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

基本信息

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

项目摘要

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，污染和能源开发的困扰，随着人口增长和城市化的过程，城市流动性仍然是城市面临的最艰巨的挑战之一。到目前为止，城市流动性表征的最常见方法集中在车辆的空间和时间位置上。但是，城市生活是城市生活的多维特征，在时空框架中，围绕人们及其需求的相互联系的基础架构和信息网络经历了纠结的层。结果，移动性的研究应超越运输系统，以客户为中心并合并到其他物理系统和网络网络中。这项对探索性研究（急切）项目的早期概念赠款是出于提高城市流动性的情境意识的需要，并向城市经理和城市居民分发可靠，及时的信息，以了解与城市流动性相关的问题。通过成功的合作，该项目旨在通过可衡量的指数开发一个新的城市流动性定义，以表征围绕公民整合运输网络，电力网络和众包数据的公民范式。预计这一渴望的项目将在与佛罗里达州塔拉哈西市合作，在全球城市团队挑战赛（GCTC）中的建立和持续努力做出贡献。研究团队已经完成了GCTC的第一阶段，这个渴望的项目将通过开发以数据驱动的方式来表征城市移动性来为第二阶段奠定基础，该方法将运输网络，电力网络，天气，天气，天气，天气收集到的数据集成在一起塔拉哈西市内的空气质量和社交媒体。这种方法将使塔拉哈西市通过可衡量的出行福利改善城市服务，并提高居民的生活质量，从而使他们成为“智能城市”的努力更近一步。这种方法将得到包括Internet2，Edd Inc.和Stantec公司在内的Active GCTC行动集群的支持，以支持Tallahassee GCTC的工作。 UHDNETCITY将能够带来可衡量的移动效益，并改善塔拉哈西（Tallahassee）居民的生活质量，以（1）降低车辆和基础设施的能源消耗，（2）减少拥塞，坠机和旅行者的挫败感，（3）改善安全性和可靠性，改善安全性和可靠性，（4）提供更简化，高效和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，以通过处理异质时空测量数据和网络模型来为实时城市移动性传感提供统一的数学基础。该数学框架将导致使用在异构城市数据源中隐藏的数据结构进行指导的监督和半监督机器学习算法之间的差距。 Uhdnetcity采用以用户为中心的游戏设计方法来鼓励居民采用塔拉哈西市开发的城市众包仪表板，例如数字移动应用程序，并促进他们参与城市流动性管理。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

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)
影响因子：
0
作者：
Lalitha Madhavi Konila Madhavi-Lalitha-Madhavi-Konila-Madhavi-65888514;Mostafa Gilanifar;Yuxun Zhou;E. Ozguven;R. Arghandeh
通讯作者：
Lalitha Madhavi Konila Madhavi-Lalitha-Madhavi-Konila-Madhavi-65888514;Mostafa Gilanifar;Yuxun Zhou;E. Ozguven;R. Arghandeh

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

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
通讯作者：
Lalitha Madhavi Konila Sriram;Mostafa Gilanifar;Yuxun Zhou;Eren Erman Ozguven;R. Arghandeh

Partial Knowledge Data-Driven Event Detection for Power Distribution Networks