Artificial Intelligence in Rail

Overview

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.

Rail transport is being looked upon favorably once again 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.

While AI has been of interest for the past 50 years or so, it is finally coming of age as we now have more and increasingly less expensive computing power available to us along with machine learning capabilities and advanced user interfaces that help make this powerful technology more accessible to a wider variety of users.  

AI is helping make 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.

Although the potential benefits are many, AI comes with some real challenges. AI applications raise legal, economic, social and ethical questions, such as who is liable for any misfortune in case of a cyber-attack or how to ensure data protection and transparency.  It also poses a potential threat to citizens and consumers, as it could be used for surveillance purposes.

Automated Train Operations Systems

While AI could be applied to improve efficiencies and reduce costs across a variety of train control, 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 transfers responsibility for managing operations from the driver to the train control system, with varying degrees of autonomy. The International Electrotechnical Commission has established four standard grades of train automation: 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.

Today’s advanced AI technology provides a powerful force to help unlock the potential further digitalization of railways.  As intermodal transport of containers is expected to grow, projects are underway to better synchronize container train movements on the network and improve real-time information and data exchange.   AI can play an important role in this.

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Keywords:Artificial Intelligence (AI), Railways, Technology, Machine Learning (ML), ARC Advisory Group.