Visible to the public UHDNetCity: User-centered Heterogeneous Data Fusion for Multi-networked City MobilityConflict Detection Enabled

Project Details
Lead PI:Reza Arghandeh
Co-PI(s):Jinghui Hou
Eren Ozguven
Performance Period:09/01/16 - 02/28/19
Institution(s):Florida State University
Sponsor(s):National Science Foundation
Award Number:1640587
375 Reads. Placed 480 out of 803 NSF CPS Projects based on total reads on all related artifacts.
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.