Smart City Digital Twins
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ItemDevising a Game Theoretic Approach to Enable Smart City Digital Twin Analytics( 2019-01-08)Despite investments in advancing information and communications technology (ICT)-integrated infrastructure systems toward becoming Smarter Cities, cities often face a large gap between smart sustainable supply and demand. Here, we review the core concepts of ICT-integrated infrastructure systems as they pertain to developing smart and sustainable cities, and describe how a game theoretic-based digital twin of a city can enable more visibility and insight into the successful implementation of such systems. This study is a foundational step toward enabling participation of all city stakeholders (i.e., government, industry, and citizens) in the decision making process and the creation of smart sustainable cities. Engaging city stakeholders in such a manner allows for collective participation in changes, which can enable continuous adaptation toward more sustaining growth and prosperity.
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ItemPersonalize Wayfinding Information for Fire Responders based on Virtual Reality Training Data( 2019-01-08)Modern buildings with increasing complexity can cause serious difficulties for first responders in emergency wayfinding. While real-time data collection and information analytics become easier in indoor wayfinding, a new challenge has arisen: cognitive overload due to information redundancy. Standardized and universal spatial information systems are still widely used in emergency wayfinding, ignoring first responders’ individual difference in information intake. This paper proposes and tests the theoretical framework of a spatial information systems for first responders, which reflects their individual difference in information preference and helps reduce the cognitive load in line of duty. The proposed method includes the use of Virtual Reality (VR) experiments to simulate real world buildings, and the modeling of first responders’ reactions to different information formats and contents in simulated wayfinding tasks. This work is expected to set a foundation of future spatial information system that correctly and effectively responds to first responders’ needs.
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ItemA Graph-based Approach for Detecting Critical Infrastructure Disruptions on Social Media in Disasters( 2019-01-08)The objective of this paper is to propose and test a graph-based approach for detection of critical infrastructure disruptions in social media data in disasters. Understanding the situation and disruptive events of critical infrastructure is essential to effective disaster response and recovery of communities. The potential of social media data for situation awareness during disasters has been highlighted in recent studies. However, the application of social sensing in detecting disruptions of critical infrastructure is limited because existing approaches cannot provide complete and non-ambiguous situational information about critical infrastructure. Therefore, to address this methodological gap, we developed a graph-based approach including data filtering, burst time-frame detection, content similarity and graph analysis. A case study of Hurricane Harvey in 2017 in Houston was conducted to illustrate the application of the proposed approach. The findings highlighted the temporal patterns of critical infrastructure events that occurred in disasters including disruptive events and their adverse impacts on communities. The findings also provided insights for better understanding critical infrastructure interdependencies in disasters. From the practical perspective, the proposed methodology study can improve the ability of community members, first responders and decision makers to detect and respond to infrastructure disruptions in disasters.
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ItemDBL SmartCity: An Open-Source IoT Platform for Managing Large BIM and 3D Geo-Referenced Datasets( 2019-01-08)The `smart city' approach has been promoted as an effective way to manage urban environments. Information and communication technology in general, as well as `Internet of Things' systems in particular, constitute an essential component of all smart city initiatives. However, many past and current smart city implementations place only an insufficient emphasis on the geo-spatial and 3D nature of data. In order to fill this gap, we present DBL SmartCity, an open-source smart city IoT platform that is based on open standards and designed from the ground-up to effectively store, manage, and present large sets of BIM and 3D geo-referenced data.
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ItemOptimum Network of Battery Storage to Support Electric Vehicle Charging Infrastructure in Smart Cities( 2019-01-08)Smart mobility and transportation is a critical component of smart cities. One barrier to the smart transportation is a lack of charging stations that can empower a huge amount of electric vehicles, especially the autonomous one. Battery storage technology provides an opportunity; however, how battery storage can serve a crucial role in enabling fast-charging stations to fulfill customer demand and providing a profit for charging station operators is unclear. This paper reports a discrete event simulation (DES) model to determine the optimum network of battery storage system considering costs and charging stations. A case study of Detroit Area in the State of Michigan is provided to demonstrate the usage of the model. Results show that lithium-ion batteries cost the most whereas zinc-air batteries cost the least. Findings suggest that a highly condensed charging station network provide higher benefit and result in lower total cost through battery units connected to a microgrid.
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ItemSemantic Modeling of Outdoor Scenes for the Creation of Virtual Environments and Simulations( 2019-01-08)Efforts from both academia and industry have adopted photogrammetric techniques to generate visually compelling 3D models for the creation of virtual environments and simulations. However, such generated meshes do not contain semantic information for distinguishing between objects. To allow both user- and system-level interaction with the meshes, and enhance the visual acuity of the scene, classifying the generated point clouds and associated meshes is a necessary step. This paper presents a point cloud/mesh classification and segmentation framework. The proposed framework provides a novel way of extracting object information – i.e., individual tree locations and related features while considering the data quality issues presented in a photogrammetric-generated point cloud. A case study has been conducted using data that were collected at the University of Southern California to evaluate the proposed framework.
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