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The objective of the project is to use empirical testing, life-cycle assessment, and systems analysis to research and demonstrate an energy productivity of goods delivery improvement with drones, delivery robots, and driverless cars of at least 20% compared to a baseline network. In addition, the research will develop proof-of-concept testing, model, and simulation for a smart curb space as an intelligently-managed urban delivery zone for improved energy productivity and sustainability, with a goal of demonstrating at least an additional 10% improvement in energy productivity.

The objective of the project is to develop and demonstrate a replicable data collection and training framework that supports energy consumption prediction that will enable real-time energy consumption and mobility optimization.

The objective of this project is to develop a Multi-Unit Dwelling and Curbside Residential Charging Toolkit including all necessary information (e.g., technical considerations, business case, sample agreements, and sample policies) to evaluate and implement innovative, cost-effective, and flexibly-expandable charging solutions that will enhance residential MUD and curbside plug-in electric vehicle (PEV) charging systems to attract and retain customers. Project results and the Toolkit will be broadly disseminated to ensure a meaningful and lasting market impact.

This project will use novel analytics to advance transportation efficiency at Dallas-Fort Worth International Airport.

This project will use distributed sensing and high performance computing to maximize mobility energy productivity at Chicago O’Hare International Airport.

This project will pursue improved safety and reduced cost associated with handling and transporting reclaimed lithium-ion batteries.

The objective of the project is to develop and demonstrate a replicable data-driven system-level model capable of identifying transportation system performance inefficiencies to enable management strategies for improved mobility/energy efficiency.

The objective of the project is to research, develop, and demonstrate an energy-optimal urban transportation hub travel simulation platform that will enhance energy flow and travel efficiency.

This project will identify barriers in baseline platooning systems and explore solutions to overcome these barriers. The project will instrument trucks with Cummins ADEPT features and other subsystems required for effective completion of project. The project will also define routes, collect and analyze data for two- and three-truck platooning configurations. The project will identify barriers and potential solutions.

The objective of the project is to enable state and local authorities to assess and optimize jurisdictional level transportation energy, emission, and productivity by monitoring real-time energy and emissions flows. The project will develop and demonstrate a practice-ready real-time online tool for evaluating surface transportation energy and emissions along with human productivity measures that utilizes existing data sources and is ready for wide-spread deployment.