EAGER: Real-Time: Learning-Mediated Control for Traffic Shaping

点击翻译按钮获取中文摘要

Efficient Management of Vehicular Traffic via Real-time Machine-Learning-Mediated Control and Traffic ShapingWhile connectivity and automation promise orders of magnitude gains in the safety and efficiency of vehicular transportation networks, these gains cannot be realized without monitoring, learning the behavior, and control of vehicles at different aggregation levels. Indeed, current congestion mitigation methods, such as speed harmonization that uses a sequence of variable speed limits along a highway do not reliably control congestion, and may exacerbate it (e.g., via shocks propagated through speed limit changes) due to the inconsistency between congestion prediction and real-time control. The objective of this project is to develop a holistic approach using machine-learning methods to identify and predict macroscopic congestion behavior of traffic based on both vehicle-borne and transportation infrastructure measurements, while designing fine-grain control systems for individual vehicles that can help to mitigate congestion effects. In doing so, the project recognizes that these designs must account for the possibility of low take-up rates of connected, automated vehicles (CAVs) over the next decade, and the consequent dominance of human-mediated vehicle operation for some time to come. The project also includes the development of educational materials on data analytics and vehicular control systems. Intrinsic to the program are efforts at outreach to involve high-school students via demonstrations and lectures based on the technology developed.The goal of this project is to develop the theory of and evaluate a novel approach to traffic management entitled "real-time learning-mediated control". The key idea is to meld large-scale real-time learning about macroscopic phenomena in a physically interpretable manner, with distributed dynamic control of individual vehicles in a provably safe and efficient manner. The work comprises two thrusts, namely (i) Traffic State Prediction, which offers a Graph Signal Processing (GSP)-based congestion prediction approach for planned and unplanned congestion-causing events, and (ii) Traffic Shaping and Control, which offers novel vehicular control methods that shape traffic in a stable manner over the multiple dimensions of target time headway and velocities over space and time, and candidate time-gap and velocity profiles in a mixed environment of both connected, automated vehicles and human driven ones. Thus, the overall aim is to combine the ability of learning methods to provide predictions about complex interconnected systems, with control laws that are safe and consistent with the laws of physics. The value of this research to broader society is in combining traffic prediction, control and learning, which can result in accurate congestion mitigation and increased throughput. Incorporating analytical concepts into senior design projects and courses enhances the project via educational impact. The project also contributes to development of systems-design expertise for students, as well as to diversity enhancement through minority student engagement.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.