Implementing an “AI Ready” Strategy

Our success in developing the machine learning model to accurately predict the outcomes of the rules-based system allowed the FTA to realize a cost savings of $1M per year in the creation and maintenance of the rules.

Since 1964, FTA has partnered with state and local governments to create and enhance public transportation systems, investing more than $12 billion annually to support and expand transit services.

To keep track of the industry and provide public information and statistics as it continues to grow, FTA’s National Transit Database (NTD) records the financial, operating and asset conditions of these transit systems.

National Transit Database (NTD). Our goal was to provide a machine learned algorithm that could mimic the existing rules-based engine that the FTA uses to identify issues in reporting.

Epigen was asked to use AI to detect issues in the reporting of public transportation within theOur success in developing the machine learning model to accurately predict the outcomes of the rules-based system allowed the FTA to realize a cost savings of $1M per year in the creation and maintenance of the rules.

In the first six months of the project, we were able to assist the FTA in implementing the infrastructure (both technical and personnel) necessary for AI, develop the communications strategy and planning methodologies, and develop and implemented an AI plan and predictive model that lowered the burden on public reporting.

This helped FTA policy makers gain insights into their data and adjusted existing legacy applications to interact with AI based models. Epigen worked closely with our Federal partners to develop an AI Ready strategy allowing them to gain valuable insights from their data and make well informed and timely decisions.