What started in Indiana at the Crossroads of America in 2019 was well-received by recent AASHTO attendees seeking new and innovative ways to work more efficiently and meet or exceed their goals. For many years, Indiana was the center of the automobile industry. Elwood Haynes built one of the first successful automobiles in 1894 and tested it on the Fourth of July in Kokomo. He wanted to show the world that he could do better. AI and ML News: Why SMBs Shouldn't Be Afraid of Artificial Intelligence (AI) Fast forward 125 years later to 2019 when the Indiana DOT had the same desire as Elwood Haynes.

The Indiana Transportation Department (INDOT) is applying machine learning (ML) to project bundling to maximize cost savings and reduce the time it takes to create bundles from a week to minutes. Transportation engineers typically group infrastructure construction projects into bundles to gain efficiencies in contracting and project management, minimize infrastructure disruption and achieve economies of scale. Bundles are created from similar projects – bridge projects, freeway lighting installations or safety improvements – or from a variety of types of work in a specific location. Bundling is complex, manual, subjective process, with engineers usually working from maps and spreadsheets. INDOT has done manual project bundling for about five years and used that data to test benefits of ML-enabled bundling, in which an algorithm that had been trained on historic data makes bundling suggestions.