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: 1646395
• 负责人: Maria Cruz
• 金额: $ 42万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646395&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.

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老化的水管，每年导致数百次断裂，并且很大一部分用水量下落不明。该项目的目标是开发模型和方法来生成、分析和共享地下基础设施系统（例如水、天然气、电力和下水道网络）的数据。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小组将利用与芝加哥市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其利益相关者相关。研究成果将纳入这三个机构的课程中。外展工作包括为 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。该项目的成果最终将帮助市政当局以更有效的方式维护和改造民用基础设施。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。该提案采用基于主题数据层之间复杂交互的以信息为中心的方法，从不完整和不精确的数据开始开发、可视化、查询、分析和提供对城市地下基础设施的全面表示。具体来说，该项目具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基于 GIS 的地下基础设施系统的精确表示； (2) 将示意性概览图与详细几何表示相结合的多层网络可视化； (3) 整合地下基础设施系统空间、时间和网络数据的查询处理算法； (4) 跨越异构地理空间数据源并纳入不确定性和约束的数据分析； (5) 在需要了解的基础上有选择地接触利益相关者并促进数据共享； (6) 与芝加哥市和埃文斯顿市合作进行评估。

项目成果

Facet Annotation Using Reference Knowledge Bases

• DOI: 10.1145/3178876.3186020
• 发表时间: 2018-04
• 期刊: Proceedings of the 2018 World Wide Web Conference
• 作者: R. Porrini;M. Palmonari;I. Cruz

Attitudes on Autonomous Vehicle Adoption using Interpretable Gradient Boosting Machine

• DOI: 10.1177/0361198119857953
• 发表时间: 2019-06
• 期刊: Transportation Research Record
• 影响因子: 1.7
• 作者: Dongwoo Lee;John Mulrow;Chana Joanne Haboucha;S. Derrible;Y. Shiftan

Mechanisms of Network Formation in the Public Sector: A Systematic Review of the Literature

• DOI: 10.1093/ppmgov/gvaa017
• 发表时间: 2020-08
• 期刊: Perspectives on Public Management and Governance
• 影响因子: 3.2
• 作者: Michael D. Siciliano;Weijie Wang;Alejandra Medina

Results of AML participation in OAEI 2018

• 发表时间: 2018
• 作者: Daniel Faria;Catia Pesquita;B. Balasubramani;Teemu Tervo;David Carriço;Rodrigo Garrilha;Francisco M. Couto;I. Cruz

GUIDES: Geospatial Urban Infrastructure Data Engineering Solutions

指南：地理空间城市基础设施数据工程解决方案

• DOI: 10.1145/3139958.3139968
• 发表时间: 2017
• 期刊: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
• 作者: Balasubramani, B. S.;Belingheri, O.;Boria, E. S.;Cruz, I. F.;Derrible, S.;Siciliano, M. D.
• 通讯作者: Siciliano, M. D.