SBIR Phase I: Information fusion-driven adaptive corridor-wide traffic signal re-timing

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from a significant reduction in traffic delays, crashes, and fatalities by implementing a fully adaptive traffic signal re-timing solution. The most recent National Traffic Signal Report Card gave a failing grade of D+ to traffic signal operations in the United States. These failing grades are despite the fact that agencies spend approximately $2 billion every year on signal operation, maintenance, and capital improvements. If the US supports its signals at an "A" level, the public would see: (i) a 15-40% reduction in traffic delay, travel time savings up to 25%, and a 10-40 % reductions in stops; (ii) a 10 % or more reduction in fuel consumption resulting in nationwide savings of almost 170 billion gallons of motor fuels per year; and (iii) up to 22% reduction in harmful emissions. From a science and technology perspective, this effort will be an impactful success story for artificial intelligence and machine learning. As the small business is a product of Iowa State University start-up factory, the project is expected to involve students looking for industry experiences in the project leading to a more comprehensive education for them. This Small Business Innovation Research (SBIR) Phase I project will develop and demonstrate proof-of-concept of a fully adaptive traffic signal re-timing solution. The key intellectual merit of this effort will be developing deep learning models to extract abstract features from a range of heterogeneous information sources to perform feature-level fusion. Upon feature extraction, the proposed solution will use scalable deep reinforcement learning models to obtain re-timing decisions. The reinforcement learning process will help the system adapt to changing traffic scenarios at different time-scales without the need for significant manual interventions. The solution will be flexible for both onboard and cloud-based computing, depending on the availability of such platforms. Overall, the proposed system will reduce implementation time and capital and maintenance expenditures. These advantages will encourage cities around the US and internationally to adopt such a re-timing strategy and will dramatically transform the current landscape of this market. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.