Data Sets, Modeling, and Decision Making in Smart Cities

Ma, Meiyi; Preum, Sarah Masud; Ahmed, Mahmuda; Tärneberg, William; Hendawi, Abdeltawab; Stankovic, John A.

doi:10.1145/3355283

Cited by 42 publications

(23 citation statements)

References 58 publications

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“…DT uses are the standard building blocks upon which a standard common language is founded and subsequently used to document and detail the DT-user interactions in a standard format to facilitate their publishing, sharing of knowledge and avoid miscommunication or confusion amongst DT actors and stakeholders across the DT market. Similar to DT uses in the realm of DTs are the methodologies for modelling and decision-making, as named by Ma et al (2019), including state-of-the-art tools and techniques implemented within smart city applications. Ma et al (2019, p.12) state that "many systems use multiple methodologies to build models".…”

Section: Taxonomic Structurementioning

confidence: 99%

“…Generally speaking, it is equally common for both online and offline DTs to inform DT users. It is, in this specialized use, that human act as actuators and decision makers, necessary for the intervention in real world to take place (Ma et al, 2019). This necessity, in fact, reflects the overwhelming complexity and dynamism of the urban environment, as well as our poor understanding of its intrinsically interconnect and changing natureall requiring collective human judgments for decision making, in other words, to satisfice rather than optimize (Simon, 1960).…”

Section: Informmentioning

confidence: 99%

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A digital twin uses classification system for urban planning & city infrastructure management

Al-Sehrawy¹,

Kumar²,

Watson³

2021

ITcon

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Recently, the concept of Digital Twin [DT] has pervaded the field of urban planning and city infrastructure management. This paper first affirms that the knowledge created by virtue of DT real-world implementation, through undertaking various DT pilot projects, case studies and proof-of-concept initiatives, comprises the ‘know-how’ and genuine practical experience upon which the DT research and practices can further develop and mature. It then argues that this type of knowledge is poorly captured and mostly left neither realized nor fully utilized. This significantly hinders the rate by which DT practices within the urban and built environments evolve. While acknowledging the benefits of the ongoing work by many DT researchers, including enumeration, categorization and detailing of multiple DT use cases, such endeavours arguably suffer from three profound weaknesses causing the inefficient sharing and transfer of DT ‘know-how’ knowledge amongst DT stakeholders. The three limitations are: (a) lack of DT standard terminology constituting a common DT language; (b) lack of standard and clear methods to enable documenting DT projects and making the ‘know-how’ explicit to the rest of the DT market; and (c) the lack of an established and adequate DT use cases classifications system to guide DT practitioners in searching for and retrieving the previously accomplished DT case studies that are most relevant to their interests and context. Correspondingly, three solutions are proposed constituting a three-pronged DT Uses Classification System [DTUCS]: prong-A (i.e. Standardize-to-Publish); prong-B (i.e. Detail-to-Prove); and prong-C (i.e. Classify-to-Reach). DTUCS is developed using a meta-methodology encapsulating a systematic literature review and three distinct sub-methodologies. The paper concludes with an overview of the implications of DTUCS along with recommendations on how it can be further validated and improved.

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Section: Taxonomic Structurementioning

confidence: 99%

Section: Informmentioning

confidence: 99%

A digital twin uses classification system for urban planning & city infrastructure management

Al-Sehrawy¹,

Kumar²,

Watson³

2021

ITcon

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“…An important characteristic of many DT use cases is the diffusion across different city layers. The fact that most smart city research and urban DTs are interdisciplinary, involving interdependent urban data and models (Ma et al, 2019), reflects the reality of multiple layers inherited in the fabric of cities and urban environment. Many researchers attempted to disentangle these city layers; White el al.…”

Section: D3: Layeringmentioning

confidence: 99%

“…Similarly, Ma et al (2019) named five different city domains: transportation, energy, emergency and public safety, social sensing and natural environment. In a review of urban planning needs and urban sensing technologies, Cunningham and Verbraeck (2018) spotted three general conceptual perspectives of the city, including physical and infrastructural, natural resources and political economy.…”

Section: D3: Layeringmentioning

confidence: 99%

A multi-dimensional digital twin use cases classification framework

Al-Sehrawy¹,

Kumar²,

Watson³

2021

Proceedings of the 2021 European Conference on Computing in Construction

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The concept of a Digital Twin [DT] has been gaining increasing attention in the realm of urban planning and city infrastructure management. In support of this movement, DT advocates have been consistently casting light on the possible DT use cases to better manifest its potential and the enormous value it promises to unlock. However, these attempts are arguably limited by the lack of a formal and standard DT use cases classification framework. Hence, this paper puts forward a multidimensional DT use cases classification framework, based on published key DT case studies and a framework development methodology, to address this limitation. It concludes with insights on further possible implications of, and enhancements to this framework.

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“…The prevalence of the Internet of Things has enabled a variety of smart city applications which aim to improve citizens' safety, wellness, and quality of life [27]. In a typical smart city control center (e.g., Intel Traffic Control Center [17], BreezoMeter Air Pollution control [5], and Microsoft CityNext [31]), the decision system takes input of various sensor readings and output actions for actuators.…”

Section: Introductionmentioning

confidence: 99%

Predictive monitoring with uncertainty for deep learning enabled smart cities

Bartocci

Stankovic

et al. 2020

Proceedings of the 18th Conference on Embedded Networked Sensor Systems

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We present CityPM, a novel predictive monitoring system for smart cities, that continuously generates sequential predictions of future city states using Bayesian deep learning and monitors if the generated predictions satisfy city safety and performance requirements. We formally define a flowpipe signal to characterize prediction outputs of Bayesian deep learning models, and develop a new logic, named Signal Temporal Logic with Uncertainty (STL-U), for reasoning about the correctness of flowpipe signals. CityPM can monitor city requirements specified in STL-U such as "with 90% confidence level, the predicated air quality index in the next 10 hours should always be below 100". We also develop novel STL-U logic-based criteria to measure uncertainty for Bayesian deep learning. CityPM uses these logic-calibrated uncertainty measurements to select and tune the uncertainty estimation schema in deep learning models. We evaluate CityPM on three large-scale smart city case studies, including two real-world city datasets and one simulated city experiment. The results show that CityPM significantly improves the simulated city's safety and performance, and the use of STL-U logic-based criteria leads to improved uncertainty calibration in various Bayesian deep learning models.

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Data Sets, Modeling, and Decision Making in Smart Cities

Cited by 42 publications

References 58 publications

A digital twin uses classification system for urban planning & city infrastructure management

A digital twin uses classification system for urban planning & city infrastructure management

A multi-dimensional digital twin use cases classification framework

Predictive monitoring with uncertainty for deep learning enabled smart cities

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