scholarly journals Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona’s Traffic with Empirical Data

2021 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Javier Argota Sánchez-Vaquerizo
Keyword(s):  
Big Data ◽  
Empirical Data ◽  
Large Scale ◽  
Smart Cities ◽  
Digital Twin ◽  
Fine Grained ◽  
Mobility Data ◽  
Digital Twins ◽  
Alternative Scenarios ◽  
Hourly Distribution

Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. Yet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. This paper, therefore, presents a new large-scale agent-based traffic microsimulation for the Barcelona urban area using SUMO to show the possibilities and challenges of building these scenarios based on novel fine-grained empirical big data. It combines novel mobility data from real cell phone records with conventional surveys to calibrate the model comparing two different dynamic assignment methods for getting an operationally realistic and efficient simulation. Including through traffic and the use of a stochastic adaptive routing approach results in a larger 24-hour model closer to reality. Based on an extensive multi-scalar evaluation including traffic counts, hourly distribution of trips, and macroscopic metrics, this model expands and outperforms previous large-scale scenarios, which provides new operational opportunities in city co-creation and policy. The novelty of this work relies on the effective modelling approach using newly available data and the realistic robust evaluation. This allows the identification of the fundamental challenges of simulation to accurately capture real-world dynamical systems and to their predictive power at a large scale, even when fed by big data, as envisioned by the digital twin concept applied to smart cities.

Disparity and Dynamics of Social Distancing Behaviors in Japan: An Investigation of mobile phone mobility data (Preprint)

2021 ◽  
Author(s):  
Zeyu Lyu ◽  
Hiroki Takikawa
Keyword(s):  
Large Scale ◽  
Age Groups ◽  
Mitigation Strategies ◽  
Social Distancing ◽  
Demographic Groups ◽  
Fine Grained ◽  
Mobility Data ◽  
The Social ◽  
The Government ◽  
Over Time

BACKGROUND The availability of large-scale and fine-grained aggregated mobility data has allowed researchers to observe the dynamic of social distancing behaviors at high spatial and temporal resolutions. Despite the increasing attentions paid to this research agenda, limited studies have focused on the demographic factors related to mobility and the dynamics of social distancing behaviors has not been fully investigated. OBJECTIVE This study aims to assist in the design and implementation of public health policies by exploring the social distancing behaviors among various demographic groups over time. METHODS We combined several data sources, including mobile tracking data and geographical statistics, to estimate visiting population of entertainment venues across demographic groups, which can be considered as the proxy of social distancing behaviors. Then, we employed time series analyze methods to investigate how voluntary and policy-induced social distancing behaviors shift over time across demographic groups. RESULTS Our findings demonstrate distinct patterns of social distancing behaviors and their dynamics across age groups. The population in the entertainment venues comprised mainly of individuals aged 20–40 years, while according to the dynamics of the mobility index and the policy-induced behavior, among the age groups, the extent of reduction of the frequency of visiting entertainment venues during the pandemic was generally the highest among younger individuals. Also, our results indicate the importance of implementing the social distancing policy promptly to limit the spread of the COVID-19 infection. However, it should be noticed that although the policy intervention during the second wave in Japan appeared to increase the awareness of the severity of the pandemic and concerns regarding COVID-19, its direct impact has been largely decreased could only last for a short time. CONCLUSIONS At the time we wrote this paper, in Japan, the number of daily confirmed cases was continuously increasing. Thus, this study provides a timely reference for decision makers about the current situation of policy-induced compliance behaviors. On the one hand, age-dependent disparity requires target mitigation strategies to increase the intention of elderly individuals to adopt mobility restriction behaviors. On the other hand, considering the decreasing impact of self-restriction recommendations, the government should employ policy interventions that limit the resurgence of cases, especially by imposing stronger, stricter social distancing interventions, as they are necessary to promote social distancing behaviors and mitigate the transmission of COVID-19. CLINICALTRIAL None

scholarly journals Urban Digital Twins for Smart Cities and Citizens: The Case Study of Herrenberg, Germany

2020 ◽  
Vol 12 (6) ◽  
pp. 2307 ◽  
Author(s):  
Fabian Dembski ◽  
Uwe Wössner ◽  
Mike Letzgus ◽  
Michael Ruddat ◽  
Claudia Yamu
Keyword(s):  
General Public ◽  
Smart Cities ◽  
Flow Simulation ◽  
Urban Mobility ◽  
Communication Tool ◽  
Digital Twin ◽  
Collaborative Processes ◽  
Digital Twins ◽  
Collaboration And Communication

