Transforming rail freight through digitalisation and automation

At the crossroads of Europe’s twin goals of reducing carbon emissions and increasing competitiveness is rail freight. That’s because, according to the European Environment Agency, a freight train emits an average of 24 grams of greenhouse gases per ton transported and kilometre travelled – less than one-fifth the amount produced by road freight.

Knowing this, the EU has set the ambitious target of doubling the use of rail freight (compared to 2005) – shifting 30% of road freight over 300 km to such modes of transport as rail by 2030 and more than 50% by 2050.

However, delivering these targets first requires positioning rail freight as a cost-effective and attractive service option to shippers.

Enter Europe’s Rail.

With the goal of initiating a ‘technological awakening’ for rail freight transport, the initiative conducted several technical demonstrators (TD), including on:

1. Fleet digitalisation and automation
2. Digital transport management

Through automation, digitalisation, interoperability, energy efficiency, innovation, and infrastructure, these TDs look to make rail freight a more attractive and sustainable transport option.

Fleet digitisation and automation

With the aim of improving strategic areas of rail freight transport, this TD developed such key concepts as a digital automatic coupler and condition-based maintenance, both of which will help enable a digital and automated rail freight system.

Digital automatic coupler

As the cornerstone technology of fleet digitisation and automation, the digital automatic coupler (DAC) has the potential to enhance rail freight’s load capacity and operational intelligence.

Discussion and key findings

• Having defined the specifications and conducted a thorough cost-benefit analysis, the TD delivered several coupler solutions, including an SA-3 and Scharfenberg type. Subsequent work introduced digital and automatic functions.
• The prototype underwent dynamic testing, conducted at different speeds on both straight and curved tracks. This was done alongside climatic tests evaluating the couplings in diverse cold weather conditions. The real-world data achieved during these tests augmented the insights gained from controlled climate chamber tests.
• EU-Rail will bring DAC up to a functional level within the railway system by preparing the necessary procedures and requirements. This includes the procedures and requirements for assessing the integration of the energy provision, vehicle control, and train functionalities into freight wagons and locomotives, ultimately enabling interoperability and general operation.

Conclusion

The new freight DAC will contribute to the automation of shunting operations. Furthermore, by introducing electricity into the wagons, the solution will bring additional functionalities to the entire freight system.

Digital automatic coupler in brief

TRL: 7 (system prototype demonstration in operational environment)

Targeted market: European freight wagons

Solution status: Ready for prototype testing

Market outlook: Good perspectives for infrastructure managers and European railway undertakings

Condition-based maintenance

Condition-based maintenance (CBM) is a maintenance strategy that monitors the condition of an asset to determine when maintenance is needed. It aims to perform maintenance only when it is necessary and not on a fixed schedule or after a failure occurs.

This proactive approach can help extend machine life, increase productivity, lower operating costs, and minimise downtime.

As such, it is a key pillar to achieving fleet digitisation and automation.

However, leveraging the potential of CBM first requires that it be fully integrated into general maintenance and operation processes. But doing so means changing traditional maintenance procedures and workflows.

This TD aimed to rethink roles and responsibilities in a digitalised maintenance process for rail freight.

Discussion and key findings

• The TD developed user-centric CBM dashboard templates for different components. Not only do these templates ensure a data structure that can serve as a basis for processing and presenting CBM use cases, it also allows for the easy comparison of data from different assets, thus improving the overall quality and speed of CBM.
• The CBM dashboards play a vital role in the overall CBM strategy. The visualisations they provide enable fleet management to make use of the additional information provided by the telematic boxes. Furthermore, the dashboards allow for a validation of the data from different locomotive components.
• To ensure the usefulness of CBM-initiated maintenance actions and to provide a continuous improvement and validation process within the CBM strategy, the TD developed a feedback loop model. Integrating the feedback loop from the workshop into CBM actions allows workers to review all CBM-driven maintenance actions.

CBM in action

To illustrate how condition-based maintenance works, take for example the diesel particle filter – a very cost-intensive locomotive component.

Too much clogging of the filter can result in problems with the locomotive and, in extreme cases, cause the locomotive to breakdown. Traditionally, the degree of this clogging was read out and evaluated according to a pre-set maintenance schedule when the locomotive visited the workshop.

However, by installing sensors into the filter, this data is now being continuously transmitted from the locomotive to a dedicated analysis centre. Here, the data is merged with a large amount of other data and used to create a model for predicting component faults.

These models are made available via an interactive dashboard where fleet control can remotely and continuously monitor diesel particle filter pressure and temperature. If defined thresholds or values are exceeded, the dashboard automatically generates a message. For example, if the particle filter is clogged and the pressure increases to more than 150 mbar, a replacement alarm is triggered.

Any generated alarm is transferred to the feedback loop, where a technical supervisor can confirm its validity before sending the alarm to the production system. Meanwhile, in the operating system, the alarm generates a code in the locomotive’s maintenance worklist so that the filter is automatically scheduled for replacement at the next workshop visit.

At the workshop, an employee executes the repair order and then fills out the prepared feedback form regarding the actual condition observed and, afterwords, documents the work carried out. This allows for a comparison between the alarm and the feedback from the workshop, enabling further improvements to the CBM process.

Since the diesel particulate filter use case was implemented, 12 unnecessary replacements have been avoided and the number of locomotive failures during operation have been substantially reduced – showing the optimisation potential that can be realised through data driven, condition-based maintenance.

