Sustainable Cities and Society 111 (2024) 105583

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Sustainable Cities and Society

journal homepage: www.elsevier.com/locate/scs

Navigating urban complexity: The transformative role of digital twins in

smart city development
Dechen Peldon a, Saeed Banihashemi b, *, Khuong LeNguyen a, Sybil Derrible c
a
School of Design & Built Environment, University of Canberra (UC), Australia
b
School of Built Environment, University of Technology Sydney (UTS), Australia
c
School of Civil, Materials and Environmental Engineering, University of Illinois Chicago, US

A R T I C L E I N F O A B S T R A C T

Keywords: This research systematically explores the burgeoning field of Digital Twins (DTs) within smart cities’ framework
Digital twin and urban development. Anchored by three research questions, the study delineates the theoretical un­
Urban design derpinnings and practical implications of DTs at a city scale. It delves into the structure, operational dynamics,
Urban planning
and diverse applications in various urban domains supported by case studies. Through a structured methodology
Smart city
BIM, Industry 4.0
employing the PRISMA framework, it includes an analysis of 64 pertinent studies from an initial pool of 519. The
research synthesis highlights the dynamic nature of DTs, their multifaceted technological layers, and their
instrumental role in shaping sustainable urban futures. Despite the promising outlook, the study also highlights
several technological and real-world hurdles that need to be addressed to fully unlock the capabilities of DTs
within urban environments.

1. Introduction landscapes. This technology, while nascent, holds much promise in

urban planning through its ability to provide engaging, real-time visu­
The dawn of the 21st century has witnessed an unprecedented surge alizations and simulations of urban environments (Caprari et al., 2022).
in urbanization, ushering in many challenges and opportunities for cities The potential applications of DT in urban development are vast
worldwide (Avezbaev et al., 2023; Major et al., 2021; Mohammadi & (Dembski et al., 2019). From infrastructure planning to environmental
Taylor, 2019). As highlighted by Deng et al. (2021), this urban expan­ sustainability, DTs offer a holistic, data-driven approach to addressing
sion is not merely a demographic shift but a complex interplay of so­ urban challenges (Caprari et al., 2022). For instance, by simulating
cioeconomic, technological, and environmental factors. As urban areas urban scenarios, city planners can anticipate and mitigate potential is­
burgeon, accommodating and sustaining their growth becomes para­ sues, ranging from traffic congestion to environmental degradation (Erol
mount while ensuring sustainability, resilience, and quality of life for et al., 2020; Mylonas et al., 2021). Furthermore, DTs amalgamation with
urban dwellers (Banihashemi & Zarepour Sohi, 2022; Mohammadi & Internet of Things (IoT) and Artificial Intelligence (AI), can improve
Taylor, 2019). predictive analytics, optimize resource allocation, and foster citizen
Central to addressing these challenges is integrating technology into engagement (Fattahi Tabasi et al., 2023).
urban planning and development (Barresi, 2023; Derrible, 2019; Men­ However, the journey of integrating DT into urban planning is not
dula et al., 2022). Moreover, ’smart cities’ concept has gained popu­ without challenges. As noted by Charitonidou (2022), cities face hurdles
larity, emphasizing digital tools utilization and data-driven approaches ranging from data privacy concerns to the need for robust technological
to enhance urban living (Mylonas et al., 2021). Within this digital infrastructure. Moreover, the multidisciplinary nature of urban planning
landscape, Digital Twin (DT) is progressively being recognized and necessitates collaboration across sectors, requiring a paradigm shift in
utilized in areas of urban planning and infrastructure management traditional planning approaches (Charitonidou, 2022). Despite these
(Depretre et al., 2022; Deren et al., 2021; Elsehrawy et al., 2021). As challenges, DT’s transformative impact in urban development is unde­
described by Elsehrawy et al. (2021), DTs are virtual counterparts of niable. While urban regions face dual challenges of growth and sus­
real-world entities, bridging the digital and physical realms of urban tainability, DTs offer a beacon of hope. They equip cities with the

* Corresponding author.
E-mail address: saeed.banihashemi@uts.edu.au (S. Banihashemi).