Cities are complex systems connected to economic, ecological, and demographic conditions and change. They are also characterized by diverging perceptions and interests of citizens and stakeholders. Thus, in the arena of urban planning, we are in need of approaches that are able to cope not only with urban complexity but also allow for participatory and collaborative processes to empower citizens. This to create democratic cities. Connected to the field of smart cities and citizens, we present in this paper, the prototype of an urban digital twin for the 30,000-people town of Herrenberg in Germany. Urban digital twins are sophisticated data models allowing for collaborative processes. The herein presented prototype comprises (1) a 3D model of the built environment, (2) a street network model using the theory and method of space syntax, (3) an urban mobility simulation, (4) a wind flow simulation, and (5) a number of empirical quantitative and qualitative data using volunteered geographic information (VGI). In addition, the urban digital twin was implemented in a visualization platform for virtual reality and was presented to the general public during diverse public participatory processes, as well as in the framework of the “Morgenstadt Werkstatt” (Tomorrow’s Cities Workshop). The results of a survey indicated that this method and technology could significantly aid in participatory and collaborative processes. Further understanding of how urban digital twins support urban planners, urban designers, and the general public as a collaboration and communication tool and for decision support allows us to be more intentional when creating smart cities and sustainable cities with the help of digital twins. We conclude the paper with a discussion of the presented results and further research directions.

scholarly journals Digital Twins-Based Smart Design and Control of Ultra-Precision Machining: A Review

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1717
Author(s):  
Lei Wu ◽  
Jiewu Leng ◽  
Bingfeng Ju
Keyword(s):  
Information Asymmetry ◽  
Large Scale ◽  
Planning Process ◽  
Design Stage ◽  
Precision Machining ◽  
Digital Twin ◽  
Module Design ◽  
Digital Twins ◽  
Ultra Precision ◽  
And Control

Ultra-Precision Machining (UPM) is a kind of highly accurate processing technology developed to satisfy the manufacturing requirements of high-end cutting-edge products including nuclear energy producers, very large-scale integrated circuits, lasers, and aircraft. The information asymmetry phenomenon widely exists in the design and control of ultra-precision machining. It may lead to inconsistency between the designed performance and operational performance of the UPM equipment on stiffness, thermal stability, and motion accuracy, which result from its design, manufacturing, and control, and determine the form accuracy and surface roughness of machined parts. The performance of the UPM equipment should be improved continuously. It is still challenging to realize the real-time and self-adaptive control, in which building a high-fidelity and computationally efficient digital twin is a valuable solution. Nevertheless, the incorporation of the digital twin technology into the UPM design and control remains vague and sometimes contradictory. Based on a literature search in the Google Scholar database, the critical issues in the UPM design and control, and how to use the digital twin technologies to promote it, are reviewed. Firstly, the digital twins-based UPM design, including bearings module design, spindle-drive module design, stage system module design, servo module design, and clamping module design, are reviewed. Secondly, the digital twins-based UPM control studies, including voxel modeling, process planning, process monitoring, vibration control, and quality prediction, are reviewed. The key enabling technologies and research directions of digital twins-based design and control are discussed to deal with the information asymmetry phenomenon in UPM.

scholarly journals DIGITAL TWIN FOR INDOOR DISASTER IN SMART CITY: A SYSTEMATIC REVIEW

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 315-322
Author(s):  
S. Shaharuddin ◽  
K. N. Abdul Maulud ◽  
S. A. F. Syed Abdul Rahman ◽  
A. I. Che Ani
Keyword(s):  
Urban Planning ◽  
Built Environment ◽  
Disaster Management ◽  
Smart City ◽  
Smart Cities ◽  
Environmental Disaster ◽  
Digital Twin ◽  
Digital Twins ◽  
Building Information ◽  
Recent Developments

Abstract. Technology has advanced and progressed tremendously, and the term city is being elevated to a new level where the smart city has been introduced globally. Recent developments in the concept of smart city have led to a renewed interest in Digital Twin. Using precise Building Information Modelling (BIM) consolidated with big data and sensors, several attempts have been made to establish digital twin smart cities. In recent years, several researchers have sought to determine the capability of smart city and digital twin for various taxonomies such as development and urban planning purposes, built environment, manufacturing, environmental, disaster management, and healthcare. Despite being beneficial in many disciplines, especially in manufacturing, built environment, and urban planning, these existing studies have shown a lack of aspect in terms of emergency or disaster-related as opposed to the elements mentioned above. This is because the researcher has not treated emergencies or disasters in much detail. Therefore, an extensive review on smart city, digital twin, BIM and disaster management and technology that revolves around these terms were summarised. In general, 39 articles from prominent multidisciplinary databases were retrieved over the last two decades based on the suggested PRISMA workflow. These final articles were analysed and categorised into four themes based on the research content, gist, and keywords. Based on the review of 39 articles related to smart city, digital twin and BIM, a workflow for the smart city digital twin and the conceptual framework for indoor disaster management was proposed accordingly. The establishment of smart city digital twins solely for an indoor emergency can be beneficial to urbanites, and it could provide numerous benefits for enhanced situation assessment, decision making, coordination, and resource allocation.

