Article from Global Review: 190610
The birth of the microprocessor: A quantum leap
In today’s modern society, computer-based technology can often be taken for granted, yet on 15
November 1971, a simple advertisement1 about the world’s ?rst commercially available
microprocessor started a revolution: The INTEL 4004 and its amazing 2,250-transistor chip
represented a quantum leap for railway signalling.
The Swedish rail industry was quick to adopt the new microprocessor-based equipment and fully
electronic interlockings soon replaced traditional signalling safety technology. Following this positive
development, SJ decided to evaluate the merits of replacing relay interlockings with computer-based
alternatives to signi?cantly reduce the cost and project time involved by installing more compact
The world’s frst Computer-Based Interlocking (CBI) entered revenue operation in May 1978 in
Gothenburg. It replaced 16 conventional interlockings and controlled 1,169 signals and turnouts. The
first Swedish ATP began operation in 1980 and soon became the industry standard.
The late Bengt Sterner, the legendary global signalling expert at SJ, commented on the introduction of
microprocessors in railway control as a “small step for a signal engineer but a giant leap for railway
technology”, paraphrasing astronaut Neil Armstrong.
Sterner and Bombardier were both deeply involved in the transformation of signalling from relay to
electronic technology. Sterner was the driving force behind increasing safety and reliability by
achieving an accurate redundancy in railway signalling electronic systems. By the late-1970s he had
introduced a 3D geographic model that represented the railway network and developed STERNOL,
the fundamental language for programming interlocking algorithms. It was also under his leadership
that the System Requirement Speci?cation (SRS) for the European Train Control System (ETCS) was
Behind the digital hype: The next steps in optimising the flow of trains
Fully understanding rail control processes, operational traffic scenarios and the associated tasks are
essential for suppliers seeking to build the next generation of smart systems.
Pre-test functions have been in use since 1979 to ensure that a desired train path can be made
available. A function that contributes to the accuracy of a train schedule can increase capacity and
optimise traffic flow. These functions can be further refined to match any type of interlocking.
In the near future, interlockings will be replaced by on-board solutions. This evolution is a necessity
and will facilitate the introduction of platooning or virtual coupling as well as the use of Automatic
Train Operation (ATO) in railways.
Additionally, track circuits and axle counters will not be considered detailed enough and will become
obsolete. We are interested in each train’s precise position and footprint and what the train ahead is
doing, speed, braking, accelerating etc., is information which needs to be calculated and transmitted
to the train behind, which implies the need for more onboard intelligence. Hence the next Train
Management Systems (TMS) will be intelligent, once it is fed with footprint information of individual
train performance, it will be able to analyse the mobility within the system and mitigate any possible
situation which may result in a delay.
The technique relying on footprint information is well-known and has been used in Communication
Based Train Control (CBTC) systems for driverless metro applications for several years.
The geographic principal, which corresponds to the network layout and associated objects, used in
CBI and TMS will be maintained as it allows for easy extension of sites. Today these systems are
extended to supervise larger areas, with regional to nationwide adoption.
Nodes in the rail network, such as turnouts, will be operated when required via radio from either the
approaching train or from the train management centre, depending on the required type of train path.
Level crossing activation is also made via radio-based communication on the position and speed of
each individual train.
The railway operates like a machine
A railway network’s operation can be compared to a complex machine, and all components of any
machine are dependent on each other. Problems can easily arise because everything is connected, so
it is important to be able to understand those connections. As we know, delays at one point on a rail
network can easily ripple through to the other end, causing challenges in managing and mitigating
Therefore, to predict delays, networks need smarter algorithms that can crunch larger amounts of
Data can be made available from various sub-systems, such as infrastructure, energy, rolling stock,
trackside control-command and signalling as well as from on-board control-command and signalling,
operation and traf?c management and even maintenance.
Each train’s performance can be tracked as every train in the system is an individual with its own
unique performance, for example different speed, traction force, axle load, weight, train resistance,
acceleration and deceleration capability, but also its driver’s performance. Thanks to this information,
delays can be prevented from spreading across a network by proactively identifying the risk of delays
and improving instructions for alternative meeting points.
In addition, variations in the geometry of lines influences a train’s mobility patterns. The interaction
between wheel and track has a great effect on maintenance intervals and cost for both vehicles and
permanent way. Continuously adjusting a train’s maximum speed is key to conserve energy, for
example optimising speed in curves and continuously adjusting it to negotiating changes in radius
and can improve passenger comfort while reducing total maintenance cost.
Furthermore, using data from track sensors to adjust/modify permitted speed and ATP-braking
capacity, based on rail friction coefficients and vibrations created in the track, subgrade and/or
structures such as bridges, is another way to open new opportunities.
Challenges and possibilities
The hardware required to conceive applications for these systems is at the very early stages of
development. Ongoing research will bring greater possibilities for delivering these digital solutions.
There is also a shortage of skilled engineers and developers available to the rail industry. People who
are able to conceive these applications are not common as designing these systems requires
individuals with a solid understanding of rail processes and rail control, and how to use it to create
more value for the customer. Competition with other industries vying for skilled engineers is a
challenge for rail.
Signalling systems rely on a lot of information and data. As rail networks expand in size and
complexity, an ever-increasing amount of data needs to be computed. Arti?cial intelligence techniques
can learn how to process more data while algorithms can predict when a locomotive or a signalling
subsystem will require maintenance.
In most cases, closed systems are preferred but data can be retrieved from outside sources if
necessary, which may create cyber-security issues as transmission networks and storage are not
always 100 per cent secure. For example, GPS systems are notoriously easy to disrupt. Techniques for
securely migrating rail network business to the Cloud may not be safe enough. We must be certain that any software being introduced across various subsystems does not include hidden vulnerabilities
that can be remotely activated by malicious third parties. Our customers must always maintain and
be in complete control of their train’s movements.
The objective is to build smarter solutions for better travel time precision, energy savings, lower
maintenance cost, increased speeds as well as for improved line capacity and headway.
The ongoing digitalisation
SWEDTRAIN, the association for Rail Industry Companies in Sweden, is fostering the legacy of the
Swedish railway by advocating the evolution of rail control systems in order to be a system that
increases capacity and ef?ciency while at the same time optimising resources to achieve improved
punctuality and a reduced environmental footprint. The final goal is a system which is built for
international implementation from a new generation of signalling products, which can then be used
on heavy traffic mainlines with major stations as well as on single lines outside the main corridors.
Artificial intelligence, smart communication, IoT and thinking outside-the-box will empower us to
conceive and operate sustainable rail transportation networks for many generations to come.
1. Published in Electronic News magazine