Award Abstract # 1640587
UHDNetCity: User-centered Heterogeneous Data Fusion for Multi-networked City Mobility

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: FLORIDA STATE UNIVERSITY
Initial Amendment Date: August 19, 2016
Latest Amendment Date: August 1, 2018
Award Number: 1640587
Award Instrument: Standard Grant
Program Manager: David Corman
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2016
End Date: February 28, 2019 (Estimated)
Total Intended Award Amount: $233,123.00
Total Awarded Amount to Date: $233,123.00
Funds Obligated to Date: FY 2016 = $233,123.00
History of Investigator:
  • Eren Ozguven (Principal Investigator)
    eozguven@eng.famu.fsu.edu
  • Laura Arpan (Co-Principal Investigator)
  • Reza Arghandeh (Former Principal Investigator)
  • Jinghui Hou (Former Co-Principal Investigator)
  • Eren Ozguven (Former Co-Principal Investigator)
Recipient Sponsored Research Office: Florida State University
874 TRADITIONS WAY
TALLAHASSEE
FL  US  32306-0001
(850)644-5260
Sponsor Congressional District: 02
Primary Place of Performance: FSU Center for Advanced Power Systems
2000 Levy Avenue
Tallahassee
FL  US  32310-5792
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): JF2BLNN4PJC3
Parent UEI:
NSF Program(s): Special Projects - CNS
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 042Z, 7916
Program Element Code(s): 171400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 11)
Cordova, Jose and Arghandeh, Reza and Zhou, Yuxun and Wesolowski, Sergiusz and Wu, Wei and Matthias, Stifter "Shape-based data analysis for event classification in power systems" 2017 IEEE Manchester PowerTech , 2017 10.1109/PTC.2017.7980950 Citation Details
Cordova, Jose and Soto, Carlos and Gilanifar, Mostafa and Zhou, Yuxun and Srivastava, Anuj and Arghandeh, Reza "Shape Preserving Incremental Learning for Power Systems Fault Detection" IEEE Control Systems Letters , v.3 , 2019 10.1109/LCSYS.2018.2852064 Citation Details
Kocatepe, Ayberk and Ulak, Mehmet Baran and Kakareko, Grzegorz and Ozguven, Eren Erman and Jung, Sungmoon and Arghandeh, Reza "Measuring the accessibility of critical facilities in the presence of hurricane-related roadway closures and an approach for predicting future roadway disruptions" Natural Hazards , v.95 , 2019 10.1007/s11069-018-3507-5 Citation Details
Kocatepe, Ayberk and Ulak, Mehmet Baran and Ozguven, Eren Erman and Horner, Mark W. and Arghandeh, Reza "Socioeconomic characteristics and crash injury exposure: A case study in Florida using two-step floating catchment area method" Applied Geography , v.87 , 2017 10.1016/j.apgeog.2017.08.005 Citation Details
Kocatepe, Ayberk and Ulak, Mehmet Baran and Sriram, Lalitha Madhavi and Pinzan, Davide and Ozguven, Eren Erman and Arghandeh, Reza "Co-resilience Assessment of Hurricane-induced Power Grid and Roadway Network Disruptions: A Case Study in Florida with a Focus on Critical Facilities" 2018 21st International Conference on Intelligent Transportation Systems (ITSC) , 2018 10.1109/ITSC.2018.8569574 Citation Details
Konila Sriram, Lalitha Madhavi and Gilanifar, Mostafa and Zhou, Yuxun and Erman Ozguven, Eren and Arghandeh, Reza "Causal Markov Elman Network for Load Forecasting in Multinetwork Systems" IEEE Transactions on Industrial Electronics , v.66 , 2019 10.1109/TIE.2018.2851977 Citation Details
Konila Sriram, Lalitha Madhavi and Ulak, Mehmet Baran and Ozguven, Eren Erman and Arghandeh, Reza "Multi-Network Vulnerability Causal Model for Infrastructure Co-Resilience" IEEE Access , v.7 , 2019 https://doi.org/10.1109/ACCESS.2019.2904457 Citation Details
Madhavi, K. S. and Cordova, Jose and Ulak, Mehmet Baran and Ohlsen, Michael and Ozguven, Eren E. and Arghandeh, Reza and Kocatepe, Ayberk "Advanced electricity load forecasting combining electricity and transportation network" 2017 North American Power Symposium (NAPS) , 2017 10.1109/NAPS.2017.8107312 Citation Details
Madhavi, K. S. and Gilanifar, Mostafa and Zhou, Yuxun and Ozguven, Eren E. and Arghandeh, Reza "Multivariate Deep Causal Network for Time Series Forecasting in Interdependent Networks" 2018 IEEE Conference on Decision and Control (CDC) , 2018 10.1109/CDC.2018.8619668 Citation Details
Ulak, Mehmet B. and Kocatepe, A. and Konila Sriram, Lalitha M. and Ozguven, Eren E. and and Arghandeh, R. "Assessment of the hurricane-induced power outages from a demographic, socioeconomic, and transportation perspective" Natural hazards , v.92 , 2018 10.1007/s11069-018-3260-9 Citation Details
Zhou, Yuxun and Arghandeh, Reza and Spanos, Costas J. "Partial Knowledge Data-driven Event Detection for Power Distribution Networks" IEEE Transactions on Smart Grid , 2017 10.1109/TSG.2017.2681962 Citation Details
(Showing: 1 - 10 of 11)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This project helped in addressing the emerging need for a modern conceptualization of urban mobility and urban activities including networked interactions between cities and citizens through analogies that are derived from network flows, data processing, and causality analysis. In essence, urban mobility is a multidimensional characteristic of cities, experienced as layers of interconnected infrastructures, places, people, and information. Similarly, emergency resilience is also mostly considered as a single dimension attribute of a system although it has a multi-domain nature. However, both the study of mobility and resilience should examine all infrastructure systems, weather and environment and information networks. As such, the project developed two novel concepts, namely “co-mobility” and “co-resilience” in order to characterize the mobility and resilience in a spatio-temporal and multi-network scheme, respectively. This characterization provides a unified data-driven mathematical foundation for urban mobility and resilience management.

