This Faculty Early Career Development (CAREER) grant supports fundamental research in understanding of traffic flow patterns in mixed autonomous and human-controlled traffic streams. Emerging connected automated vehicle (CAV) technologies hold enormous potential to reduce transportation system congestion, improve safety, and facilitate higher energy efficiency. While anticipated benefits are greatest when all vehicles on the roads are CAVs, the near-term future will consist of mixed connected vehicles (CVs) connected automated vehicles and non-connected (human-driven, or HV) vehicles. This project will study mixed traffic streams, quantifying their impact on congestion and traffic flow, and develop robust strategies intended to improve performance and safety of the system as a whole, thereby enabling better understanding of how different vehicle types interact in traffic. The results will guide the development of roadway design, policies, and long-term planning for future transportation systems. The research activities will be closely integrated with a set of education and outreach activities to effectively promote smart and sustainable transportation. These activities include (i) developing a set of tools that will be shared with the research and practice communities (e.g., portable driving simulators, and an open-sourced micro-simulation platform), (ii) enhancing existing engineering curricula, (iii) broadening participation of women in STEM, and (iv) providing outreach to a broad audience, including K-12 students and teachers as well as cross-sector research communities.

The research objectives of this project are to (i) characterize the driving behaviors of HVs and CAVs in conflicting traffic streams, (ii) the collective impact of mixed traffic on the traffic flow, and (iii) investigate robust tactical-level control strategies for CAVs to improve system performance with respect to throughput, traffic flow stability, and safety. The research involves data collection on using driving simulators and field tests, establishment of behavior models for different vehicle types (i.e., CAVs, CVs, and HVs) based on the collected data, and design and evaluation of tactical level control strategies using rule-based and Artificial Intelligent (AI)-based control approaches. This research will uncover the cooperative behavior of CAVs and the behavior of HVs and CVs under cooperation in the context of conflicting traffic streams. The research is expected to produce effective control strategies for CTS with mixed traffic. The outreach plan will provide high school students and teachers with exposure to the university's virtual driving lab as well as to research challenges in emerging transportation systems.

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.

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University of Massachusetts Lowell
United States
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