Conclusion

CBM allows maintenance to be performed only when necessary and based on real data about the asset’s condition. It involves changing the way maintenance staff work and the company’s processes. Although this can initially be costly and complicated, in the long run, CBM reduces costs, avoids unnecessary maintenance activities, increases the availability of the locomotive, and improves the reliability of a freight asset. This makes CBM a viable option for railway undertakings and those entities in charge of maintenance – one that could result in a 10% cost reduction in lifecycle costs (LCC).

Digital transport management 

With the aim of bridging the gap between timetable planning and operational traffic, as well as between yard and network management, this TD focused its research on real-time network management and the intelligent video gate, both of which will help make digital transport management a reality.

Real-time network management

Traditionally, traffic management uses a reactive strategy where it works in response to what is occurring in the traffic system. The objective of this line of research is to shift traffic management towards a proactive strategy, one that enables it to take control over traffic and, in doing so, help close the gap between timetable planning and operational traffic.

To do this, research focused on better understanding the:

• Coordination of all operational activities taking place at arrival/departure yards
• Re-planning timetables for line traffic
• Prediction of system effects and the combined operations of yards and lines

Based on this research, a comprehensive yard and network management platform comprised of a yard coordination system (YCS), timetable modification module (TIMO), and yard departure deviation prediction model (YPM) was designed for the proactive planning of operational activities at a marshalling yard.

Discussion and key findings

• The YCS can enhance transparency and facilitate cooperative, proactive planning. It also has the potential to prevent and mitigate departure delays.
• A TIMO designed for short-term re-planning of line traffic was evaluated. The evaluation method used a heuristic approach that aims to address a high bottleneck robustness. It also looked at algorithm runtime and other criteria (e.g. train path deviation, change in departure time, etc.). The effect of several TIMO parameters (e.g. iteration settings, size of allowed time windows, share of other train paths that may be adjusted) were studied for both peak and off-peak traffic.
• A proof-of-concept model framework for increasing the predictability of yard departures and arrivals was evaluated. The model incorporates a machine learning-based YPM into a macroscopic network simulation model.
• Both the infrastructure manager and the yard operator can benefit from this model, the former by getting a more realistic picture of the train that runs along the line and the latter by improved yard arrival estimations.

Conclusion

Yard and network management is a solution capable of efficiently managing yards and the rail network. Using it will enable the identification of any difficulties and/or shortcomings of a yard manager and provide improved algorithms and specifications. It will also increase the automation of planning and traffic control tasks.

As a result, the yard and network management solution is expected to result in:

• 10% cost reduction at yards due to reduced lead time and the fact that real time yard management improves use of infrastructure.
• Up to 50% reduction in yards staff costs (reduced staff needed for train set-up, marshalling, and train-check) due to automatic train operation, automatic coupling, waggon intelligence, and video waggon detection gates.
• Up to 50% reduction in yard unreliability (delayed departure) due to real time yard management, automatic coupling, and intelligent waggons.
• Up to 20% improvement in yard total lead time (reduction of total yard lead time) due to real time yard management and higher punctuality of arriving trains.

Real time network manager in brief

TRL: 6 (technology demonstrated in relevant environment)

Targeted market: Mainly railway undertakings focused on the growth of pan-European freight operations

Solution status: In development (expected to be available for industrialisation by end of EU-Rail programme)

Market outlook: Good perspectives for freight operators, infrastructure managers, and railway undertakings

Next step: Develop real-time functions, perform real-time demonstrations, integrate with relevant systems/information platforms

Intelligent video gate

An intelligent video gate (IVG) concept is a gate system located at intermodal terminals and equipped with high-frequency cameras to automatically identify wagons and intermodal loading units. The gate is activated via a combination of cameras, illuminators, RFID readers, tags, and wheel sensors. Once activated, the IVG uses image recognition and machine learning algorithms to ID wagon numbers, loading units, and placards.

The use of IVG can reduce the time needed to conduct inspections and the shunting of arriving trains, which in turn means less unreliable departures. Terminal loading time can also be significantly reduced, particularly at large intermodal terminals (although the impact regarding block trains is more uncertain).

In terms of impact, it is expected that IVG can:

• Reduce wagon check time by up to 20%.
• Reduce terminal staff costs by up to 50% (the result of reduced inspection times for all train types).
• Terminal loading times could be reduced by as much as 50% (due to early wagon status information by the video gates, punctual train arrivals, and an improved management system.

It should be noted that although installing an IVG requires a significant investment, this cost could be partially compensated for by savings gained on infrastructure and equipment costs.

Conclusion

Using IVGs in terminals and yards significantly improves operational efficiency for terminal operators by:

• Accelerating processes through the automatic identification of wagons and cargo carriers
• Enhancing automation for check-in, inspections, damage claims, and the handling of dangerous goods

Intelligent video gate in brief

TRL: 7 (system prototype demonstration in operational environment)

Targeted market: Infrastructure managers, terminal handlers, freight railway undertakings

Solution status: In development (expected to be available for industrialisation by end of EU-Rail programme)

Market outlook: Good perspectives for terminal operators, railway infrastructure managers, and railway undertakings

Next step: Enabling improved cross-border operations through the use of Standardised European Railway Checkpoints, including the deployment of innovative technologies such as Intelligent Video Gates (TRL 8)

Transforming learning into innovation

By transforming learning into innovation, both TDs highlighted here allow the rail freight sector to visualise and concretely test technological advancements and to more adequately quantify the impact of fleet digitalisation and automation and digital transport management technologies.