The topical issues of science and technology. Хрестоматия для студентов-бакалавров направления «Лингвистика»

Rail Transport

Improving rail crossing safety with artificial intelligence

Karsten Oberle

September 25,2020

Artificial intelligence (AI) and machine learning are increasingly being applied to some of the thorniest issues that rail operators confront, and one of the more promising ones is the monitoring of level crossings. Karsten Oberle, Head of Rail, Transportation Segment at Nokia Enterprise, explains more.

There are good reasons for the excitement around the application of video analytics, AI and machine learning to improve safety at level crossings. From 2009-2018, the U.S. Federal Rail Administration (FRA) reported an average of more than 900 injuries and 250 deaths at railway crossings annually. In the EU in 2018, Eurostat reported that there were 447 accidents at level crossings, which were also the cause of more than one in four of all railway fatalities.

For several years now, rail operators have used CCTV cameras to monitor level crossings and other parts of their infrastructure in order to reduce incidents and improve safety for the public and employees. The challenge with video, however, is that it often requires someone to continuously watch the feed in order to detect safety anomalies. Unsurprisingly, however, human operators can develop ‘video blindness’, and after relatively brief periods of time monitoring multiple video feeds, they can fail to see incidents that the cameras are recording.

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The logic of this approach is much simpler. The analytics program can be instructed to ignore activity on the level crossing until the closing of the gates. At this point, the tracks should be empty.

Upon barrier closing, any changes to the digital video pattern of

‘empty tracks’ is then treated as an ‘event’, which can trigger an alert to a human operator to review the footage. When the operator reviews the anomalous video footage, they can then take action if it is required.

In this approach, the AI engine does not have to be as smart as a human, it just has to do the boring job of monitoring the 99.9 per cent of the video stream when nothing is actually happening. Over time and thousands of hours of analysing footage the algorithm can be trained to recognise patterns that don’t require an alert, such as rain or snow, leaves and other normal environmental events. Personnel can tag this kind of footage so that the software gets constant feedback to improve its abilities and reduce the number of false positives.

This kind of video analytics solution also simplifies the forensic analysis of incidents, including identifying near misses. The software can simply be instructed to discard or archive all footage that corresponds to the normal view of the scene, which not only drastically reduces the amount of video storage required, but also decreases the amount of footage that the forensic team has to review.

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As such data is collected across hundreds of thousands of level crossings, it is not hard to imagine that the lessons learned will lead to much greater safety for the public and big operational savings to the rail operators. One operator that has already run trials on this type of enhanced railway crossing safety is Japanese rail operator Odakyu Electric Railway.

With testing at the Tamagawa Gakuenmae No.8 railroad crossing in Machida City, Tokyo, Odakyu has been working to detect abnormal events by applying machine learning-based AI to available camera images.

Analysing available image feeds generated by conventional railroad crossing cameras, the software can identify potential issues in real time. Running on edge computing resources, it can also greatly reduce required bandwidth at remote sites, which may have limited connectivity.

The primary benefit of this kind of approach to video analytics is its ability to add an extra level of safety – in addition to existing deployed safety systems – by transmitting real-time alerts to external systems when important activity is detected. This enables the railway crossing sensors to integrate with existing operational processes, augmenting the knowledge and decisions made by security and safety personnel, without replacing them. This also helps with acceptance of the system by operations personnel.

Finally, because these systems can work with most existing IP cameras, and even older, lower resolution cameras, they don’t

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require an expensive upgrade to modern HD CCTV systems. This means that using CCTV cameras to monitor railway crossings using machine learning isn’t simply a futuristic and unaffordable solution, but rather a practical way in which most rail operators could easily improve safety at railway crossings today. (6108)

“Global Railway Review”

Digitalisation in rail freight:

The key to a smart supply chain

Berd Hullerum

September 14,2020

Freight transport in Europe is expected to grow by 30 per cent by 2030 and, as part of the Rail Freight Forward initiative, Transfesa Logistics is committed to growing the portion of rail transport from the current 18 per cent to 30 per cent, thereby saving 290 million tonnes of CO2. A key to achieving this lies in the continued digitalisation of the rail system and, even more, on the full digitalisation of the entire supply chain. Bernd Hullerum, CEO of Transfesa Logistics, explains that, as a

‘door-to-door’ integrated solution provider, it is putting a major effort in achieving this by focusing on paperless processes and full track and trace capabilities for its customers.

Where do we stand today: Freight transport does not stop growing, even during a global pandemic like the one we are experiencing today. Freight transport and, even more so, rail freight transport, is essential and perhaps more important than

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ever before. During the recent impact of the pandemic, the whole of society has understood that efficiently working supply chains, which previously have not been noticed, are essential to our day-to-day life and for the proper functioning of the economy. Thanks to the fact that supply chains have been stable, society has been able to access basic goods without limitations, and we have supported the transport of critical equipment for health professionals across Europe. Especially, the rail sector has demonstrated its resilience and stability in the most difficult of environments.

This pandemic has also taught us that we are already a digitalised society. Communication is – by being forced through social distancing and banning people from leaving home – already majorly undertaken via online and app solutions, the home office is now the standard, video conferences a normality and business 4.0 a must. I can only agree that the current pandemic has done more for digitalisation than many other efforts over recent years.