https://doi.org/10.1016/j.scs.2024.105583
Received 8 April 2024; Received in revised form 5 June 2024; Accepted 6 June 2024
Available online 7 June 2024
2210-6707/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
D. Peldon et al. Sustainable Cities and Society 111 (2024) 105583

capabilities to envision the future and make well-informed choices, and • RQ2. What are the potential and current applications of DT?
ensure a sustainable, resilient, and inclusive urban future (Deng et al., • RQ3. What challenges and limitations are associated with deploying
2021). DT at a city scale?
While the possibilities of DT in urban planning are extensive, its
application, especially concerning sustainability, remains in its infancy
(Mylonas et al., 2021). Furthermore, the integration of social commu­ 2.2. Paper selection
nity dynamics within urban DT remains an emergent field of research.
How DT can encapsulate the rich tapestry of urban social interactions, is A PRISMA flow diagram was utilized to record the step-by-step re­
still developing. Whereas, recognizing this dimension is crucial for view process for the paper selection stage (Fig. 2). The following sections
creating more holistic and responsive urban management tools that delineate the methodical steps undertaken to sort through, evaluate, and
truly reflect the complexity of human environments within cities. The select the most relevant studies that align with the research objectives
existing literature offers fragmented insights, with some knowledge gaps and questions.
in understanding the comprehensive impact and potential of DT in an
urban context. Hence, this comprehensive literature review seeks to 2.2.1. Identification
consolidate existing knowledge, identify gaps, and outline directions for The primary database utilized for this search was Scopus. Additional
upcoming studies on DT within the scope of urban planning and smart papers were obtained from Google Scholar for more comprehensive
cities. analysis. The search strategy incorporated the following keywords:
“Digital Twin”, “Urban Planning”, “Smart Cities”, and “Infrastructure
2. Review methodology planning”. Multiple combinations of these keywords were utilized using
Boolean operators “AND” and “OR”. The initial search yielded a total of
This study has adopted a systematic review approach to explore and 519 records, comprising 493 from Scopus and 26 from Google Scholar,
analyse existing literature on DT at the city scale. The methodology without a time range.
structure consists of four primary steps as shown in Fig. 1. The first step
includes defining a clear purpose of this study through three research 2.2.2. Screening
questions. Subsequently, it includes the paper selection process where a Following the preliminary identification, a rigorous screening pro­
PRISMA flowchart and guidelines are utilized to record the step-by-step cess was undertaken to verify the studies’ relevance and quality. The
approach of identification, screening, eligibility assessment and select­ initial stage entailed eliminating duplicate records, resulting in the
ing the definitive papers for analysis. Following this, it includes the exclusion of 125 duplicates. This left 394 unique records that were then
analysis part which consists of descriptive analysis and content analysis. subjected to a title and abstract screening. Based on the relevance to the
The final step includes gap identification and recommendations for research topic and predefined inclusion and exclusion criteria, 279 re­
future research. cords were excluded at this stage. Studies were included if they

2.1. Research questions • Addressed the utilization of DT in urban planning or smart city
contexts.
This systematic review primarily aimed in establishing the theoret­ • provided insights into the challenges or potentials of DT in urban
ical base and acquiring a detailed comprehension of the principles, core contexts.
technological aspects, potential applications, and limitations of DT at a • consisted of articles reviewed by experts, conference papers, or
city level within the fields of smart cities and urban planning. Conse­ credible industry reports.
quently, this led to the formulation of the following three research
questions: Studies were excluded if:

• RQ1. How has the concept and implementation of DT evolved and • the language was not in English.
pervaded within the areas of smart cities and urban planning? • a record did not focus on the application of DT in urban contexts.

Fig. 1. Review methodology structure.

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D. Peldon et al. Sustainable Cities and Society 111 (2024) 105583

Fig. 2. PRISMA flow diagram of the study.

• a record was purely technical without any relevance to urban plan­ assessment, 32 reports were excluded.
ning or sustainability.
2.2.4. Final inclusion
2.2.3. Eligibility assessment After the comprehensive screening and eligibility assessment, 64
The remaining 115 records were then sought for a more detailed studies were found suitable for the final systematic review. These studies
retrieval and assessment. However, 19 of these records could not be provide valuable insights, data, and findings that were synthesized and
retrieved for various reasons (e.g., access restrictions, unavailability, analyzed to address the research questions.
and broken links). The full texts of the successfully retrieved 96 records
were thoroughly assessed for their eligibility. Criteria for this assessment
included the study’s relevance to the research questions, methodological
rigour, and the quality of data presented. Following this detailed

Fig. 3. a) Annual publications trend, b) Publications outlet.

