Across the United States, local and state transportation agencies are under pressure to maintain their aging infrastructure while also advancing equity goals set at the federal level. The core challenge: the data used to guide their investment is woefully uneven. This is where AI for infrastructure equity comes in, offering agencies a path to balance funding priorities with better, more affordable data.
Even larger agencies, which may be able to afford comprehensive surveys and sophisticated tools, can run into logistical and budgetary challenges. For smaller communities, the ask can become seemingly impossible. As a result, an increasing disparity leaves rural and underserved areas with less visibility, fewer resources, and longer maintenance backlogs.
Closing this gap is essential to ensuring that infrastructure spending delivers fair outcomes. The right technology can begin to make that goal possible.
A National Priority: Equity in Transportation
Equity has long been a priority for the U.S. Department of Transportation (USDOT). The agency’s Equity Action Plan makes clear that access to safe, reliable infrastructure is not evenly shared, with rural and underserved communities often facing deferred maintenance, limited mobility, and less visibility in planning decisions.
Among the biggest barriers leading to these imbalances is data. Smaller communities cannot afford frequent inspections and advanced asset management tools. This growing data divide compared with their larger counterparts reinforces inequity in both funding and outcomes.
The High Cost of Traditional Inspections
Infrastructure data is critical for decisions about where and how to invest limited resources. Unfortunately, collecting the data needed to make these decisions is expensive.
Traditional roadway inspections rely on specialized vehicles, complex sensors, and trained crews. Research shows that even automated pavement condition surveys can cost up to $200 per mile, with manual processes often reaching $500 per mile and above. For a county with hundreds of road miles, that bill can quickly reach hundreds of thousands of dollars.
That cost becomes a massive barrier for agencies with limited budgets. Hence, they rely on outdated, incomplete, and even anecdotal information, leading to roads deteriorating before needs are formally documented. In grant applications, where up-to-date data is required, the lack of accuracy can be fatal. Adopting AI for infrastructure equity allows agencies to cut costs and keep their data accurate at scale.
The Consequences of the Growing Data Divide
Without current data, agencies in smaller communities begin to struggle in their planning and grant request process. More specifically, they tend to face three problems:
- Hidden backlocks, with roadway conditions that may appear manageable until sudden failures demand emergency repairs. Deferred maintenance, of course, is far more expensive than timely upkeep.
- Unequal competition for funds, with agencies boasting larger budgets able to more easily provide the data-driven justifications that federal and state grant programs increasingly require.
- Reduced public trust, as residents begin to see issues like potholes, cracked sidewalks, and missing crosswalks, which can reinforce growing perceptions of neglect.
Worse, these inequities tend to compound over time. Agencies in rural and underserved regions fall further behind over time, even as national infrastructure investments and spending continue to grow.
A More Inclusive Approach: AI for Infrastructure Equity in Action
Fortunately, new technology is beginning to lower the barriers to data access. For example, roadway imagery can now be captured through more than just specialized vehicles, with automated sources like dashboard cams from fleets and vehicles that already travel local roads. With the right software, this imagery collection approach can lead to instantly actionable data, which changes the equation in a few key ways:
- Lower cost. Leveraging existing imagery streams means data can be collected at a fraction of the cost of traditional road surveys.
- Scalability. Coverage is not limited to a few corridors. Instead, it can expand to entire networks, including rural areas.
- Repeatability. Because image collection is automated, data can be updated frequently, helping agencies monitor change over time.
Through this more accessible and affordable way of data collection, small agencies can gain better decision-making tools. Infrastructure data becomes more democratized, and funding allocation can become more equitable in the process.
At its best, this leveling of the playing field also comes with other significant benefits. Local leaders can bring actionable data to their state and federal partners, while agencies can identify which corridors need repairs most urgently. The data can even lead to increased transparency, with the potential of public-facing dashboards that can show residents how road conditions are being tracked and addressed.
Aligning Agency Operations With USDOT’s Equity Goals
USDOT has made clear that access to safe transportation is a matter of equity. Programs like the Reconnecting Communities Pilot and BUILD (formerly RAISE) Grants prioritize projects that close gaps in access, even in the most underserved populations. Getting there requires data, so lowering the cost of data collection directly supports USDOT’s goal of delivering fair access to mobility and safety.
To close the data divide, public agencies must audit their current practices to identify gaps in how asset condition is measured and reported. New data sources, like low-cost imagery intelligence, can supplement or replace the more expensive manual surveys.
However, the data gathered cannot simply be about pavement performance. A broader approach to gathering data that allows assessments of access, safety, and community impact is vital. With a more practical and transparent approach, even small and mid-sized agencies and the community they serve can improve asset management practices on smaller budgets.
Integrating AI for infrastructure equity reinforces these federal priorities by helping agencies connect community impact directly to funding justifications.
Leveraging AI to Turn Data Into a Public Good
Bridging the data gap has become a core part of transportation planning. Residents in rural towns and districts have the same expectations of safe roadways as residents in major cities, and USDOT has made an intentional effort to fund the areas in most need of infrastructure improvements. A more systematic, comprehensive, and affordable way to collect data is what will drive progress toward transportation equity.
Closing the data divide ensures that all communities can compete fairly for resources, maintain safer roads, and deliver equitable mobility access. Affordable, AI-driven imagery intelligence makes that possible, representing an important step toward democratizing data and ensuring access for all.
Driving AI for Infrastructure Equity With Blyncsy
Blyncsy’s scalable imagery intelligence platform empowers agencies of all sizes to collect, analyze, and act on infrastructure data while lowering traditional cost barriers. Request a demo to learn how our solution can help agencies close the data divide and align with USDOT’s equity goals.
