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These immersive, virtual 3D models are helping plan the infrastructure of the future

‘Digital twins’ of spaces like ports, roads, and train stations let planners experiment with changes before implementing them in real life.

These immersive, virtual 3D models are helping plan the infrastructure of the future
[Images: cherezoff/iStock, sgame/iStock]
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It can be hard to remember the “before-times”—the relative normalcy that existed before the COVID-19 pandemic took hold. But even as supply chains strained, airport traffic shrunk, and public transit facilities and roads emptied, transportation technologists have been actively working on how businesses and society will reemerge as the pandemic is tamed.

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Managers at the Port of Rotterdam, the largest seaport in Europe, are preparing for when autonomous vessels will pull in to load and unload containers filled with the goods many rely on every day. These crewless container ships will navigate its harbor, assuring safety through the efficient exchange of ship-to-shore data. Road engineers in New Zealand are predicting how quickly, or slowly, drivers might take that curve or open road, preventing their untimely undoing. Airports and railway station operators are telling travelers where they are within terminals and the best routes to get where they need to go without having to hunt for an information desk or static map with a “you are here” dot.

In each case, reality is being captured, and the future is being designed with 3D virtual models, or “digital twins.” These mirrors replicate the physical environment as well as processes, relationships, and behaviors. Digital twins take in vast amounts of data, including streams from Internet of Things (IoT) sensors, to monitor current operations and historical behavior, and plan for what may happen next. When built with geospatial technology, or a geographic information system (GIS), digital twins are immersive, multidimensional, and location intelligent—able to show in detail what’s happening, when, and where, and can be used to explore or predict future states.

It’s the same concept that factories employed for years, creating digital versions of their physical assets to keep apprised of every detail. Now, these “mirror world” replicas are allowing the transportation industry to understand systemic risk in a holistic way, enabling predictive asset management to optimize the application of limited resources, and to avoid dangerous and costly disruptions.

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Roads as Smart as Cars

As far as predictions are concerned, it’s not a matter of if, but when, fully autonomous cars take to the roads in significant numbers, bringing with them the promise of fewer accidents caused by human error. For that to happen, though, the roads vehicles traverse will need to be just as smart.

In New Zealand, officials with the country’s transportation agency have used GIS models to measure the country’s existing road system to predict just how fast drivers can travel down a straightaway or how much to slow down for a curve on less-traveled rural roads. Ideally, they work to prevent out-of-control crashes through signage, speed limits, and physical fixes. And, in Colorado, smart mapping has helped skiers, snowboarders, and residents of mountain towns to navigate high-elevation roads during winter unimpeded. Teams in the state’s department of transportation capture data about weather, freight movements, and demographics, then apply this location intelligence to predict road maintenance needs. In Iowa, sensors attached to snowplows are collecting similar data for digital twins of road networks to show what has been cleared, saving the state $2.7 million annually.

With the help of these GIS-driven dashboards and other visualization tools, human observers gain a near real-time view of a transportation system’s performance. That view isn’t limited to behind-the-scenes operations; some transportation hubs are putting their 3D models out in front for their travelers to see and use, helping them navigate airports and train stations.

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Wayfinding Inside and Out

Europe’s largest railway network, Deutsche Bahn, is experimenting with smart maps for its passengers to help them decide whether to take a taxi, rent a bike or scooter, take a rideshare, or just walk once they’ve arrived at their station.

Airports in Geneva and Los Angeles—as well as train stations in London—have constructed 3D models of their spaces to guide passengers. And Ireland’s international hub deployed the Dublin Airport App, which uses location intelligence to provide a smooth travel experience by aiding both customers and staff. These mobile applications show a traveler’s location inside the terminal, mapping a route for them to a gate or restaurant. The GIS technology that underpins the app has also given the airport’s employees a single view of the truth, providing them with greater situational awareness, and allowing them to pinpoint exact locations of the assets in question.

Streamlining Supply Chains

Just as road engineers are preparing for a driverless future and Dublin airport is using its virtual model to help employees do their jobs, the logistics industry is using digital twins to get smarter about how goods are delivered, from ship cargo to the customer’s door.

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The pandemic has fueled online orders and made truck deliveries near constant sights in neighborhoods. Parcel delivery companies such as UPS and FedEx use digital models powered by location intelligence to see where and when it may be best to dispatch bicycle couriers or smaller vehicles for the “last mile” to reach customers in dense urban centers.

Long before a package arrives on a doorstep, though, many of the products found inside reach their destination country on container ships. Just as road engineers are preparing for driverless cars, ports are creating plans in anticipation of automated ships. Europe’s biggest and busiest marine hub, the Port of Rotterdam, expects its first fully autonomous vessel to arrive within the decade and is preparing by ensuring the port’s systems can communicate with vessels and trucks, automatically, with minimal human intervention. Seaports like Rotterdam keep their multi-modal transportation networks connected using location intelligence technologies such as sensors and GIS to facilitate movement of ships, containers, and people.

When autonomous ships are communicating directly with port infrastructure, machine to machine, the port’s entire physical environment will need to be mapped, monitored, and controlled in a virtual one. The data being shared between all these objects will be the most crucial part of the digital twin environment.

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Building Smarter Cities

Data sharing—not just between objects but between digital twins themselves—will become more important as entire cities become smarter, too, and reliant on sensors and IoT devices to take the guesswork out of planning for the future.

The Canal & River Trust used digital models in coordinating the work of multiple contractors restoring an obsolete 2,000 mile-long network of commercial canals in England and Wales into popular open space and recreation areas for residents and tourists. GIS-driven apps are provided to users of the public spaces to report instances of works needing to be done such as graffiti removal or repairs. Elsewhere, lanes for car traffic are being turned into bicycle and pedestrian paths with the help of 3D models. The paths of roads, new underground metro tunnels, the positioning of new cell towers, or buildings inside cities can first be tested and viewed in a virtual environment among multiple stakeholders, including government officials, industry, and the public.

Predictability could be something to look forward to in the post-pandemic world, at least when it comes to how people and products travel. It will likely require peering through a looking glass, or digital twin, applying geospatial technology and data that will show the best paths forward.

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Ian Koeppel is a cultural geographer and France-based international expert in the transportation industry for Esri, a global location intelligence and mapping technology company.