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D. Peldon et al. Sustainable Cities and Society 111 (2024) 105583

3. Analysis and results et al., 2021; Mylonas et al., 2021). This evolution from NASA’s early use
of digital twins to their current role as comprehensive models for more
3.1. Descriptive analysis intricate processes and systems underscores their evolving definition
and application, meeting the demands of various industries and tech­
3.1.1. The trend of publishing articles nological progress.
Fig. 3.a indicates the trend of publication distribution of the selected
papers over the years. With a modest number of 4 publications in the 4.1.1. Diverse definitions of digital twin
year 2019, it indicates that DT was relatively nascent or less explored. A digital twin (DT) fundamentally comprises three key elements: the
From 2019 to 2022, a notable increase in publications volume was physical entity, its digital representation, and the data links (Deng et al.,
observed. The number more than tripled from 2019 to 2022, high­ 2021) that enable a bidirectional data exchange between them (Wang
lighting a growing interest and recognition of the importance of the et al., 2023). Considering the novelty of the DT concept, it requires
research area. In 2023, there is a slight decrease to 12 publications in refining and clarifying the definitions and concepts, their present stage
2023. While this is a reduction from the previous year, it is still three of progress and identifying future challenges (Ferré-Bigorra et al., 2022).
times more than the number of publications in 2019, indicating that the However, despite DT’s growing recognition in research fields and
topic remains of significant interest. Fig. 3.b also shows the distribution practical applications, there’s yet to be a universally agreed-upon defi­
of publications by type, providing an overview of the literature nition (Lu et al., 2020). As per Mylonas et al. (2021), DT’s understanding
composition. is complicated by the involvement of various sectors that apply them
across different domains, each viewing DT through their unique
3.2. Thematic and content analysis perspective. As DT continues to develop, this has resulted in a broad­
ening of the definition, which, in certain instances, extends beyond just a
The thematic and content analysis forms the fourth step of the review technological viewpoint.
methodology structure. The content of each paper was thoroughly Table 1 provides an overview of different DT definitions. While the
reviewed and the key findings were analysed and outlined in six cate­ core concept of a DT as a digital mirror to a physical counterpart remains
gories corresponding to the research questions. First, it includes an un­ consistent, the specific application and functionality of a DT can vary
derstanding of the historical evolution, diverse definitions and significantly depending on the industry. Each definition captures the
fundamental distinctions from other digital representations. Secondly, it essence of DTs in its context, highlighting the technology’s adaptability
analyses the technological aspects of DT including its system architec­ and the importance of real-time data and connectivity in creating a
ture, layers, underlying technologies and existing tools and products. responsive and accurate digital counterpart.
Thirdly, it encapsulates the operational aspects of DT in a smart city
context and is followed by their potential uses across different fields, 4.1.2. Differentiating digital twins from other digital representation
encompassing urban planning and its sustainability aspects. This is then Despite the increasing study of DTs in the industrial and
followed by an overview of the selected case studies for a comprehensive manufacturing sectors, there remains ambiguity in distinguishing a fully
understanding of practical implementations in the real world. Lastly,
limitations of DT implementation form the seventh category of content
analysis. This analysis is outlined in the Section 4 below. Table 1
Diverse DT Definitions.
4. Discussion Source Application Area Definition

