Vehicular Traffic Modeling and Control in Mixed Manual and Automated Environments

互联汽车和自动化汽车等尖端汽车技术为大幅改善交通运营和安全提供了前所未有的机遇。这些技术可以从根本上改变司机之间的互动，并具有巨大的潜力来补救已知的损害交通效率和稳定性的交通现象。在不同类型的自动化车辆技术中，协作式自适应巡航控制系统尤其具有优势，因为它具有独特的能力，可以促进高性能，使道路通行能力翻倍(或更多)，并显著提高流量稳定性。这项研究项目旨在阐明人工和高性能自动化车辆混合流中的交通拥堵机制。它还旨在制定缓解战略，以前所未有的水平减少交通拥堵，为国家的经济竞争力和可持续城市发展做出贡献。该项目将参与一系列综合研究、教育和外联活动，将从这项研究中获得的知识扩大到更广泛的受众。这些活动包括(I)开发基于模拟的教育模块，(Ii)使用驾驶模拟器传播结果，以及(Iii)让本科生和研究生参与研究和教育。交通崩溃(广泛拥堵的开始)通常发生在合并和交织附近的高速公路瓶颈处。这种现象的特点是先有高流量，然后是瓶颈排泄率的显著降低(称为“能力下降”)。这项研究的目的是：(1)阐明混合手动和协作式自适应巡航控制车辆环境中瓶颈处交通崩溃的行为机制；(2)开发理论上有基础的控制策略，以缓解交通崩溃和容量下降。为了更好地理解这些机制并实现基于车辆的控制，本研究将通过将微观特征(驾驶员特征、车道变化)与中观特征(车辆排队)以及最终与宏观特征(故障流)联系起来，进行多尺度分析和建模。这一研究将推动自动化车辆时代交通流研究的前沿。这项研究的结果将促进我们对拥塞机制的了解，特别是在扩展合并和编织瓶颈方面。此外，将根据对驾驶行为的基本理解制定控制策略，通过基于车辆的控制有效地缓解交通故障。将在不同的复杂程度上制定前瞻性和反应性战略，以便能够对选定的自动化水平实施强有力的控制。

Cutting-edge vehicle technologies such as Connected Vehicles and Automated Vehicles present unprecedented opportunities to drastically improve traffic operations and safety. These technologies can fundamentally change driver interactions and have enormous potential to remedy traffic phenomena known to be detrimental to traffic efficiency and stability. Among different types of Automated Vehicles technologies, Cooperative Adaptive Cruise Control is particularly advantageous due to its unique ability to foster high performance, doubling (or more) roadway capacity and significantly improving flow stability. This research project seeks to shed light on the traffic congestion mechanisms in mixed streams of manual and high performance automated vehicles. It also aims to develop mitigation strategies to reduce traffic congestion by unprecedented levels, contributing to the nation's economic competitiveness and sustainable urban development. This project will engage in a range of integrated research, educational and outreach activities that will expand the knowledge obtained from this research to a broader audience. The activities include (i) developing simulation-based educational modules, (ii) disseminating results using driving simulator, and (iii) engaging undergraduate and graduate students in the research and education.Traffic breakdown (onset of wide-spread congestion) is often triggered at freeway bottlenecks near merges and weaves. This phenomenon is characterized by high flow prior to breakdown, succeeded by a significant reduction in bottleneck discharge rate (known as "capacity drop"). The objectives of this research are to: (1) shed light on the behavioral mechanisms underlying traffic breakdown at bottlenecks in mixed manual and Cooperative Adaptive Cruise Control enabled vehicular environments and (2) develop theoretically-grounded control strategies to mitigate traffic breakdown and capacity drop. To better understand these mechanisms and enable vehicle-based control, this research will perform multi-scale analysis and modeling by linking microscopic features (driver characteristics, lane changes) to mesoscopic features (vehicle platooning) and eventually to macroscopic features (breakdown flow). This research will push the frontier of traffic flow research in the era of automated vehicles. Results from this research will advance our knowledge of congestion mechanisms, particularly around extended merge and weave bottlenecks. Moreover, control strategies will be developed based on the fundamental understanding of driving behavior to effectively mitigate traffic breakdown via vehicle-based control. Both proactive and reactive strategies will be developed at different levels of sophistication to enable implementation of robust control for select automation levels.