Surface transportation is entering one of the most dynamic periods of innovation in decades. Technology is no longer an accessory or a “nice to have”:  it’s reshaping the foundations of how we plan, operate, and invest in the nation’s mobility systems.  Agentic and generative AI, connected and automated vehicles, vehicle-to-everything communication, digital twins, edge intelligence, even quantum computing are rewriting the playbook for transportation systems management and operations (TSMO).

Across transportation agencies, modes, and markets, the pace of research, testing, and experimentation has accelerated dramatically. Yet much of this progress in innovation remains local, undertaken and operating at the project, demonstration, and pilot level.

The transportation community is outstanding at innovation.  But what we haven’t yet mastered is scaling what works.

Federal, state and local agencies are deeply engaged in research, development and deployment (RD&D) projects.  The pipeline is rich with pilots and demonstrations that show real promise, and without a doubt this pipeline should be fed and nurtured.  But RD&D activities are most often structured primarily to coordinate and demonstrate what can work, not to ensure that what does work can scale.

Lack of scalability in new technology pilots is not just inefficient. Scaling innovations up and expanding adoption across systems and locations creates network effects that improve the overall benefits of that technology as well as the return on investment.

The Federal government, with support from state and local partners, can help to close the scalability gap by treating scalability not as a byproduct of successful RD&D, but as a program and project design principle, and as a measurement of technological and institutional readiness and maturity.  By embedding scalability into program and project structure, we’ll produce repeatable pathways to innovation that make successful deployments easier to trust and replicate, facilitating network benefits.

Evolving a next-generation RD&D innovation pipeline that encourages and facilitates the capacity to deploy innovation at scale will require a renewed and shifted focus in the RD&D programs. Specifically, the next authorization presents an opportunity to strengthen and renew institutional focus on the performance assessment process and by doing so demonstrate a commitment to generating the performance data that are so critical to deployment scalability.

Scalability depends on trust, and trust depends on proof.  Transportation agencies do not scale what they cannot prove in terms of value.  That is why performance assessment should be treated as a cornerstone of RD&D innovation.

Deployment Scalability as a Policy Goal

Scalability is not automatic, and it is not a given.  Only those solutions that deliver proven, consistent, replicable benefits (and to some extent costs) across varied contexts, networks, and jurisdictions over time are truly scalable.  Achieving this “scalability triad” (interoperability, replicability, proof of value) requires intentionality and long-term commitment at both the agency and program level.

In recent years, there’s been much success in bringing requirements for interoperability and replicability to the table early when making investment and project design decisions about transportation technology deployments.  What can lag is often the “proof of value” – in other words, performance assessment.  But just a slight shift in focus to facilitate transparent performance assessment could bring this third element of the scalability triad to the forefront and improve the decision-making and long-term investment phases of the RD&D innovation lifecycle.

De-Risking Technology Deployment: Building Confidence Through Performance Assessment

Clear public data on proven performance benefits/outcomes reduces risk for deployers investing in transportation technologies.  Grounding deployment in repeatable, transparent performance knowledge that travels from one deployment and deployer to the next makes it easier for deployers to make the decision to commit to investing in a technology deployment. It builds the confidence that makes scalability possible.

The institutional capacity to rigorously evaluate transportation technology deployment performance is not some sort of simple administrative function, nor is it a strictly analytical exercise that is a barrier to overcome on the way to completing a deployment.  Instead, this capacity can and should be viewed as a strategic business asset for improving decision making, long-term strategic planning, public resource stewardship, and maintenance of public trust.

Further, institutional support for rigorous performance assessment allows a “network of proof” to emerge: every deployment adds to the evidence base and contributes to the de-risking that supports scalability.  This makes performance assessment central to innovation policy.

The U.S. Department of Transportation already has a strong foundation in this space.  Its ITS Benefit and Cost Database, developed over decades, houses thousands of quantitative data points arising from reliable performance evaluations of advanced transportation technologies deployed in the field.  There is clear evidence that deployers truly value access to these evaluation results.  However, the dataset is not as complete as it could be due to the often uneven capacity for rigorous evaluation and performance assessment at the local agency and/or project level. Scarcity of reliable deployment performance data (especially cost data) can work to disincentivize future deployments; it’s a particularly frustrating cycle.