(Austin et al., 2020, p. Smart Cities "A smart city digital twin is
4.1. Historical context and evolution of digital twin 2) defined here as a cyber
component that mirrors the
The idea of DT was initially introduced by Michael Grieve in 2003 to physical urban system through
describe product lifecycle management, a concept that has since un­ real-time monitoring and
synchronization of urban
dergone significant evolution (Ketzler et al., 2020; Mylonas et al., 2021;
activities."
Wang et al., 2023). Grieves initially termed his model the “Mirrored (Mylonas et al., 2021, p. Built Environment/ "a digital twin is a realistic digital
Spaces Model,” which set the foundation for the future development of 3) Infrastructure representation of assets,
DTs (Masoumi et al., 2023). This model was pivotal in transitioning the processes or systems in the built
concept into smart manufacturing, aligning it closely with the Industry or natural environment."
(Spiridonov & Shabiev, Urban Planning "Digital twin is an interactive
4.0 movement (Mylonas et al., 2021). 2020, p. 3) digital model of a city-planning
By 2010, the concept had evolved, and NASA adopted the term object, implemented in the
“digital twins” to describe a sophisticated simulation that accurately planning and management
reflects the real-time status of its physical counterpart across various system based on a complex
analytical urban information
scales, utilizing both historical and real-time data (Wang et al., 2023).
computer platform;"
NASA’s early adoption of DTs, notably in their Apollo program, (GE Digital, 2017, as Manufacturing/ "A dynamic digital
demonstrated the technology’s significance in the aerospace sector as a cited in Lu et al., Product Design representation of an industrial
sophisticated model for mirroring information (Mohammadi & Taylor, 2020, p. 2) asset that enables companies to
2019; Mylonas et al., 2021). NASA’s implementation of DTs was aimed better understand and predict
the performance of their
at continuously forecasting the health, lifespan, and likelihood of mis­ machines, find new revenue
sions’ successes involving vehicles or systems (Deng et al., 2021). streams, and change the way
While the initial applications of DTs were predominantly in aero­ their business operates."
nautics, their use has since broadened to encompass a variety of sectors (Glaessgen & Stargel, Aerospace "Is an integrated multi-physics,
2012, as cited in multiscale, probabilistic
and use cases, such as product design, structural health monitoring,
Caprari et al., 2022. P. simulation of an as-built vehicle
waste recycling, and agriculture (Ferré-Bigorra et al., 2022). The con­ 5) or system that uses
struction industry saw a notable uptake in DT development only the best available physical
recently, particularly focusing on city administration (Ferré-Bigorra models, sensor updates, fleet
et al., 2022). Additionally, the smart cities initiative has started to utilize history, etc., to mirror the life of
its corresponding flying twin."
this concept, thereby broadening the impact and scope of DTs (Deren