This foundation was supported by assessing citizens’ perceptions of smart city apps as well as several motivations and barriers that predicted citizens’ intentions to adopt and use smart city apps to report problems such as power outages and roadway blockages. Through a qualitative focus group study and a quantitative survey with a diverse pool of participants of various age groups and socio-economic status as well as city experts, these motivations and challenges were analyzed extensively. Findings indicated the facilitating features (e.g., convenience, ease of use) that drive the adoption of smart city app usage as well as barriers (e.g., privacy concerns, unwanted notifications) that might hinder their adoption and continued usage. As much as the concept of smart city technology is exciting and promising, findings indicate that developers and city governments need to promote smart city app benefits and address barriers before their full potential to improve a city can be realized.    

The research team hopes the assimilation of the findings of this project will provide municipal utilities with a new way to use information to help them better predict the fluctuating power needs of their community and recognize opportunities to improve traffic flow. By helping municipal utilities adopt strategies to help them become more efficient in providing power to residents, this will ultimately lower costs and provide a better experience for their community. The developed models also show potential for improvement in emergency resilience. Researchers hope the knowledge of the co-resilience approach will help emergency agencies and other relevant departments accurately predict high risk locations with respect to fallen trees, power outages and roadway closures.

Findings from the survey also indicated that citizens used a variety of channels (e.g., phone, web site, e-mail, smart city app) in order to report problems they experienced (e.g., power outage or blocked roadway) during and after two hurricanes. Importantly, citizens’ satisfaction with the local government’s response to their reported problems was dependent on the channel they used to report the problem/s. These findings can be used by Florida and its communities to develop better crisis communication and storm-emergency plans that fit the diverse needs of the populations and allow for less-stressful evacuations, assist with effective sheltering and, ultimately, save lives.

City governments and communities were engaged through meetings where appropriate, allowing expert and public perspective and perception on the findings of this research. In order to maximize the educational impact of the project, the research team actively engaged high school students through FSU’s Young Scholars Program as well as undergraduate and graduate students from multiple disciplines (civil engineering, electrical engineering, and communications) in data analysis and modeling. This research exposed these students to many mobility and resilience issues, which range from accessibility and safety to information dissemination to public and resilience of infrastructure networks. These activities led to research publications which were published in peer-reviewed journals and presented at conferences such as Transportation Research Board (TRB), National Communication Association (NCA) and IEEE conferences.

 


Last Modified: 04/22/2019
Modified by: Eren E Ozguven

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