Vehicular Traffic Modeling and Control in Mixed Manual and Automated Environments

混合手动和自动环境中的车辆交通建模和控制

• 批准号: 1536599
• 负责人: Soyoung Ahn
• 金额: $ 39.39万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1536599&HistoricalAwards=false
• 关键词: Vehicular; Traffic; Modeling; Control; Mixed

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.

互联汽车和自动驾驶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会。这些技术可以从根本上改变驾驶员的互动，并具有巨大的潜力来纠正已知对交通效率和稳定性有害的交通现象。在不同类型的自动驾驶汽车技术中，协同自适应巡航控制由于其独特的能力而具有特别的优势，可以提高高性能，使道路容量增加一倍（或更多），并显着提高流动稳定性。该研究项目旨在阐明手动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。该项目将开展一系列综合研究、教育和外联活动，将从这一研究中获得的知识传播给更广泛的受众。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。交通中断（大范围拥堵的开始）经常在高速公路瓶颈附近的合并和交织处触发。这种现象的特征在于故障前的高流量，随后是瓶颈放电率的显著降低（称为“容量下降”）。本研究的目标是：（1）阐明在混合手动和协同自适应巡航控制启用车辆环境中的瓶颈处的交通故障的行为机制，以及（2）开发理论基础的控制策略，以减轻交通故障和容量下降。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）并最终与宏观特征（故障流）联系起来进行多尺度分析和建模。这项研究将推动自动驾驶汽车时代交通流研究的前沿。这项研究的结果将推进我们的拥塞机制的知识，特别是围绕扩展合并和编织瓶颈。此外，控制策略将基于对驾驶行为的基本理解来开发，以通过基于车辆的控制来有效地减轻交通中断。将在不同的复杂程度上开发主动和被动策略，以实现对选定自动化级别的鲁棒控制。