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replicated DT from other digital models and shadows (Shahat et al., In contrast, Lu et al. (2020) describe a hierarchical five-layer system
2021). This confusion arises partly due to digital twins being inter­ that separates data acquisition and transmission into distinct layers,
twined with various technologies, leading to a mix-up in their defini­ highlighting the complexity of data handling and the importance of
tions, potential, and obstacles (Raes et al., 2021). It is important to robust communication technologies for DTs. It includes a data acquisi­
elucidate these distinctions to advance their evolution (Quek et al., tion, transmission, digital modelling, data/model integration, and ser­
2023). A summarized overview from different authors in Table 2 illus­ vice layers. The data acquisition layer is the foundational layer that
trates the unique features of DTs in comparison to other digital models, deals with the gathering of heterogeneous data from multiple sources
aiming to clarify these differences (Austin et al., 2020; Hämäläinen, while the transmission layer is responsible for transferring data to the
2021; Quek et al., 2023; Sepasgozar, 2021; Shahat et al., 2021). other upper layers. The digital modelling layer encompasses a digital
depiction of physical structures such as BIM and CIM models. The
4.2. Technological aspects data/model integration layer serves as the central component of DT
architectural framework, integrating all the data and models and
4.2.1. Urban digital twin providing functionalities for manipulation, storage, analysis, and pro­
The urban digital twin (UDT) is a realistic digital depiction of urban cessing. The service layer, positioned at the uppermost level, delivers
environments, encompassing their components, operations, and systems services to the community, and facilitates interaction between in­
(Nochta et al., 2021. It supports decision-making processes aimed at dividuals and the integrated models.
achieving outcomes at the city scale, such as urban planning and man­ Ferré-Bigorra et al. (2022), on the other hand, provide a unique
agement, as well as related services, offering enhanced perspectives for perspective by situating a 3D digital urban model at the foundation of
informed decisions (Nochta et al., 2021). An Urban DT is comprises of UDTs. They advocate for a four-layer architecture with an additional
interconnected sub-DTs, representing certain aspects of the functioning physical layer, emphasizing the interactive capabilities through sensors
and development of the urban environment (Lu et al., 2020; Papyshev & and actuators and the importance of direct actuation and user data
Yarime, 2021). These are integrated with intelligent functions including provision in the service layer. The four layers include the data acquisi­
AI, machine learning, and data analytics, can enable precise adjustment tion layer, data modelling layer, simulation layer and service/actuation
and alignment with the actual condition of the city’s infrastructure by layer. The data acquisition layer plays a pivotal role in autonomous
integrating data from diverse sources in real time (Ivanov et al., 2020). gathering and conveying data to the digital modelling layer, which is
These models are crafted to precisely mirror and forecast the present and tasked with maintaining an up-to-date digital counterpart of the actual
prospective conditions of their physical equivalent, both accurately and system. Following this, the simulation layer takes over, analyzing the
swiftly. (Lu et al., 2020). data within the model and forwarding the outcomes to the service/­
actuation layer. It is at this juncture that DT engages with the physical
4.2.2. Urban digital twin architecture system, both by directly influencing it and by supplying information to
The architecture of DT is a multi-layered framework, starting from the users, thereby completing the cycle between virtual and real-world
the physical reality and extending into the cyber domain through entities.
various layers of data handling, modelling, integration, and service de­
livery (Fig. 4). While there is a common consensus among researchers 4.2.3. Advanced technologies for DT development
that the DT architecture is fundamentally layered, the number and The development of a DT relies on a variety of advanced technolo­
function of these layers vary. A summary of these varying layers ac­ gies. This section highlights the key technologies for developing DT,
cording to different authors is provided in Table 3. especially within the realms of urban management and planning.
Lv et al. (2022) propose a four-layer architecture comprising phys­
ical, data, model, and functional layers, emphasizing the progression • 5G-enabled IoT
from tangible and intangible physical elements to the functionalities
that deliver DT’s value. However, Alva et al. (2022) streamline the In the context of Urban DT development, DT requires collecting data
structure into three layers physical, cyber, and cognitive layer, focusing from physical objects or processes to create their digital virtual
on the flow from physical components to a cognitive layer that supports models, a process greatly enhanced by 5G-enabled IoT technologies.
decision-making, emphasizing the user experience and the interpreta­ As highlighted by Wang et al. (2023), 5 G technologies significantly
tive processing of data. Jiang et al. (2022) offer a component-based improve data connectivity and the effectiveness of information
view, identifying five essential parts of a DT: physical, virtual, connec­ dissemination in DT-supported Smart Cities. The 5G-enabled IoT,
tions, data, and service. This perspective encapsulates the functionalities identified as the backbone for dynamic data collection and feedback
of the layered architectures but focuses on the interconnectivity and the mechanisms, serves a vital function in data acquisition and trans­
operational aspects of DTs. mission layer (Lu et al., 2020). This technology is pivotal in enabling

Table 2
Distinction between digital twin and other digital representations (adapted from Austin et al. (2020); Hämäläinen, (2020); Quek et al. (2023); Sepasgozar, (2021);
Shahat et al. (2021)).
Feature Traditional 3D BIM Model Digital Shadow Digital Twin
Models

Static or Dynamic Static digital Static digital representation with Static with dynamic updates Dynamic digital representation
Representation representation physical information for management
Data Flow None Manual interaction with physical data Unidirectional data transfer Bi-directional and continuous data flow
from physical to virtual
Update Mechanism Do not update over Require manual data entry Automatic, but only one way Automatic and integrated in both directions
time physical to digital
Synchronisation Static, no Static, manual synchronization Updates from physical to digital, High-level, real-time synchronization
synchronization no reverse influence
Control None None or limited to the design phase No control over the physical Sends control information to the physical
system system
Integration Visual representation Design and construction information Partial, with updates from the Full cycle of data exchange and management
only physical system only between physical and virtual system

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Fig. 4. Urban DT architecture (Adapted from Ferré-Bigorra et al. (2022); Lu et al. (2020); Lv et al. (2022)).

