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Anna's journey

How far will digitalisation take us?

Find out as we join Anna on a journey 10 years into the future.

Saturday, 23 September 2030. “Good morning, Anna. This is your mobility app speaking. Are you ready for your trip to the coast? Just swipe to start. And don’t forget your umbrella. It’s going to rain later.”

Anna lives in the city and she’s been looking forward to this trip all week. To get to the coast, she’ll be using three modes of transport: the metro, a main line train and an autonomous vehicle. She planned and paid for the entire journey using the Mobility-as-a-Service (MaaS) app on her phone. 

By swiping the app on her phone, she initiates her journey. In the background, players right across the transport ecosystem – metro, main line and ride sharing – are now expecting Anna.

Like most people, Anna has opted to be “known” by her MaaS provider because it saves money and makes journeys easy. The app taps into everything from Anna’s search history and social media profile, to real-time rail signalling data. Everything – even her identity management – is cybersecured, so trust is not an issue.

The app allows her to plan and pay for journeys on any mode of transport, anywhere. Not that there’s much planning involved – the app takes care of all that. And there’s no need to buy tickets: the app is account based and the ticket is stored in Anna’s phone.

The metro is only a few hundred metres from Anna’s home, so she walks to the station. Out of habit, she taps in with her thumb – although she needn’t have bothered: thanks to be-in/be-out technology, the metro knows where she is anyway. 

As it’s Saturday morning, it’s not too busy. On a weekday, though, the flow of people is huge. Passenger signalling guides people around the station and even steers them to less busy parts of platforms and trains. The system is integrated with train supervision and positioning data from personal mobility apps, so crowding is managed at every level.

Passenger signalling – an evolution of passenger information displays – became mandatory as the capacity of metros increased. For a time, the capacity of stations, rather than track and trains, was the main constraint. But thanks to passenger signalling, the flow is now always smooth.

Once inside the station, Anna checks the passenger signals to find the right platform. The train won’t be arriving at its usual platform this morning, as one of the tunnels is closed for maintenance. 

In 2030, platform designations are routinely switched around at weekends, off-peak periods and whenever there’s disruption. The reason? Bi-directional running. This makes it possible to carry out maintenance work and upgrades on each separate tunnel in a twin-bore network, while continuing to run a limited bi-directional service. This became possible when operators adopted bi-directional CBTC and later, autonomous trains. Anna can still remember when operators needed to shut the whole line just to carry out maintenance.

Anna doesn’t have to wait long for a train. That’s good for her. But it’s a challenge for advertisers, who need to get their ads in front of passengers. Fortunately, Passenger Flow Analytics tells them how many people are on the platform (and on the train) and how long they’re going to be there. So they can be confident that people are seeing their ads – and operators can better monetise advertising space. Passengers get cheaper rides thanks to this subsidy.  

Ah! Here comes my train. Anna steps on board and takes a seat. It’s an autonomous metro train, with radar, lidar and cameras acting as “eyes”. Trackside equipment has been almost entirely eliminated – most of the safety-critical intelligence is on the train itself. 

Anna’s metro train is not only self-aware, it’s also aware of where all the other trains are. Trains negotiate with each other to decide who should go first at junctions. And they communicate directly with point machines to set routes. Operational conflicts are resolved by the trains themselves.

Although Anna’s train “thinks” for itself, it’s still centrally supervised. This role is performed by the demand-driven Operation Control Centre (OCC), which orchestrates operations in response to shifting passenger needs. One thing that makes this possible is data from mobility apps (like Anna’s), which provide operators with a clear idea of people’s travel intentions before they even leave home. 

Once at the main line station, Anna finds her carriage (she could have used her phone for this) and pauses to wait for the platform screen doors to open. In 2030, platform screen doors are mandatory, even on main line stations. None of this was possible until the advent of autonomous trains on main lines, capable of inch-perfect stopping.

The train Anna gets on is in two parts. At the front, there’s a RailBot – a streamlined drone locomotive. This will travel ahead of the autonomous passenger train. At full speed, the RailBot will be nearly 3km in front of the main train, scanning the track with an array of sensors and relaying safety critical information to the train behind it. Thanks to the RailBot, Anna’s train can see round corners.

Anna’s train uses sensors – cameras, lidar and radar – to “see” its environment. As well as detecting obstacles, these sensors provide real-time track surveys. And they enable virtual coupling, so trains can travel in convoy. 

As her train reaches its cruising speed of 400kmh, Anna settles back and reflects on how well robotic technology works on the railways. On the roads, by contrast, city speed limits have been reduced to just 20kmh to protect pedestrians – vital, because a third of the population is now aged 60 or over. 

Anna notices that the train has slowed down slightly. That’s because sensors in the RailBot have just detected a heavy rain shower several kilometres ahead and the braking curve will be affected. Just as well I brought my umbrella, thinks Anna as rain lashes the train windows. 

As with the metro, there’s not much trackside infrastructure. Lineside signals are a distant memory. Anna’s train “knows” where it is thanks to satellite localisation. This is supplemented by inertial measurements. However, fibre optic axle counters are deployed along the length of the route. In 2030, these are no longer the primary form of train detection, but they generate vital data about train weight and wheel condition. They also provide a cross-check with the train’s on-board navigation systems. 

Everything is supervised from the network OCC. While trains manage most conflicts themselves, it’s still necessary to have a big-picture view. As well as supervising safety, the OCC acts as a capacity market. Spare paths are traded between competing passenger and freight operators.

Although Anna and most of her fellow passengers may not realise it, the flow of data in the rail network of 2030 is phenomenal. All parts of the rail ecosystem – trains, fixed assets and supervision – are in constant communication. And thanks to predictive maintenance, service-affecting failures are a thing of the past. 

Anna’s journey is nearing its end. The rain has stopped. As her train pulls into the station, she notices a cluster of robot delivery vehicles. Robotic last-mile distribution has allowed railways to get back into the door-to-door freight market, just like in her grandfather’s day. 

Outside the station, an autonomous electric car is waiting to take Anna to her hotel. She doesn’t own a car herself and she did not have to book this one – the MaaS app takes care of everything, offering the convenience of having a car on-demand without the cost and hassle of ownership. 

A short ride later, Anna arrives outside her hotel. “Hello Anna,” pipes up the app. “Hope you had a good journey. We’re just arriving.” But Anna doesn’t hear. She’s already jumped out of the car and hurrying up the steps to meet her friends.