Crowdsensing in Smart Cities

2019 ◽  
pp. 893-915
Author(s):  
Paolo Bellavista ◽  
Giuseppe Cardone ◽  
Antonio Corradi ◽  
Luca Foschini ◽  
Raffaele Ianniello
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Smart Cities ◽  
Design Guidelines ◽  
Technical Data ◽  
Fine Grained ◽  
Living Lab ◽  
Widespread Availability ◽  
One Year ◽  
The University

The widespread availability of smartphones with on-board sensors has recently enabled the possibility of harvesting large quantities of monitoring data in urban areas, thus enabling so-called crowdsensing solutions, which make it possible to achieve very large-scale and fine-grained sensing by exploiting all personal resources and mobile activities in Smart Cities. In fact, the information gathered from people, systems, and things, including both social and technical data, is one of the most valuable resources available to a city's stakeholders, but its huge volume makes its integration and processing, especially in a real-time and scalable manner, very difficult. This chapter presents and discusses currently available crowdsensing and participatory solutions. After presenting the current state-of-the-art crowdsensing management infrastructures, by carefully considering the related and primary design guidelines/choices and implementation issues/opportunities, it provides an in-depth presentation of the related work in the field. Moreover, it presents some novel experimental results collected in the ParticipAct Crowdsensing Living Lab testbed, an ongoing experiment at the University of Bologna that involves 150 students for one year in a very large-scale crowdsensing campaign.

Crowdsensing in Smart Cities

2015 ◽  
pp. 316-338 ◽  
Author(s):  
Paolo Bellavista ◽  
Giuseppe Cardone ◽  
Antonio Corradi ◽  
Luca Foschini ◽  
Raffaele Ianniello
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Smart Cities ◽  
Design Guidelines ◽  
Technical Data ◽  
Fine Grained ◽  
Living Lab ◽  
Widespread Availability ◽  
One Year ◽  
The University

The widespread availability of smartphones with on-board sensors has recently enabled the possibility of harvesting large quantities of monitoring data in urban areas, thus enabling so-called crowdsensing solutions, which make it possible to achieve very large-scale and fine-grained sensing by exploiting all personal resources and mobile activities in Smart Cities. In fact, the information gathered from people, systems, and things, including both social and technical data, is one of the most valuable resources available to a city's stakeholders, but its huge volume makes its integration and processing, especially in a real-time and scalable manner, very difficult. This chapter presents and discusses currently available crowdsensing and participatory solutions. After presenting the current state-of-the-art crowdsensing management infrastructures, by carefully considering the related and primary design guidelines/choices and implementation issues/opportunities, it provides an in-depth presentation of the related work in the field. Moreover, it presents some novel experimental results collected in the ParticipAct Crowdsensing Living Lab testbed, an ongoing experiment at the University of Bologna that involves 150 students for one year in a very large-scale crowdsensing campaign.

scholarly journals Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 461 ◽  
Author(s):  
Luliang Tang ◽  
Jie Gao ◽  
Chang Ren ◽  
Xia Zhang ◽  
Xue Yang ◽  
...  
Keyword(s):  
Land Use ◽  
Big Data ◽  
Large Scale ◽  
Scientific Basis ◽  
Small Sample ◽  
Fine Grained ◽  
Urban Construction ◽  
Urban Cluster ◽  
Novel Method ◽  
Land Use Functions