The cycle can be broken by strengthening institutional support for evaluation itself and working to ensure that deployers can and do conduct robust and meaningful performance assessments that yield quantifiable, credible, and transferable results.

This is not about imposing performance requirements or targets. It’s about building the institutional capability and culture to generate honest, data-driven insights that ALL agencies can use to make better investment decisions. Each well-executed evaluation adds to this shared evidence base, de-risking innovation by building the “network of proof” that accelerates scaling.

Value Multipliers Through Network Effects

As performance evidence accumulates, it creates confidence and trust in the potential of the new technology.  Confidence and trust enable scale.  Scale, in turn, unlocks network effects – the stage where collective adoption multiplies investment value and increases the magnitude of benefit. Strengthening the connective tissue between and among deployments therefore multiplies the return on every RD&D dollar.

Each successful deployment strengthens the fabric of the larger system and brings more value to the traveling public and to the taxpayer. This increase in value is a big payoff of scalability. The goal, then, is not just to fund individual deployments, but to support and facilitate network value by enabling interoperability, common architecture and standards, and federated/integrated data.

Artificial intelligence brings these dynamics into sharp relief. AI’s potential to enhance TSMO depends on data volume, data quality, data integration, and continuous learning. AI thrives in environments rich with reliable, integrated, and continually refreshed data. Critically, AI becomes more valuable as more deployments feed the system: each new data source improves model accuracy, each refined algorithm benefits all connected deployments, and each performance insight accelerates system-wide learning.

This is the power of scale. The same structures that support the deployment scalability triad (interoperability, replicability, and proven performance) are precisely what make AI useful and trustworthy in transportation. Supporting that data ecosystem is not a “side task”; it is a prerequisite for effective AI.

Replicable results drive adoption. Adoption drives interconnectedness. Interconnectedness produces the network effects at scale that sustain national progress in reducing roadway deaths, improving traveler mobility, and driving the American economy. If AI is to become a core aspect of TSMO, the next generation of RD&D programs must treat AI and deployment scalability as mutually reinforcing. Only a scaled innovation ecosystem can enable AI in transportation to deliver real public value.

Organizing for Scale: Building an Innovation Architecture for the Future

The next surface transportation authorization could increase scalability of transportation technology deployments via increased programmatic or structural/institutional clarity on the critical role of performance assessment and evaluation capacity building at the state and local level.  Existing federal programmatic and institutional resources can be leveraged to contribute to this goal, embedding a stronger role for evaluation and performance learning directly into the federal RD&D innovation architecture, thereby helping to ensure that innovation can travel farther, faster, and with greater widespread positive impact.

At its core, scaling innovation in transportation technology deployment isn’t just about scaling technology.  It’s about scaling trust.  Trust in results, trust in outcomes, and trust in investment value.

When agencies can see credible performance evidence and when evaluation is supported as a kind of shared public good, accelerated adoption can occur more naturally. The next era of transportation innovation may be measured not only by the brilliant execution of individual pilots and demonstrations, but by the ability of federal, state, and local agencies to scale trust in order to scale deployment.

Federal programs that help build that trust by encouraging state and local deployment partners to place increased emphasis on performance transparency and performance assessment have the potential to unlock the next era of widespread, connected, intelligent, and dynamic mobility.  The next surface transportation authorization can deliver a framework for how innovation grows: grounded in evidence, structured for scale, and built on trust.

Transportation has always been about connection—between people, places, and possibilities. It’s time to leverage an already-effective federal RD&D innovation system that better connects incredible ideas with the scale they deserve.

Marcia Pincus, PMP, is the Principal & Founder of Lucid Perch Consulting LLC.  She recently retired from the U.S. Department of Transportation, where she spent 20+ years in the Intelligent Transportation Systems (ITS) Joint Program Office in various roles, including over a decade as the Program Manager for ITS Deployment Evaluation and Performance Assessment.

The views expressed above are those of the author and do not necessarily reflect the views of the Eno Center for Transportation.