massive IoT connectivity, essential for the real-time data processing Deng et al. (2021) highlight that the technology of surveying and
and management of urban services (Deng et al., 2021). This inno­ mapping in urban environments encompasses two main areas: first,
vative concept, evolving from embedded computing, sensor net­ the assessment of a city’s topography, environmental features, and
works, and ubiquitous systems, enables various objects in our spatial layout; and second, the integration of this data into a cohesive
surroundings to interact and collaborate towards fulfilling the col­ system using GIS. The surveying phase involves four key technolo­
lective objectives of the city (Mylonas et al., 2021). gies: tilt photography, the use of Unmanned Aerial Vehicles (UAVs),
3D laser scanning, and GPS. For the mapping phase, the focus is on
• GIS and BIM Integration two technologies: the reconstruction of real-world three-dimensional
models and the processing of geographic data from multiple sources.
The integration of GIS with BIM marks a significant advancement in Tilt photography and UAV technology enable immediate and precise
the representation and management of urban environments. As collection of orthographic, tilted, or LiDAR point cloud data in urban
outlined by Deng et al. (2021), DT in the city context creates a virtual areas, considerably decreasing the amount of field mapping
mirror of the physical city, facilitating various operations such as required. They facilitate detailed measurements of urban features
disassembly, duplication, transfer, modification, deletion, and from various perspectives, encompassing land, air, waterways, and
repeated manipulation of its digital equivalent. The combination of subterranean areas. 3D laser scanning employs high-speed laser
GIS and BIM brings together the strengths of both systems: GIS’s scanning and distance measurement to gather extensive dense point
geospatial analysis capabilities and BIM’s detailed architectural and cloud data, including 3D coordinates, reflectivity, and surface detail.
structural data. This integration offers a more holistic and multidi­ It is instrumental in creating detailed 3D models and various map
mensional view of urban landscapes, allowing for enhanced data such as lines, areas, and volumes rapidly. GPS technology ac­
real-time visualization and analysis of urban spaces. However, quires global coordinate point cloud data with high positional ac­
Shirowzhan et al. (2020) note that while this combination enables curacy, enhancing the precision and reliability of surveying data.
real-time visualization and analysis, it also faces challenges with
interoperability, geospatial big data computation, and maintaining These technologies collectively form the technological foundation of
data integrity across multi-cloud environments. DTs, facilitating the collection, integration, processing, and secure
management of urban data (Deng et al., 2021). The amalgamation of the
• Surveying and Mapping Technology above technologies is crucial for the successful implementation and
operation of DTs in urban management and planning.

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Table 3 Table 4
Urban Digital Twin Layers. Existing DT tools and products.
Source Digital Twin Description Tool/Product Company Applications Key Features Use Case
Layers
3DEXPERIENCE Dassault City-scale DTs, Designing 3D Virtual
Lv et al. (2022) Physical layer Tangible elements and objects within Systemes 3D modeling, models, Singapore
the actual environment. simulation simulating DTs, Jaipur
Data layer Physical layer’s data collection and and
storage. information
Model layer Creating of models which depict the management
physical entities Azure Digital Microsoft Knowledge DTDL language, NA
Functional layer Applications and services that use the Twins graphs, integration
models to deliver value. environment with Azure IoT
Alva et al. (2022) Physical layer The real-world physical components. modelling services, live
Cyber layer The digital representation and graph
processing of the physical layer. visualization
Cognitive layer The decision-making and intelligence HxDR Hexagon 3D replicas of Subscription- NA
layer interprets data and supports user urban based SaaS,
experience. environments combines
Jiang et al. (2022) Physical The actual physical entities. heterogeneous
Virtual The digital counterparts of the physical data, 3D model
entities. focus
Connection The links between physical and virtual SmartWorldPro CityZenith Consolidation Unity engine, Amaravati
entities. of BIM, CAD, SDK for
Data The data generated and used by the DT. GIS, and IoT extension,
Service The services provided by the DT to sensors scalable from
users. buildings to
Lu et al. (2020) Data acquisition Collection of data from various sources. cities
layer 51City OS-POS 51WORLD Virtual world Visualization Jiangbei
Transmission layer Transfer of data to other layers. integration front-end, 3D New
Digital modelling Creation of digital representations like and control model tool, District,
layer BIM and CIM. urban Shanghai
Data/model Integration and manipulation of data management
integration layer and models. solutions
Service layer Delivery of services to society and Twin Builder ANSYS Simulation 1D model NA
interaction with integrated data/ scenarios, IoT creation, ready
models. integration simulation
Ferré-Bigorra Data acquisition Autonomous data gathering from the models
et al. (2022) Layer physical world. iTwin Platform Bentley Building DT Cloud platform, Helsinki
Data modelling Maintenance of an up-to-date digital Systems applications, APIs for DT Dublin
layer counterpart. reality data apps, NVIDIA
Simulation layer Analysis within the model to predict and management Omniverse
analyze outcomes. integration
Service/actuation Interaction with the physical system and Optimal Reality Deloitte Traffic and Real-time data NA
layer provision of information to users. network ingestion,
scenarios dynamic
modelling, web
4.2.4. Existing DT tools and products portal access
ArcGIS ESRI GIS, Reality PaaS offering, Boston
The literature analysis further provides an overview of various
Capture, BIM real-time IoT Rotterdam
software solutions and platforms that are currently available for creating data and AI
and managing DT, especially within smart cities’ framework. Table 4 integration integration,
summarizes the tools and products, along with their key features, ap­ location
plications and use case examples adapted from Mylonas et al. (2021). services
ICL Digital Twin IES Insights on 3D models, NTU
energy, simulation Singapore
4.3. Smart city and urban digital twin operations, engine, real-
carbon, capital time data, AI
algorithms
The idea of a smart city has been a major driving force in the digital
GE Solutions General Asset, Solutions for NA
transition initiatives of urban areas (Hämäläinen, 2020, 2021). This Electric Network, various use
concept is fundamentally composed of technological, human, and Process DTs cases, Predix
institutional elements which collectively enhance the management of IoT platform
various urban sectors including transport, environmental concerns, en­ integration
Mindspere Siemens DT hosting, Cloud-based NA
ergy, waste, public safety, and education (Mohammadi et al., 2020; data from IoT platform,
Ricciardi & Callegari, 2023). In this context, the integration of advanced products/ part of
technologies, particularly the implementation of urban DTs, is poised to systems Xcelerator
amplify the effectiveness of smart cities in governing these built envi­ portfolio
Descartes Labs Descartes Analytics Data refinery, NA
ronments (Ricciardi & Callegari, 2023). These virtual models encapsu­
Platform Labs geospatial modelling
late the core aspects of smart cities—technological infrastructure, platform environment,
human capital, and governance structures and extend their application analytics
to critical areas such as urban planning, physical and ICT infrastructure, solutions
and intelligent solutions. By incorporating additional domains like
disaster management, tourism, and economic activities, the smart city
framework not only fosters environmental and economic sustainability
but also supports a more resilient and adaptive urban development
(Shahat et al., 2021)