The design of urban clusters has played an important role in urban planning, but realizing the construction of these urban plans is quite a long process. Hence, how the progress is evaluated is significant for urban managers in the process of urban construction. Traditional methods for detecting urban clusters are inaccurate since the raw data is generally collected from small sample questionnaires of resident trips rather than large-scale studies. Spatiotemporal big data provides a new lens for understanding urban clusters in a natural and fine-grained way. In this article, we propose a novel method for Detecting and Evaluating Urban Clusters (DEUC) with taxi trajectories and Sina Weibo check-in data. Firstly, DEUC applies an agglomerative hierarchical clustering method to detect urban clusters based on the similarities in the daily travel space of urban residents. Secondly, DEUC infers resident demands for land-use functions using a naïve Bayes’ theorem, and three indicators are adopted to assess the rationality of land-use functions in the detected clusters—namely, cross-regional travel index, commuting direction index, and fulfilled demand index. Thirdly, DEUC evaluates the progress of urban cluster construction by calculating a proposed conformance indicator. In the case study, we applied our method to detect and analyze urban clusters in Wuhan, China in the years 2009, 2014, and 2015. The results suggest the effectiveness of the proposed method, which can provide a scientific basis for urban construction.

scholarly journals Urban Digital Twins – A FIWARE-based model

2021 ◽  
Vol 69 (12) ◽  
pp. 1106-1115
Author(s):  
Martin Bauer ◽  
Flavio Cirillo ◽  
Jonathan Fürst ◽  
Gürkan Solmaz ◽  
Ernö Kovacs
Keyword(s):  
Smart City ◽  
Data Exchange ◽  
Reference Model ◽  
Smart Cities ◽  
Digital Twin ◽  
Data Exchange Platform ◽  
City Community ◽  
Digital Twins ◽  
The City ◽  
Different Time Scales

Abstract This article describes the use of digital twins for smart cities, i. e., the Urban Digital Twin (UDTw) concept. It shows how UDTws can be realized using the open source components from the FIWARE ecosystem that are already used in more than 200 cities worldwide. The used NGSI-LD standard is supported by the European Connecting Europe Facility, the Open and Agile Smart City community, the Indian Urban Data Exchange platform, and the Japanese Smart City Reference Model. Unlike digital twins in other domains, e. g., manufacturing, where digital twins are co-developed with their physical counterparts, UDTws often evolve driven by different stakeholders, on different time scales, as well as by utilizing many different data sources from the city. This article builds on a well-established lifecycle model for Digital Twins and combines this with a conceptual model for digital twins consisting of data, reactive, predictive and forecasting (“what if”) digital twin functionalities. The article also describes how AI-based technologies can be used to extract knowledge to build the UDTws from the IoT-based infrastructure of a smart city.

scholarly journals Towards Digitalization in Bio-Manufacturing Operations: A Survey on Application of Big Data and Digital Twin Concepts in Denmark

2021 ◽  
Vol 3 ◽  
Author(s):  
Isuru A. Udugama ◽  
Merve Öner ◽  
Pau C. Lopez ◽  
Christan Beenfeldt ◽  
Christoph Bayer ◽  
...  
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Business Case ◽  
Digital Twin ◽  
Manufacturing Operations ◽  
Current State ◽  
Production Processes ◽  
Big Data Applications ◽  
Digital Twins ◽  
Biotech Industry

Digitalization in the form of Big Data and Digital Twin inspired applications are hot topics in today's bio-manufacturing organizations. As a result, many organizations are diverting resources (personnel and equipment) to these applications. In this manuscript, a targeted survey was conducted amongst individuals from the Danish biotech industry to understand the current state and perceived future obstacles in implementing digitalization concepts in biotech production processes. The survey consisted of 13 questions related to the current level of application of 1) Big Data analytics and 2) Digital Twins, as well as obstacles to expanding these applications. Overall, 33 individuals responded to the survey, a group spanning from bio-chemical to biopharmaceutical production. Over 73% of the respondents indicated that their organization has an enterprise-wide level plan for digitalization, it can be concluded that the digitalization drive in the Danish biotech industry is well underway. However, only 30% of the respondents reported a well-established business case for the digitalization applications in their organization. This is a strong indication that the value proposition for digitalization applications is somewhat ambiguous. Further, it was reported that digital twin applications (58%) were more widely used than Big Data analytic tools (37%). On top of the lack of a business case, organizational readiness was identified as a critical hurdle that needs to be overcome for both Digital Twin and Big Data applications. Infrastructure was another key hurdle for implementation, with only 6% of the respondents stating that their production processes were 100% covered by advanced process analytical technologies.

scholarly journals Greenhouse industry 4.0 – digital twin technology for commercial greenhouses

2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Daniel Anthony Howard ◽  
Zheng Ma ◽  
Christian Veje ◽  
Anders Clausen ◽  
Jesper Mazanti Aaslyng ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Industry 4.0 ◽  
Influential Factors ◽  
Sensor Data ◽  
Greenhouse Production ◽  
Production Chain ◽  
Improve Energy Efficiency ◽  
Digital Twin ◽  
Digital Twins

AbstractThe project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

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