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Achieving rail efficiencies in an evolving world If transportation is the lifeblood of modern cities, roads are all too often the clogged arteries, says Karsten Oberle, Head of Rail Business at Nokia Our reliance on road-based transport has many social, economic and climate costs, including negative outcomes for physical and mental health. Most municipal and regional governments have, for these reasons, long prioritised investing in their urban rail and transit systems in the hope of shifting transportation from road to rail. The pandemic, unfortunately, has been a step backwards for transit, with many commuters choosing either to stay at home or travel by car. As the pandemic eases, ridership is recovering, but with many companies embracing hybrid work, questions around commuting patterns have yet to be answered fully. With pandemic ridership down, transit authorities have been forced to find ways to keep their systems running despite the dramatic loss of fares. This has put a lens on operational efficiency as well as better ways to help passengers feel safe. At the same time, the longer-term issues of climate sustainability and the need to grow transit coverage and capacity have not gone away. The answers, however, may not be the same. Open air transportation modes that gained popularity during the pandemic, such as bicycle and scooter sharing, have the potential to address our transportation needs in different ways. What does the rapid electrification of cars and trucks mean for city plans to combat climate change, and what about charging infrastructure for electric vehicles of all sizes? Are autonomous technologies and robo-taxis really within reach, and what role will they play as part of the multi-modal mobility-as-a-service (MaaS) future? Transportation 4.0 With the slowdown in fare revenues, operational efficiencies are critical. Digital and Industry 4.0 technologies such as IoT, cloud, 5G, digital twins, AI and machine learning (ML) offer a host of potential solutions to rail and transit operational use cases. They include improved asset utilisation and maintenance, autonomous and remote operations, enhanced worker and passenger safety, mission-critical video and voice communications, as well as the ability to make rail and transit systems more
agile, responsive and better able to integrate into multi-modal transportation systems. In terms of operational efficiency, maintenance management is one of the key use cases for IoT and ML-based analytics. Today’s scheduled maintenance approach is low on actual performance or conditions information because it relies on the manufacturer’s best estimation of what is a safe maintenance schedule. A manufacturing fault or a mistake in assembly or repair can be missed, leading to the early failure of a component outside the scheduled maintenance window. Conditions-based maintenance is the next step in maintenance management that uses IoT sensor-based data for assets such as tracks or rolling stock. The goal is to gather data on the actual condition of the equipment using IoT sensors and compare current data to historical data to make repair decisions. In this way, machine learning analytics can correlate the observed condition to known equipment failures in the past, even going so far as to implementing predictive maintenance. This helps operators to better manage resources and avoid prolonged downtime as equipment waits to be maintained. Rail and transit operators can actually do less maintenance and achieve better, safer results. Automation A consistent theme for most rail and transit operators around the world is the shortage of drivers and yard workers. This is one area where automation and remote operations can help in different ways. There are high profile examples today of driverless trains such as the Paris Metro Line 14. Shunting yard operations are also being upgraded with trackside remote terminals. A yard worker can drive the shunter remotely while performing mechanical tasks such as pulling pins and other interventions needed during shunting and marshalling. Further adding to the safety and efficiency of these operations, IoT sensors and video analytics can help to maintain adequate distance from obstacles, regulate speed over points, and ensure safe coupling with sophisticated retarders. Remote back
office and yard workers will in the future oversee multiple operations simultaneously, with the ability to take control at any time or during specific points in the operation. Safety analytics The wide adoption of IoT sensors in railway and transit operations will be slow as older equipment and fleets are replaced with the new IoT-equipped technology. Much easier to leverage are the video cameras already used for CCTV systems, even when they are several decades old. Using AI/ML-based analytics, they can become sophisticated sensors. The Tokyo metro, for instance, is using video analytics to identify the number of people in metro cars to better manage train congestion. Video analytics can also be used for increased safety. Cameras pointed at a metro platform, for instance, generate a very consistent image of an empty track when the train is not in the station. The video analytics “learn” what normal looks like. If, however, a person falls onto the track, this will generate a different image or ‘anomaly’. The ML-based analytics may recognise that a person is on the track, or the anomaly will trigger the software to alert transit personnel, who can instantly view the footage and take immediate action. Video analytics can also be used for level Rail Professional
