Scale-Dependent Observability of Emergent Dynamics: Application to Traffic Flow with Connected Vehicles

突发动力学的尺度相关可观测性：在联网车辆交通流中的应用

基本信息

批准号：
1663652
负责人：
Kshitij Jerath
金额：
$ 25.97万
依托单位：
Washington State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2019-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1663652&HistoricalAwards=false
关键词：
Scale Dependent Observability Emergent Dynamics

项目摘要

Global emergent patterns are observed in several large-scale complex systems, such as transportation networks, power grids, and financial markets. Gaining understanding of these dynamically evolving behavioral patterns is very important to solve problems associated with such systems. For example, in transportation networks, these emergent patterns usually dictate congestion dynamics and are poised to undergo a transformative change with the introduction of connected vehicles that can communicate with each other. Consequently, our ability to observe such patterns plays a critical role in effectively managing the transition to a smarter transportation network as well as in improving system performance and reducing congestion costs. This research seeks to answer questions about the appropriate scale at which these patterns may be best observed. Additionally, this work also seeks to assess the effect of varying penetration rates of connected vehicles on the ability to observe emergent patterns in traffic. This work has a great potential to significantly improve our ability to monitor, predict and control the occurrence of emergent congestion events. In the case of traffic flow applications, this study could help reduce worldwide congestion costs that are estimated to be in several hundreds of billions of US dollars annually. The techniques developed during this study will enhance our fundamental knowledge about how to observe the emergent behavior and use this knowledge to analyze and solve the problems associated with several other complex systems. The project also has highly innovative educational plan of creating visually appealing and lucid graphics material to engage undergraduate and graduate students, as well as the general public.The primary objective of this research project is to create a rigorous methodology to determine the spatial scale and model order required to observe and predict emergent phenomena in complex systems. In a narrower context of traffic flow, the project seeks to establish the modeling requirements for observing emergent congestion events on a multi-lane highway, and predicting such behavior with better accuracy than current prediction models. The approach will modify existing Krylov subspace-based model order reduction techniques by explicitly incorporating spatial scales into the process. More importantly, the novel contribution of this work will be the control-theoretic formulation of the renormalization group theory borrowed from the field of statistical mechanics to gain an understanding of how the observability of emergent dynamics depends on spatial scale. The research will include the study of spatial dependence of observability in complex systems in a control-theoretic setting. This work will also contribute to the study of how penetration rate (i.e., the distribution of a sensor network in a complex system) impacts the observability of emergent behavior in complex traffic flow dynamics.

在几个大型复杂系统中观察到全球紧急模式，例如运输网络，电网和金融市场。了解这些动态发展的行为模式对于解决与此类系统相关的问题非常重要。例如，在运输网络中，这些新兴模式通常决定拥塞动态，并有助于通过引入可以相互通信的连接车辆进行变革性变化。因此，我们观察到这种模式的能力在有效管理向更智能的运输网络的过渡以及改善系统性能和降低拥塞成本方面起着至关重要的作用。这项研究试图回答有关最佳观察到这些模式的适当规模的问题。此外，这项工作还旨在评估连接车辆的渗透率变化的影响对观察交通紧急模式的能力的影响。这项工作具有很大的潜力，可以显着提高我们监视，预测和控制紧急事件发生的能力。就交通流量应用程序而言，这项研究可以帮助降低全球交通拥堵成本，估计每年数十亿美元。在这项研究中开发的技术将增强我们关于如何观察新兴行为并利用这些知识来分析和解决与其他几个复杂系统相关的问题的基本知识。该项目还制定了高度创新的教育计划，可以创建视觉吸引人和清醒的图形材料，以吸引本科生和研究生以及公众。该研究项目的主要目标是创建一种严格的方法，以确定在复杂系统中观察和预测出现现象所需的空间规模和模型顺序。在交通流的狭窄背景下，该项目试图建立在多车道高速公路上观察紧急事件的建模要求，并比当前的预测模型更准确地预测这种行为。该方法将通过将空间尺度明确地纳入过程来修改现有的基于Krylov子空间的模型订购技术。更重要的是，这项工作的新贡献将是从统计力学领域借来的重新归一化群体理论的控制理论公式，以了解新兴动力学的可观察性如何取决于空间规模。该研究将包括研究在控制理论环境中复杂系统中可观察性的空间依赖性。这项工作还将有助于研究渗透率（即，在复杂系统中传感器网络的分布）如何影响复杂的交通流动动力学中新兴行为的可观察性。