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

通过实时机器学习实现车辆交通的高效管理--学习中介的控制和交通整形虽然连通性和自动化有望在车辆交通网络的安全和效率方面带来数量级的收益，但如果没有对不同聚合级别的车辆的监控、学习和控制，这些收益是无法实现的。事实上，当前的拥堵缓解方法，例如使用沿高速公路的可变速度限制序列的速度协调，不能可靠地控制拥堵，并且由于拥堵预测和实时控制之间的不一致，可能会加剧拥堵(例如，通过速度限制改变传播的冲击)。该项目的目标是开发一种使用机器学习方法的整体方法，以基于车载和交通基础设施测量来识别和预测交通的宏观拥堵行为，同时为单个车辆设计有助于缓解拥堵影响的细粒度控制系统。通过这样做，该项目认识到，这些设计必须考虑到未来十年联网自动车辆(CAV)使用率较低的可能性，以及随之而来的在未来一段时间内以人为媒介的车辆运营的主导地位。该项目还包括开发有关数据分析和车辆控制系统的教育材料。该项目的本质是通过演示和讲座，以所开发的技术为基础，努力扩大到让高中生参与进来。该项目的目标是发展一种新的交通管理方法的理论，并对其进行评估，该方法名为“实时学习中介控制”。其关键思想是以物理可解释的方式融合关于宏观现象的大规模实时学习，以及以被证明安全和有效的方式对单个车辆进行分布式动态控制。这项工作包括两个方面，即(I)交通状态预测，它为计划内和计划外的拥堵事件提供基于图形信号处理(GSP)的拥堵预测方法；(Ii)交通整形和控制，它提供新的车辆控制方法，在目标时间间隔和空间和时间上的速度的多个维度上以稳定的方式塑造交通，以及在连接的自动车辆和人工驾驶的混合环境中的候选时间间隔和速度分布。因此，总体目标是将提供对复杂相互关联系统的预测的学习方法的能力与安全且符合物理定律的控制律结合起来。这项研究对更广泛的社会的价值在于将交通预测、控制和学习结合在一起，从而可以准确地缓解拥塞并增加吞吐量。将分析概念融入到高级设计项目和课程中，通过教育影响来加强项目。该项目还有助于发展学生的系统设计专业知识，并通过少数族裔学生的参与来增强多样性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.