Digital twins are more than models. They’re decision tools for transit leaders.

Dynamic digital twins take that capability further, taking in multiple factors, integrating planning and decision-making and delivering an understanding of how dynamics interact. That view of a complex situation helps transit leaders make decisions in real time, for example how to manage passenger flow as people move from one mode of transit to another in a busy hub, whether it’s commuter train, subway, bus line or bike path. This technology will only continue to improve, as quantum computing and other emerging tools come online in a major way. 

Both static and dynamic digital twins offer value and can be adopted in stages based on an organization’s AI maturity. Both can help modernize transit to better support everyday reality and the signature moments that ramp up pressure across transit networks.

In Canada, municipalities are already using digital twins to achieve these kinds of results. In cities nationwide, similar possibilities abound. Digital twin technology can help leaders understand how disruption might affect people, productivity and — critically — confidence. 

As a decision tool, digital twins can empower transit agencies to test scenarios like how service changes will affect riders, understand risk such as which assets or service points create the greatest operational vulnerability, and plan future-ready systems. 

When teams incorporate digital twins in decision-making, they gain an invaluable view of what’s happening and the ability to spot what could happen next. 

Digital twins also offer a way to change how transit agencies measure success. Traditionally, transit has focused primarily on network performance and the operational efficiency of that network — do trains and buses arrive and leave on time, for example. Digital twins enable the ability to move away from an operational mindset that measures the performance of the assets in the network, to performance based on the customer experience. This takes us one step closer to focusing on and improving comfort and convenience and the rider experience.

Safety and security are prime examples. At EY, we bridge AI with existing video management capabilities to eliminate surveillance blind spots. Our EY Video Analyzer summarizes scenes captured on video and analyzes what’s going on in a given situation. 

By converting video into actionable intelligence, transit agencies can use the tool to keep riders safe by identifying, identify maintenance needs, spot hazards on a track and gain other critical insight that traditional computer vision or surveillance tools miss.

And that allows transit agencies to make informed decisions immediately.

More and more transit agencies are assuming responsibility for connected mandates. Multiple modes of transportation. Traffic congestion. Pedestrian routes. Bike, scoot and car-sharing services. Street parking needs that grow alongside the use of car-sharing services. Curbside management born of the uptick in e-commerce and rideshare services – which will accelerate as autonomous vehicles become mainstream.

Cities everywhere are beginning to connect ownership and management of these priorities in a single, streamlined ecosystem that prioritizes personal privacy. As they do, digital twins can be an essential tool in decision-making, planning and modernization. 

Digital twins can help transit leaders see what is happening and shape what happens next.