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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。汽车促进了社会经济发展，几乎连接了所有社会部门。然而，快速的城市化和交通系统的扩张在世界范围内造成了许多问题，包括拥堵和事故。随着城市化和汽车生产预计在未来几十年进一步增长，更好的交通规划和管理变得势在必行。交通是一个动态系统，在城市中从一个区域传播到另一个区域。因此，对城市尺度的交通动力学进行整体的、系统的观点是必然的，也是必要的，这样才能使其达到多方面的优化。然而，由于交通数据的局限性和交通系统的多尺度解释，目前对这一课题的研究还很匮乏。这项提议的重点是利用移动数据来有效和高效地重建城市规模的交通。重建的交通不仅可以用于规划和管理城市交通，还可以利用先进的交通模拟来预测交通模式。该项目预计将在交通研究方面进行创新，从而使计算机科学、土木工程、城市规划、地球科学和供应链管理等多个学科的人们受益。随之而来的教育和外展活动包括在计算机科学和智能交通系统的交叉点制定课程，以及为代表性不足的群体的学生以及高中生提供研究机会。该项目的总体目标是利用移动数据开发有效和高效的城市规模交通重建方法。首先，利用移动数据中嵌入的时间信息来估计单个路段的行驶时间。根据估计的行驶时间，随后将估计其他宏观交通状态，如速度、流量和密度。其次，开发了一种新的地图匹配技术，用于在低采样率移动数据的情况下生成车辆轨迹。第三，采用基于模拟的优化方法重建微观交通动力学，同时确保数据充足和数据缺乏区域边界的交通流量一致。最后，通过研究各种ITS应用对效率和重构逼真度的要求，以及宏观和微观交通仿真之间的有效转换方法，探索了一种混合仿真的方法，以实现高效的交通重构。建议的方法将使用公开可用的和专有的数据进行评估。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.