This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This project will develop novel modeling, control, and optimization methods for connected and automated vehicles to operate in human-dominated traffic to improve the efficiency and sustainability of the urban transportation system while respecting individual drivers? unique behaviors and social norms accordingly. The significance of the research is highlighted by the following two needs. First, the inefficiency of the urban transportation system has resulted in substantial fuel waste and emissions over the decades. Leveraging vehicles? growing autonomy and connectivity, a significant boost of energy efficiency, emission performance, and traffic management can be achieved through dedicated control and optimization of vehicle maneuvers and routes. Second, human drivers will remain the majority of operators on the road in the foreseeable future. The resulting mixed traffic where connected and automated vehicles and human drivers share the road with frequent interactions requires detailed modeling of human drivers? behaviors in a socially compatible context. The proposed research can generate socioeconomic incentives such as improving the efficiency of the urban transportation system and promoting technology acceptance for sustainable mobility, thereby alleviating the nation's energetic and environmental concerns. The scientific outcome of the project will advance convergent research areas of control theory, optimization, human behavioral study, and machine learning. The project will involve an interdisciplinary team of students through hands-on research opportunities at Texas Tech University, which has been historically and actively engaged in serving the traditionally underrepresented student body in STEM, contributing towards equitable and inclusive educational and social outcomes.