The transportation sector is responsible for over a quarter of all energy consumption in the United States; thus, any meaningful attempts to address climate challenges by reducing energy consumption must prioritize transportation. The long-term goal of this interdisciplinary project is to develop holistic strategies to reduce transportation-related energy consumption in an equitable manner via system-level optimization of transportation networks and services. The research team will model transportation as a set of connected human-cyber-physical systems using data collected at various spatial and temporal scales.
The team will leverage artificial intelligence methods to address the computational challenge of decision-making given the numerous system-level decisions that need to be made in the areas of traffic signal control, lane management, public transportation planning and operations, and demand management. Reductions in energy consumption—and, hence, greenhouse gas emissions and urban air pollution—are expected by both prioritizing movements that can save energy (e.g., reducing the number of vehicle stops so energy is not needlessly wasted) and promoting travel modes that are more sustainable. Seed funding will enable the investigators to form a new, interdisciplinary research team, focusing on the societal challenges of climate and transportation, and to obtain preliminary results that will support the development of competitive proposals for federally funded research projects.
