CPS: Synergy: Collaborative Research: Mapping and Querying Underground Infrastructure Systems

CPS：协同：协作研究：测绘和查询地下基础设施系统

• 批准号: 1823279
• 负责人: Goce Trajcevski
• 金额: $ 15.85万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-16 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1823279&HistoricalAwards=false
• 关键词: CPS; Synergy; Collaborative; Research; Mapping

One of the challenges toward achieving the vision of smart cities is improving the state of the underground infrastructure. For example, large US cities have thousands of miles of aging water mains, resulting in hundreds of breaks every year, and a large percentage of water consumption that is unaccounted for. The goal of this project is to develop models and methods to generate, analyze, and share data on underground infrastructure systems, such as water, gas, electricity , and sewer networks. The interdisciplinary team of investigators from the University of Illinois at Chicago, Brown University, and Northwestern University will leverage partnerships with the cities of Chicago and Evanston, Illinois, to make the approach and findings relevant to their stakeholders. Research results will be incorporated in courses at the three institutions. Outreach efforts include events for K-12 students to develop awareness about underground infrastructure from a data and computational perspective. The results of the project will ultimately help municipalities maintain and renovate civil infrastructure in a more effective manner.Cities are cyber-physical systems on a grand scale, and developing a precise knowledge of their infrastructure is critical to building a foundation for the future smart city. This proposal takes an information centric approach based on the complex interaction among thematic data layers to developing, visualizing, querying, analyzing, and providing access to a comprehensive representation of the urban underground infrastructure starting from incomplete and imprecise data. Specifically, the project has the following main technical components: (1) Generation of accurate GIS-based representations of underground infrastructure systems from paper maps, CAD drawings, and other legacy data sources; (2) Visualization of multi-layer networks combining schematic overview diagrams with detailed geometric representations; (3) Query processing algorithms for integrating spatial, temporal, and network data about underground infrastructure systems; (4) Data analytics spanning heterogeneous geospatial data sources and incorporating uncertainty and constraints; (5) Selective access to stakeholders on a need-to-know basis and facilitating data sharing; and (6) Evaluation in collaboration with the cities of Chicago and Evanston.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.

实现智慧城市愿景的挑战之一是改善地下基础设施的状况。例如，美国大城市有数千英里的老化水管，导致每年数百次中断，以及很大比例的用水量无法计算。该项目的目标是开发模型和方法，以生成，分析和共享地下基础设施系统的数据，如水，天然气，电力和下水道网络。来自伊利诺伊大学芝加哥分校、布朗大学和西北大学的跨学科研究团队将利用与芝加哥和伊利诺伊州埃文斯顿市的合作伙伴关系，使研究方法和研究结果与利益相关者相关。研究成果将纳入这三个机构的课程。外联工作包括为K-12学生举办活动，从数据和计算的角度提高对地下基础设施的认识。该项目的成果将最终帮助市政当局以更有效的方式维护和翻新民用基础设施。城市是一个大规模的网络物理系统，准确了解其基础设施对于为未来的智慧城市奠定基础至关重要。该提案采用以信息为中心的方法，基于专题数据层之间的复杂交互，从不完整和不精确的数据出发，开发，可视化，查询，分析和提供城市地下基础设施的全面表示。具体而言，该项目有以下主要技术组成部分：（1）从纸质地图、CAD图纸和其他遗留数据源生成地下基础设施系统的精确GIS表示;（2）将示意性概图与详细几何表示相结合的多层网络可视化;（3）集成地下基础设施系统的空间、时间和网络数据的查询处理算法;（4）数据分析跨越各种地理空间数据源，并纳入不确定性和制约因素;（5）在需要了解的基础上有选择地接触利益攸关方，并促进数据共享;以及（6）与芝加哥和埃文斯顿市合作进行评估。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Semi-supervised Trajectory Understanding with POI Attention for End-to-End Trip Recommendation

• DOI: 10.1145/3378890
• 发表时间: 2020-02
• 期刊: ACM Transactions on Spatial Algorithms and Systems (TSAS)
• 作者: Fan Zhou;Hantao Wu;Goce Trajcevski;A. Khokhar;Kunpeng Zhang

LaCAVR: Load and Constraints Aware Vehicle Rerouting

LaCAVR：负载和约束感知车辆重新路由

• DOI: 10.1109/mdm.2019.00-32
• 发表时间: 2019
• 作者: Bis, David;Bix, Noah;Gruman, Benjamin;Guenette, Sam;Hauge, Adam;Moser, Hannah;Paul, Jimmy;Trajcevski, Goce
• 通讯作者: Trajcevski, Goce

Disentangled Network Alignment with Matching Explainability

• DOI: 10.1109/infocom.2019.8737411
• 发表时间: 2019-04
• 期刊: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
• 作者: Fan Zhou;Zijing Wen;Goce Trajcevski;Kunpeng Zhang;Ting Zhong;Fang Liu

CET-LATS: Compressing Evolution of TINs from Location Aware Time Series

CET-LATS：根据位置感知时间序列压缩 TIN 的演化

• DOI: 10.1145/3397536.3422352
• 发表时间: 2020
• 作者: Giri, Prabin;Hashemi, Hooman;Gossling, Evan;Guo, Jason T;Shah, Koshal P;Trajcevski, Goce
• 通讯作者: Trajcevski, Goce

Adversarial Point-of-Interest Recommendation

• DOI: 10.1145/3308558.3313609
• 发表时间: 2019-05
• 期刊: The World Wide Web Conference
• 作者: Fan Zhou;Ruiyang Yin;Kunpeng Zhang;Goce Trajcevski;Ting Zhong;Jin Wu