of real-time data and robust alerts. 

Additionally, event or incident alerts that provide location insights and intelligence allow staff to have early warning signals about site performance, assets, and the people they manage for faster response.

AI Implementation in the Railway Industry – Common Challenges

Artificial Intelligence seems to be a powerful cutting-edge solution for almost all areas of railway systems. However, the technology presents a number of obstacles that must be overcome during the planning phase:

AI Implementation in the Railway Industry

1) Easy Penetration

The techniques developed must be usable in all instances of the problem, without the need for extensive retraining. A scheduling algorithm developed for one part of the rail network should be used elsewhere.

2) Cost and Complexity

Modifying existing systems to IT-based subsystems will require careful planning and enormous cost.

3) Variability

Not all problem instances contain the same number of inputs and decisions. Many algorithms under the AI umbrella handle only a fixed input-output size. Careful design can resolve the discrepancy between the raw methodology and the domain constraints. But this requires time and effort.

4) Observance of Procedures, Restrictions, and Operating Rules

Real-world systems and national standards must provide the data requirements to build any methodology. They must enable AI-based measurements and avoid errors in those calculations. The need will be to meet the integration and connectivity requirements within the allowed limits of transmission latency.

5) Interoperability

With big data, sharing will come with the need for closer collaboration. This includes operations, communications, and data integration between different OEMs. Therefore, the different AI-based solutions need to be integrated into a cohesive framework.

6) Need to Detail

Black box approaches are acceptable for studies and proof of concepts. But they are not feasible for safety-critical railway applications. Instead, machine learning approaches in the form of decision trees or neural networks with small input and output sizes will be required.

Here We Come in The Picture 

The rail industry has seen a 100% increase in digitization, accountability, and transparency of the ecosystem, among other things. Performance against SLAs has increased by approximately 30-50% thanks to the availability of real-time data and robust alerts. Above we explained how the rail industry is using AI to tackle long waiting lists and improve the experience and safety of its passengers.

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