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4.3.1. Operational aspects of smart city’s digital twin • Buildings Layer: The Buildings Layer in a digital twin city in­
As per (White et al., 2021), a smart city’s DT development can follow corporates highly detailed models of existing buildings, using BIM,
a six-layered hierarchy of data and information, ranging from the most 3D laser scanning and 3D data, to produce precise virtual represen­
fundamental level to the highest. This model, as depicted in Fig. 5, is tations of the built environment (White et al., 2021). DTs employing
composed of six distinct layers: terrain, buildings, infrastructure, technologies like AI, machine learning, and semantic modelling can
mobility, digital layer/smart city, and virtual layer/digital twin. These classify buildings according to their energy usage, thereby opti­
layers can be presented as: mizing energy efficiency. Additionally, the real-time monitoring and
benchmarking of building energy use are integrated into this layer,
• Terrain Layer: This foundational layer maps the physical geography contributing significantly to efficient energy and carbon manage­
of the city and includes its topographical, geological, and hydro­ ment in buildings (Mylonas et al., 2021). These integrations ensure
logical elements. This layer details natural features like water bodies, that the building layer not only represents the physical structure of
gradients, and soil types, which are crucial for understanding the the urban environment but also serves as a dynamic tool for sus­
city’s environmental context and potential challenges (White et al., tainable and efficient energy management, playing a vital part in
2021). This layer can be employed to model water-related risks such reducing city’s total carbon footprint.
as flood scenarios. These models can provide valuable insights into • Infrastructure Layer: The third layer incorporates the fundamental
flood dynamics, such as how water flows through urban terrains, services and infrastructures, including roads, power, telecommuni­
identifying potential high-risk areas, and evaluating the effectiveness cations, water distribution and sewer networks sourced from
of existing water management infrastructure. This information is comprehensive databases like OpenStreetMap and enhanced with 3D
essential for developing proactive measures and strategies to prevent mapping for topographical accuracy. DTs monitor and maintain city
or minimize the impact of risks such as flooding, earthquakes, and infrastructure like roads and utilities, integrating data into BIM
extreme weather conditions (Mylonas et al., 2021). models and GIS (Wang et al., 2023). This aligns with the

Fig. 5. Layers required to develop a smart urban DT, adapted from White et al. (2021).

