CRII: III: Towards Effective and Efficient City-scale Traffic Reconstruction

CRII：III：迈向有效和高效的城市规模交通重建

• 批准号: 2412340
• 负责人: Weizi Li
• 金额: $ 17.48万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2412340&HistoricalAwards=false
• 关键词: CRII; III; Towards; Effective; Efficient; CRII; III; Towards; Effective; Efficient

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Automobiles have facilitated socio-economic development and connected nearly all social sectors. However, rapid urbanization and expansion of the traffic system have caused many issues worldwide including congestion and accidents. As urbanization and vehicle production are projected to further increase in coming decades, better planning and management of traffic become imperative. Traffic is a dynamical system that propagates from one area to another in a city. So, in order to optimize it for various purposes, a holistic and systematic viewpoint of city-scale traffic dynamics is inevitable and necessary. Nevertheless, studies of this topic are currently lacking due to the limitation of traffic data and the multi-scale interpretation of the traffic system. This proposal focuses on leveraging mobile data to effectively and efficiently reconstruct city-scale traffic. The reconstructed traffic can be used to not only plan and manage urban traffic but also to predict traffic patterns by leveraging advanced traffic simulation. This project is expected to innovate in transportation and traffic research, and thus benefit people from various disciplines, including computer science, civil engineering, urban planning, earth science, and supply chain management. The accompanying educational and outreach activities include curriculum development at the intersection of Computer Science and Intelligent Transporation Systems, and research opportunities for students in underrepresented groups as well as high school students. The overall goal of this project is developing effective and efficient reconstruction methods of city-scale traffic using mobile data. First of all, the travel time of individual road segments will be estimated using the time information embedded in mobile data. With the estimated travel time, other macroscopic traffic states such as speed, flow, and density will be subsequently estimated. Second, a novel map-matching technique for generating vehicle trajectories will be developed in case of low-sampling rate mobile data. Third, simulation-based optimization will be adopted to reconstruct microscopic traffic dynamics while ensuring consistent traffic flows at the boundaries of data-sufficient and data-lacking areas. Lastly, a hybrid simulation will be explored with the aim to achieve highly-efficient traffic reconstruction through studying various ITS applications' requirements on efficiency and reconstruction fidelity, and an effective conversion method between macroscopic and microscopic traffic simulation. The proposed methods will be evaluated using both publicly available and proprietary data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项是根据2021年《美国救援计划法》的全部或部分资助的（公共法117-2）。自动行动已促进了社会经济发展，并几乎将所有社会部门联系起来。但是，交通系统的快速城市化和扩张引起了全球许多问题，包括拥塞和事故。随着城市化和车辆生产预计将在未来几十年内进一步增加，对流量的更好计划和管理变得必须进行。流量是一个动态系统，在一个城市中从一个区域传播到另一个区域。因此，为了为各种目的进行优化，城市规模的交通动态的整体和系统的观点是不可避免的和必要的。然而，由于流量数据的局限性以及对流量系统的多尺度解释，目前缺乏对该主题的研究。该提案的重点是利用移动数据有效地重建城市规模的流量。重建的流量不仅可以用来计划和管理城市流量，还可以通过利用高级流量模拟来预测流量模式。预计该项目将在运输和交通研究中进行创新，从而使来自各个学科的人们受益，包括计算机科学，土木工程，城市规划，地球科学和供应链管理。随附的教育和外展活动包括在计算机科学和智能转运系统的交汇处开发课程，以及为人数不足的群体以及高中生的研究机会。该项目的总体目标是使用移动数据开发有效，有效的城市级交通方法。首先，将使用移动数据中嵌入的时间信息估算单个道路段的旅行时间。随着估计的旅行时间，其他宏观交通状态（例如速度，流量和密度）将随后估算。其次，在低采样率移动数据的情况下，将开发一种用于生成车辆轨迹的新型地图匹配技术。第三，将采用基于仿真的优化来重建微观流量动态，同时确保在数据填充和数据占用的领域的边界保持一致的流量。最后，将探索混合模拟，目的是通过研究其对效率和重建忠诚度的各种应用要求，以及宏观和微观交通模拟之间的有效转换方法来实现高效的交通重建。提出的方法将使用公开可用和专有数据进行评估。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持的。