The digital investment in rail traffic management

Moving now into the railway segment, starting with the infrastructure, we also see significant movements. All national infrastructure managers are heavily investing in the digitalisation of traffic management in order to improve the capacity of the existing infrastructure – for example, through smart switches, automated crossings and many other initiatives. However, there are two major challenges: first, the huge investments needed. For example, the fitting of only the entire German rail network with the European Train Control System

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(ETCS) could potentially cost up to €30 billion. Secondly, the missing European harmonisation and, still, the dominance of country-specific solutions. We need a strong European push for harmonised platforms and borderless data exchanges. In the end, the goal must be that it is as easy to run a train across Europe as it is to drive a truck on European highways.

Efficient asset management is key

For us, the efficient management of our assets is key. Here, expert artificial intelligence (AI) systems allow us, for example, to move from corrective to preventive maintenance – not only for high-tech locomotives, but also for ‘stupid’ rail wagons. In

Transfesa Logistics, for example, we are installing smart chips in our axles to allow for the full traceability of axles and wagons across Europe. This provides more than just information on a wagon’s location. It also helps us to monitor usage times and incidents, and thereby allows us to steer the maintenance pro-actively, including feeding this information into our maintenance workshops. Also, in the maintenance workshops, paperless and seamless information flows – e.g. through tablets and scan devices – helping to avoid manual intervention and errors in the revision and repair process.

Digitalisation to fully integrate the supply chain

As aforementioned, we are committed to growing the portion of rail freight transport significantly, thereby supporting European goals on CO2 reduction. For this, the integration of supply chain solutions is key, and digitalisation plays a major role. Today, data exchange and flawless track and trace from door-to-door is a challenge. Shippers, agents, transport

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companies, rail and terminal operators, public institutions etc, all have their own data/systems and interfaces which are not always automated, and no commonly shared platforms exist. In order to offer a solution to this problem, Transfesa Logistics launched the SORTLOT Project. The target is to develop, together with our partners, a digitalised supply chain ecosystem based on blockchain technology for providing end-to-end traceability and smart contracts for process automation, IoT (Internet of Things) in asset identification and the development of innovative AI applications, as well as ‘machine learning’ for the entire logistics process, as examples.

Thereby all stakeholders – such as shippers, rail and road transport companies, shipping lines, logistics operators, port authorities, railway infrastructure managers, customs authorities etc. – can operate on one fully integrated open ecosystem.

A second example of integrating the entire value chain using digitalisation to drive business control and efficiency is the automotive finished vehicle transport. Once cars are produced in the factory, they are then transported by rail to their final destination – normally a vehicle logistics centre, where they are unloaded, prepared and then delivered to the car dealership or, nowadays, even to the final customer directly to their home. Especially in the time of coronavirus, this is an interesting opportunity for contactless handover to the customer. Our car carrier wagons are equipped with GPS for full and real-time tracking. This helps, on the one hand, to immediately see supply chain interruptions and act accordingly, but it is also the

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basis for traffic optimisation based on AI algorithms. Once the cars arrive, through IoT technology, we have full and paperless traceability for storing, washing, inspection and final delivery preparation, to vehicle registration and customer delivery. Of course, this information is also available for our transport customers and suppliers in real-time.

Intermodality

A third example is the intermodal business. Transfesa Logistics has a long-standing experience in European intermodal business – for example, establishing the longest European rail freight service between France and Turkey and connecting the European automotive industry centre with our rail solutions. Our traditional history has been in the export of fruits from Spain, a segment that we just recently re-opened, with a new service connecting Valencia with Dagenham. Especially in the alimentation sector, but also general for intermodal services, the traceability of the transported goods is key for our customers. Especially in the reefer business, temperature control is vital. We achieve this by partially installing GPS, as well as an integrated transport management system that manages the entire door-to-door process, from pick-up, pre-haulage by truck, terminal management, long-haul by train, terminal management at the delivery point and last-mile truck. This allows for full transparency for the traffic managers and early warning tools to avoid supply chain interruptions, as well as full access to this information for our customers. The next step will be to allow automated booking for these intermodal trains across Europe.

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Summary

These are just some concrete examples of how we believe digitalisation will change the rail sector as an integrated part of European supply chain solutions, driving rail freight to be an integral part of eco-friendly, sustainable, efficient and high-quality transport solutions and helping Europe to reach its CO2 targets. (7883)

“Global Railway Review”

Aerial analytics: Laying the tracks of a new intelligence

Pradeep Sukumaran

August 26, 2020

Remember your first train ride? Depending on where in the world you grew up and when, the memory differs. Mine is from the 1980s in India: a trip back to my ancestral home in Kerala. From the minute the engine blows its resoundingly loud whistle, there’s magic in the air. You scramble to your seat alongside strangers soon to be friends. And while you gaze out through the window into the expanse as the train rolls along, you can’t help but think that this box on wheels is a slice of home. As warm as it gets, and just as safe.

That’s the idealist in me talking. The pragmatist pulls me in another direction: to peek closer at the 123,542km of rail tracks in India alone. The U.S. is 202,500km, and the European Union spans 225,625km of rail: almost five times the Earth’s equatorial length. When you’re dealing with such a large network, the statistics are overwhelming, and you’re bound to

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