CAREER: Robust Traffic Management for Connected Vehicle Systems

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This Faculty Early Career Development (CAREER) project will contribute to the nation's economy and quality of life by enhancing the robustness of connected vehicle systems (CVS) to ensure that traffic management works as expected in a wide range of conditions, both foreseen and unforeseen. Connected vehicle technologies provide unique opportunities for cooperative travel to improve mobility and safety through innovative traffic management procedures. However, these procedures introduce coupling effects between information and traffic that could reduce traffic flow stability. These coupling effects are the result of the feedback loops between information and traffic that are highly dynamic and prone to disturbances transmitted from one system to the other. It is both important and challenging to ensure traffic flows are robust in response to these effects. This CAREER award supports research that will lead to new paradigms for the design and operation of robust traffic management to foster mobility, safety, and sustainability. The findings will directly benefit society in terms of more effective use of both cyber and physical infrastructures, leading to more sustainable and efficient transportation planning and operations that fully consider dynamic supply-demand interactions. The award also supports educational efforts contributing to transportation workforce by training the professionals with the dynamic systems perspective in traffic engineering. The project will disseminate research and educational outcomes to a wide audience, including K-12 students, professionals, and the public using Virtual Reality-based and simulation platforms. The research goal of this CAREER project is to develop a theoretical framework with solid scientific foundations to model the information-traffic co-evolutionary process to address the challenges created by CVS. The framework is built on a stochastic, coupled dynamical system that can model connected vehicle traffic dynamics at the local and network scales. The primary tool used in this project is the notion of non-equilibrium flow robustness, which will advance the instability analysis and attraction domain analysis of the coupled dynamical system. As the foundation, the non-equilibrium flow analyses help the algorithmic development related to detecting non-equilibrium flow transition, quantification and prediction of CVS robustness, and design of adaptive control principles by leveraging the complex network theory, stochastic optimal control, and variational principle. Research findings will be disseminated through a wide range of channels and integrated into advanced curricula to help cultivate the next generation of engineers and scientists to meet the challenges of the Internet-of-Things era.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.