How advanced optimization won $57M in state funding for local projects

Spotsylvania County operates in an intensely competitive environment when seeking state transportation funding through Virginia's "Smart Scale" system, where it must compete against approximately 400 projects statewide for a limited pool of capital.

The challenge is a complex balancing act: the county is limited to submitting four projects per round and must decide which candidates to select, which "add-on" features (such as sidewalks) to include to boost multimodal scores, and how much local funding to contribute to remain competitive without overspending.

Traditionally, making these decisions required navigating shifting state scoring templates and unpredictable competitor behavior, often resulting in high local cost-shares and inconsistent project win rates.

We developed a cloud-based AI decision support platform that transforms transportation project selection from guesswork into scenario-based optimization. We employed advanced optimization to determine exactly how to select and configure projects and allocate "leveraged funding" (the county's self-funding contribution) to win as many projects in prioritized order as possible.

By simulating various competitive landscapes using data from past rounds, our software identifies the optimal configuration that achieves these wins while strictly remaining within the self-funding budget and minimizing the total funds contributed by the county.