EAGER: Interactive Reconstruction and Visualization of Metropolitan-Scale Traffic

EAGER：大都市规模交通的交互式重建和可视化

• 批准号: 1247456
• 负责人: Ming Lin
• 金额: $ 10万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247456&HistoricalAwards=false
• 关键词: EAGER; Interactive; Reconstruction; Visualization; Metropolitan; EAGER; Interactive; Reconstruction; Visualization; Metropolitan

Traffic congestion is a global challenge. Besides the obvious energy and environmental impacts, traffic congestion imposes tangible costs on society. It is unlikely that traditional physically-centered mitigation strategies by themselves will be successful or sustainable in the current economical and environmental climate. Numerous strategies have been proposed to construct Intelligent Transportation Systems (ITS), by incorporating sensing, information, and communication technologies in transportation infrastructure and vehicles. In this EAGER proposal, we present an early-concept exploration to investigate an innovative and transformative approach for ITS. We envision that this exploratory research could advance the next generation of ITSs by introducing a tightly-integrated real-time traffic simulation, estimation, and visualization for traffic management. We are developing novel hybrid methods for real-time flow estimation, traffic reconstruction and visualization, as well as designing GPU and many-core algorithms to accelerate the overall performance.If successful, this research could enable adaptive route planning for vehicle guidance and navigational aid to alleviate traffic congestion through an algorithmic lens. The proposed unified framework also has the potential to provide computational advances for diverse applications, including regulating traffic, improved urban planning, transportation system design, virtual tourism, education, entertainment, surveillance, and emergency response. The set of pedagogical and outreach activities complement and extend the research impact through integrated education-research programs and effective dissemination of research results.

交通拥堵是一个全球挑战。 除了明显的能源和环境影响外，交通拥堵还对社会施加了切实的成本。在当前的经济和环境环境中，传统的以身体为中心的缓解策略不可能成功或可持续。已经提出了许多策略，通过将传感，信息和通信技术纳入运输基础设施和车辆来构建智能运输系统（ITS）。 在这项渴望的建议中，我们提出了早期概念探索，以研究其创新和变革性的方法。 我们设想，这项探索性研究可以通过引入紧密集成的实时交通仿真，估算和可视化来推动下一代ITS。 我们正在开发用于实时流量估算，交通重建和可视化的新型混合方法，以及设计GPU和多核算法以促进整体性能。如果成功，这项研究可以通过算法的算法透镜来实现自适应路线计划，以实现车辆指导和导航援助，以减轻交通拥堵。 拟议的统一框架还有可能为各种应用程序提供计算进步，包括调节交通，改善城市规划，运输系统设计，虚拟旅游，教育，娱乐，监视和紧急响应。 一系列教学和外展活动通过综合教育研究计划以及有效传播研究结果来补充研究的影响。