UHDNetCity: User-centered Heterogeneous Data Fusion for Multi-networked City Mobility

UHDNetCity：以用户为中心的异构数据融合，实现多网络城市移动性

• 批准号: 1640587
• 负责人: Eren Ozguven
• 金额: $ 23.31万
• 依托单位: Florida State University
• 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 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1640587&HistoricalAwards=false
• 关键词: UHDNetCity; User; centered; Heterogeneous; Data

As more of the world's cities suffer from congestion, pollution, and energy exploitation, urban mobility remains one of the toughest challenges that cities face as the process of population growth and urbanization continues. So far, the most common approach for urban mobility characterization focuses on vehicle's spatial and temporal positions. However, urban mobility is a multidimensional characteristic of the city life, experienced as tangled layers of interconnected infrastructures and information networks around people and their needs in a spatio-emporal frame. As a result, the study of mobility should go beyond transportation systems, be customer-centered and merged into other physical systems and cyber networks. This Early-concept Grant for Exploratory Research (EAGER) project is motivated by the need to increase the situational awareness in urban mobility and distribute reliable and timely information to city managers and city residents about issues associated with urban mobility. Through successful collaboration, this project aims to develop a new definition of urban mobility with measurable indices to characterize the urban mobility paradigm around citizens integrating transportation networks, electricity networks, and crowdsourced data. This EAGER project is expected to contribute to the team's established and ongoing effort in the Global City Teams Challenge (GCTC) in collaboration with the City of Tallahassee, Florida. The research team has completed the first phase of the GCTC, and this EAGER project will lay the foundation for the second phase by developing a data-driven approach to characterize urban mobility, which integrates collected data from the transportation network, electricity network, weather, air quality and social media within the City of Tallahassee. This approach will put the City of Tallahassee one step closer in their efforts towards being a "smart city" by improving the city services through measurable mobility benefits, and enhance the quality of life for residents. This approach will be supported by the active GCTC action cluster including Internet2, EDD Inc., and StanTec companies to support the Tallahassee GCTC efforts. The UHDNetCity will be able to bring measurable mobility benefits and improve Tallahassee resident's quality of life in terms of (1) lowering energy consumption by vehicles and infrastructure, (2) reducing congestion, crashes and traveler frustration, (3) improving safety and reliability, and (4) providing a more streamlined, efficient and cost-effective system to operate and maintain city service networks. The UHDNetCity framework combines data fusion, signal processing, and machine learning, to provide a unified mathematical foundation for real-time urban mobility sensing by processing heterogeneous spatio-temporal measurement data and network models. This mathematical framework will lead to bridging the gap between supervised, and semi-supervised machine learning algorithms for urban mobility characterization using hidden data structures in the heterogeneous urban data sources. The UHDNetCity employs a user-driven play-centric design approach to encourage resident's adoption of the urban crowdsourcing dashboards such as DigiTally mobile app developed by the City of Tallahassee and promotes their engagement in the urban mobility management.

随着世界上越来越多的城市遭受拥堵、污染和能源开采的困扰，随着人口增长和城市化进程的继续，城市交通仍然是城市面临的最严峻挑战之一。到目前为止，最常见的方法来描述城市的流动性集中在车辆的空间和时间位置。然而，城市流动性是城市生活的一个多维度特征，在空间-时间框架中围绕着人们及其需求的相互关联的基础设施和信息网络的复杂层次。因此，对移动性的研究应该超越交通系统，以客户为中心，并融入其他物理系统和网络。EAGER项目的动机是需要提高城市交通的态势感知，并向城市管理者和城市居民提供有关城市交通问题的可靠和及时的信息。通过成功的合作，该项目旨在制定一个新的城市流动性定义，并提供可衡量的指标，以描述围绕公民整合交通网络，电力网络和众包数据的城市流动性范式。这个EAGER项目预计将有助于该团队与佛罗里达塔拉哈西市合作，在全球城市团队挑战赛（GCTC）中建立和正在进行的努力。该研究团队已经完成了GCTC的第一阶段，EAGER项目将通过开发一种数据驱动的方法来描述城市移动性，从而为第二阶段奠定基础，该方法整合了从塔拉哈西市的交通网络，电力网络，天气，空气质量和社交媒体收集的数据。这一方法将使塔拉哈西市通过可衡量的流动性效益改善城市服务，并提高居民的生活质量，从而更接近成为“智慧城市”的努力。这一方法将得到包括Internet 2、EDD Inc.、和斯坦泰克公司支持塔拉哈西GCTC的努力。UHDNetCity将能够带来可衡量的流动性效益，并在以下方面改善塔拉哈西居民的生活质量：（1）降低车辆和基础设施的能源消耗，（2）减少拥堵，碰撞和旅行者的挫折感，（3）提高安全性和可靠性，以及（4）提供更精简，高效和具有成本效益的系统来运营和维护城市服务网络。UHDNetCity框架结合了数据融合、信号处理和机器学习，通过处理异构时空测量数据和网络模型，为实时城市移动性感知提供统一的数学基础。这种数学框架将导致弥合监督和半监督机器学习算法之间的差距差距，在异构城市数据源中使用隐藏的数据结构来表征城市移动性。UHDNetCity采用以用户为导向、以游戏为中心的设计方法，鼓励居民采用城市众包仪表板，如塔拉哈西市开发的DigiTally移动的应用程序，并促进他们参与城市交通管理。

项目成果

Shape Preserving Incremental Learning for Power Systems Fault Detection

用于电力系统故障检测的保形增量学习

• DOI: 10.1109/lcsys.2018.2852064
• 发表时间: 2019
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Cordova, Jose;Soto, Carlos;Gilanifar, Mostafa;Zhou, Yuxun;Srivastava, Anuj;Arghandeh, Reza
• 通讯作者: Arghandeh, Reza

Measuring the accessibility of critical facilities in the presence of hurricane-related roadway closures and an approach for predicting future roadway disruptions

• DOI: 10.1007/s11069-018-3507-5
• 发表时间: 2019-02-01
• 期刊: NATURAL HAZARDS
• 影响因子: 3.7
• 作者: Kocatepe, Ayberk;Ulak, Mehmet Baran;Arghandeh, Reza
• 通讯作者: Arghandeh, Reza

Multivariate Deep Causal Network for Time Series Forecasting in Interdependent Networks

• DOI: 10.1109/cdc.2018.8619668
• 发表时间: 2018-12
• 期刊: 2018 IEEE Conference on Decision and Control (CDC)
• 作者: Lalitha Madhavi Konila Madhavi-Lalitha-Madhavi-Konila-Madhavi-65888514;Mostafa Gilanifar;Yuxun Zhou;E. Ozguven;R. Arghandeh

Partial Knowledge Data-Driven Event Detection for Power Distribution Networks

• DOI: 10.1109/tsg.2017.2681962
• 发表时间: 2018-09
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: Yuxun Zhou;R. Arghandeh;C. Spanos

Causal Markov Elman Network for Load Forecasting in Multinetwork Systems

• DOI: 10.1109/tie.2018.2851977
• 发表时间: 2019-02
• 期刊: IEEE Transactions on Industrial Electronics
• 影响因子: 7.7
• 作者: Lalitha Madhavi Konila Sriram;Mostafa Gilanifar;Yuxun Zhou;Eren Erman Ozguven;R. Arghandeh