Rutgers research provides a practical roadmap for modernizing transportation asset management and capital planning practices.
Major phases of the AIP process.
Researchers at Rutgers CAIT led a pilot project with The Port Authority of New York and New Jersey that demonstrated use cases for predictive analytics in supporting long-term infrastructure asset management and investment planning strategies.
Recently published in Transportation Research Record, the study outlines a scalable, data-driven approach to asset investment planning (AIP) that integrates predictive analytics, risk modeling, and multi-objective optimization.
The pilot focused on 44 bridge structures near the George Washington Bridge, applying the methodology over a 20-year planning horizon. Using historical inspection data and deterioration modeling, researchers developed performance forecasts for deck and joint elements, incorporating risk indices and treatment strategies.
For example, results showed that a $5 million annual budget for deck elements maintained fair conditions early on but led to significant deterioration by 2041. Higher funding levels ($8–10 million) sustained better performance and reduced risk, while stepped budgets offered balanced outcomes.
“It is critical for transportation agencies to align funding strategies with lifecycle performance goals,” said Dr. Maurizio Morgese, Engineering Research Project Manager at the Rutgers Center for Advanced Infrastructure and Transportation (CAIT). “To accomplish this, agencies need tools and techniques that enable them to access their data, visualize it, and conduct scenario-based planning. This work demonstrates how the industry can move beyond reactive maintenance toward proactive investment planning.”
The AIP methodology also addresses limitations in traditional prioritization approaches, which often lack predictive capabilities and multi-year analysis. By contrast, the AIP framework supports consistent performance target setting, cross-asset coordination, and integration with external systems.
The methodology is also scalable and has the potential to expand to other asset types and incorporate emerging technologies such as machine learning and digital twins.
“Maintaining the vast US bridge network in a state of good repair is a top priority for all transportation agencies,” Morgese said. “This study offers a practical roadmap for modernizing transportation asset management practices and optimizing capital planning.”
This study was supported by CAIT-UTC-REG61, a research project funded by CAIT’s USDOT University Transportation Center (UTC) grant.
