AI and Digital Twins Can Help Detect Roadway Problems Earlier: Nidhi Sonar

When people hear “digital twin,” many imagine a sophisticated 3D model. What makes a true digital twin different?

A 3D model is essentially a digital representation of what an asset looks like. A digital twin goes a step further by representing how that asset is actually behaving over time.

What makes it a true digital twin is the continuous connection between the physical and digital environments. Data from sensors, inspections, traffic systems, weather conditions, maintenance records and other sources can continuously update the digital representation. Ideally, the information also flows the other way, where insights from the twin help inform decisions in the physical world.

To me, the distinction is really between visualisation and intelligence. A model tells us what exists; a digital twin can help us understand what is happening, why it may be happening and, increasingly with AI, what could happen next.

What kind of real-time information would be required to create a useful digital twin of a road, bridge or transportation network?

It depends on the asset and the problem we are trying to solve. We do not necessarily need to collect every possible piece of information simply because the technology allows us to.

For a roadway network, useful inputs could include traffic volume, vehicle speeds, congestion, incidents, pavement condition, weather and even construction activity. For bridges, we might incorporate strain, vibration, displacement, temperature, loading and inspection data.

The most useful digital twins would also combine real-time information with historical records. A sensor reading by itself tells us the condition at one moment, but when we compare that reading against years of deterioration, maintenance and environmental data, it becomes much more meaningful.

The goal should therefore be to collect the right data for a specific decision rather than simply creating the largest possible dataset.

Could you explain, through a practical example, how AI might identify an infrastructure problem before it becomes visible during a routine inspection?

Consider a bridge instrumented with sensors measuring vibration, strain and temperature. Under normal conditions, the bridge develops a fairly predictable response to traffic and environmental changes.

An AI model could continuously learn those patterns and identify subtle deviations. Perhaps vibration at one location begins increasing very gradually, while the relationship between temperature and strain also starts changing. None of those changes individually may be large enough to trigger an alarm or even be visible during a routine inspection.

However, when the model considers those signals together and compares them against historical behaviour, it may recognise that the structure is behaving differently from its established baseline.

That does not mean AI diagnoses the defect or automatically decides that the bridge is unsafe. Rather, it can flag the location for engineers to investigate sooner.

That is where I see the strongest value of AI helping engineers know where to look before a problem becomes obvious.

You worked on a smart-city digital twin project designed to predict crime hotspots. How did that experience influence your thinking about transportation infrastructure?

That project significantly shaped how I think about digital twins because the research was not focused simply on creating a digital representation of a city. We were trying to use data within that environment to support an operational decision.

My research involved predicting crime hotspots and supporting the dynamic deployment of licence plate reader cameras. What interested me most was that the digital environment became useful when it helped connect historical patterns, spatial information and real-world interventions.

Transportation infrastructure operates in a very similar way. Roads and transportation networks are not static assets. Traffic patterns change, infrastructure deteriorates, weather creates disruptions and human behaviour continuously affects the system.

That experience made me see digital twins less as sophisticated models and more as decision-making environments. The technology becomes valuable when it helps us answer questions such as where resources should be deployed, which locations need attention first and how conditions are changing over time.

How can digital twins help transportation agencies move from reactive repairs towards predictive maintenance?

Traditionally, a large portion of infrastructure maintenance is condition-based or reactive. We inspect an asset, identify deterioration and then determine what needs to be repaired.

Digital twins can add another layer by continuously tracking how an asset is performing between those inspection cycles. If we combine sensor measurements, historical deterioration, maintenance records, traffic loading and environmental exposure, AI can begin identifying patterns associated with future failure or accelerated deterioration.

That allows agencies to estimate not only the current condition of an asset but potentially how quickly that condition is changing.
The benefit is not necessarily predicting the exact day something will fail. Even knowing that one pavement section or infrastructure component is deteriorating significantly faster than comparable assets could help agencies prioritise inspections, maintenance funding and rehabilitation before the problem becomes considerably more expensive.

What role do 5G, IoT networks, edge computing and cloud connectivity play in keeping these digital representations updated?
They essentially form the communication infrastructure behind the digital twin.

IoT devices provide the connection to the physical environment through sensors and connected equipment. 5G and other communication networks allow large quantities of that data to move quickly. Edge computing allows some information to be processed close to where it is generated, which is particularly useful when a system needs to respond quickly.

Cloud platforms can then provide the computing power and storage needed to integrate information across an entire transportation network.

However, I would not say that every digital twin requires all these technologies simultaneously. The architecture should reflect the use case. A traffic-management application may require second-by-second communication, while pavement deterioration may only need periodic updates.

Connectivity should support the engineering objective rather than becoming the objective itself.

If connectivity becomes unavailable or sensor data is incomplete, how reliable does the digital twin remain?

A well-designed digital twin should not immediately become unusable because one sensor stops transmitting. There should be redundancy within the system, quality checks on incoming information and a clear indication of how current and complete the underlying data is. Historical information and predictive models may allow the twin to estimate conditions temporarily, but those estimates should be clearly distinguished from actual observations.

This is particularly important for safety-critical infrastructure. Engineers need to know whether they are looking at measured, predicted or outdated data.

Reliability is therefore not simply about keeping the digital twin running. It is also about communicating uncertainty. A trustworthy system should be capable of saying, in effect, “Our confidence in this prediction has decreased because the available information is incomplete.”

