State agencies must make major operational and capital investments into our multi-modal transportation system with wide-ranging impacts. How do we assess the sustainability of each mode, across environmental, social, and economic dimensions, to help agencies make more informed decisions? 

Vulnerable communities are often located near deteriorated and/or high-volume roads. The condition of such roads can have negative impacts on both the drivers and residents in terms of increased vehicle operating costs, emissions, and noise. How do we develop a user-centric metric that can be incorporated in pavement design frameworks alongside life-cycle costs and emissions?

Pavement design often includes a mixture of mechanistic and empirical components. Mechanistic components rely on physical relationships and processes, whereas empirical components use experimental and field data. Can we use physics informed machine learning to bring the two closer together such that our approaches can take advantage of both components?

While section level pavement life-cycle assessment (LCA) have seen numerous advancements over the last decade, network-level LCA frameworks are still missing key pieces. How can we fill in this knowledge gap, keeping in mind that increasing the scope to network level may bring additional sources of uncertainty that must be accounted for?