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

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性的挑战。除了明显的能源和环境影响外，交通拥堵还给社会带来了有形的代价。在当前的经济和环境气候下，传统的以物质为中心的缓解战略本身不太可能成功或可持续。通过将传感、信息和通信技术整合到交通基础设施和车辆中，已经提出了许多构建智能交通系统(ITS)的策略。在这个急切的提案中，我们提出了一个早期的概念探索，以研究智能交通系统的创新和变革性方法。我们预计，这项探索性研究将通过为交通管理引入紧密集成的实时交通模拟、评估和可视化来推动下一代智能交通支持系统的发展。我们正在开发用于实时流量估计、交通重构和可视化的新型混合方法，并设计GPU和多核算法来加速整体性能。如果研究成功，这项研究将通过算法镜头实现车辆诱导和导航辅助的自适应路径规划，以缓解交通拥堵。拟议的统一框架还有可能为各种应用提供计算进步，包括监管交通、改进城市规划、交通系统设计、虚拟旅游、教育、娱乐、监控和应急响应。这套教学和推广活动通过综合教育-研究方案和有效传播研究成果来补充和扩大研究的影响。