AI systems are only as reliable as their underlying data. How can authorities manage inaccurate, outdated or biased infrastructure data?

Data governance needs to become part of the infrastructure strategy rather than something addressed after the AI model has already been built.

Authorities need standardised formats, documented data sources, clear ownership and procedures for identifying missing or outdated information. Automated quality checks can flag unusual values, but domain experts are still essential because an algorithm may identify something as statistically unusual even when there is a valid engineering explanation.

Bias is also important. For example, if certain roads have been inspected more frequently than others, the dataset may make those roads appear to have more defects simply because we have observed them more closely. AI outputs should always be evaluated in the context of how the data was collected. Better algorithms cannot compensate indefinitely for poor underlying information.

Could digital twins realistically help cities reduce congestion and improve traffic management, or is their immediate value mainly in infrastructure maintenance?

I think both applications are realistic, although they operate on very different timescales. Traffic management may be one of the most intuitive applications because transportation agencies already collect large amounts of operational data through signal systems, connected vehicles, cameras and traffic sensors.

A network-level digital twin could simulate how congestion evolves and allow agencies to evaluate signal timing, incidents, lane closures or detours before implementing changes. Maintenance applications may develop somewhat differently because deterioration occurs over longer periods and requires the integration of inspection, asset-management and sensor information.

Ultimately, the greatest value may come when these systems are connected. Traffic influences infrastructure loading, construction affects congestion and asset condition can affect network capacity. A mature digital twin could help agencies evaluate those relationships rather than treating operations and infrastructure maintenance as completely separate problems.

What are the biggest barriers to adoption: cost, legacy infrastructure, fragmented data, shortage of skills or coordination between agencies?

I would argue that fragmented systems and coordination are probably the most difficult barriers because they affect almost everything else.

Transportation agencies already have enormous amounts of useful data, but it often exists across different software platforms, departments, consultants and asset-management systems. Even within one organisation, traffic, roadway design, maintenance, structures and GIS teams may maintain completely different datasets. Cost and technical skills certainly matter, but agencies can gradually acquire new technology and expertise. Integrating decades of legacy information and creating standards that allow different groups to work together can be considerably more difficult.

For digital twins to scale, interoperability will therefore be just as important as AI itself.

How relevant are these technologies for India, where cities operate at enormous scale and infrastructure conditions can vary significantly?

I think they are extremely relevant to India precisely because of that scale.

Indian cities are dealing simultaneously with rapid urbanisation, increasing travel demand, ageing infrastructure and major new transportation investments. Digital twins could help agencies manage those systems more systematically, particularly for traffic operations, metro systems, highways, bridges and rapidly developing urban corridors. At the same time, implementation needs to reflect local realities. Building a digital twin does not necessarily mean instrumenting an entire city with thousands of expensive sensors from day one.

India could benefit from a more targeted approach: identifying high-value corridors or critical assets, integrating data that already exists and gradually expanding the system as agencies demonstrate measurable benefits.

Given the scale of infrastructure investment occurring in India, even relatively small improvements in maintenance prioritisation or network efficiency could translate into significant savings.

Could digital twins work for smaller cities and resource-constrained public agencies, or will adoption initially remain limited to major projects?

They absolutely can work for smaller agencies, but the definition of a digital twin may need to be more practical.
A smaller city does not necessarily need a highly detailed 3D model of every asset connected to thousands of sensors. It might begin with one roadway corridor, one recurring maintenance problem or another carefully selected infrastructure asset, combining existing GIS information, inspection records and a limited number of sensors.

Cloud computing is also making sophisticated analytics more accessible without requiring agencies to maintain extensive computing infrastructure internally.

The key is avoiding the idea that digital twins need to begin at the city scale. Starting with a focused problem allows agencies to demonstrate value before investing in a much larger system.

Who should ultimately be accountable when an AI-supported infrastructure decision proves incorrect?

The professional or organisation responsible for the engineering decision must ultimately remain accountable.

AI should function as a decision-support system, particularly when public safety is involved. It can process far more information than a person could reasonably analyse manually and can highlight risks or patterns that may otherwise go unnoticed.

However, an algorithm does not understand the broader consequences of a transportation decision in the same way that an engineer or public agency does. Accountability therefore needs to remain clearly defined through engineering review, documentation and validation of AI-supported recommendations.

At the same time, engineers need enough understanding of these systems to question their outputs. We should not accept a recommendation simply because the model is sophisticated. Professional judgement remains essential.

What transportation use case do you believe could achieve meaningful, large-scale adoption during the next five years?

I think AI-assisted infrastructure inspection and asset management have particularly strong potential for large-scale adoption.
Transportation agencies already collect enormous amounts of imagery, inspection information, pavement-condition data and maintenance records. AI can help process that information much faster, identify deterioration patterns and prioritise locations requiring closer engineering review.

It also offers a relatively practical pathway for adoption because agencies do not need to transform their entire infrastructure network overnight. AI can first augment existing inspection and asset-management workflows and gradually become connected to broader digital-twin environments.

Over the next five years, I expect the most successful applications will probably be the ones that fit naturally into what engineers already do.

Technologies tend to scale when they remove repetitive work, improve decision-making and allow engineers to focus their time where professional judgement provides the greatest value.

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