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D. Peldon et al. Sustainable Cities and Society 111 (2024) 105583

infrastructure layer, focusing on the health and functionality of

essential city structures. One such example can be DTs’ application in
real-time modelling of urban stormwater networks is relevant to this
layer, helping manage infrastructure related to water distribution
and quality (Elsehrawy et al., 2021).
• Mobility Layer: Here, the movement of people and goods is simu­
lated, integrating various transportation modes and behaviours to
understand traffic patterns and optimize urban mobility. Data from
video surveillance, public transport databases, and sensors are used
to model traffic flows and enhance transportation systems
(Ferré-Bigorra et al., 2022).
• Digital Layer/Smart City: In a study by White et al. (2021), the
Digital Layer/Smart City of a digital twin has become a central hub
for integrating IoT sensors throughout the urban landscape. These
sensors collect a vast array of data, which is crucial for the real-time
monitoring and effective management of various urban services. This
comprises transportation, infrastructure, waste disposal, safety, in­
stitutions, healthcare, and other community amenities. This layer
collects essential data for simulations in the following Virtual
Layer/Digital Twin, drawing inputs from the preceding layers. In­
sights acquired from simulations are then distributed throughout the
city’s layers as actionable intelligence. Data sources in this stratum
are varied, encompassing citizens, mobile devices, and assets across
the urban landscape.
• Virtual Layer/Digital Twin: As noted by White et al. (2021), this
Fig. 6. Stages of DT development, adopted from Petrova-Antonova and
layer is an integral component that utilizes data from the Digital
Ilieva, (2019).
Layer/Smart City for advanced simulations and analyses, crucial for
urban planning and management. This layer effectively in­
terconnects with the Digital Layer, enabling a bidirectional flow of In essence, an Urban DT serves as a dynamic, interactive model that
data. It is instrumental in specific use cases, such as renewable en­ not only reflects the current state of an urban environment but also
ergy planning, where offshore wind data informs simulations for the predicts future conditions and informs decisions that shape the real-
optimal placement and sizing of wind turbines, considering factors world city. This iterative process ensures that DT evolves in tandem
like visual impact and navigation routes. Another key application is with the city it represents, furnishing a potent instrument for urban
in building construction, where the layer assesses the impact of new planning and administration.
structures on sunlight distribution and structural integrity based on
wind and seismic data. Additionally, the Virtual Layer/Digital Twin 4.4. DT application in urban planning and smart city domain
actively involves citizens in urban development, allowing them to
provide feedback on proposals like new buildings and park designs, The Urban DT bridges the gap between smart cities’ theoretical
thereby enhancing participatory planning. This interactive platform concepts and actual interventions as it helps optimize a smart city’s
not only gathers public input but also informs city councils and urban performance (Petrova-Antonova & Ilieva, 2019). It can capture real-time
planners, facilitating informed decision-making. Through this layer, data (Shirowzhan et al., 2020), allowing visualisation of all resources
the digital twin city becomes a dynamic tool for visualization, and interactions in the city which then helps monitoring of infrastruc­
experimentation, and collaborative urban management (White et al., ture, utilities, businesses and planning future developments (Mashaly,
2021). 2021). It provides insights into urban efficiency, prompting real-world
measures such as modifications to city design or transportation
4.3.2. Developmental stages of smart urban DT methods (Petrova-Antonova & Ilieva, 2019).
The development of an Urban DT involves a sophisticated integration Mylonas et al. (2021) have discussed various smart city domains for
of the physical and virtual realms where Petrova-Antonova and Ilieva which an Urban DT can be utilized, including building surveillance,
(2019) propose six interconnected stages to apply. It includes six distinct urban planning, circular economy, transport, risk alleviation and
stages: create, interact, aggregate, analyse, insight and the decision healthcare and sustainability. Similarly, (Alva et al., 2022) have listed
stage (Fig. 6). six use cases for the Urban DT including predictions and scenario
First, the ’Create’ stage lays the groundwork by assembling a CIM modelling, preparing for emergencies, optimizing functionalities, and
(City Information Model) from diverse data sources, including sensor formulating strategies. The applications in urban development and
readings and existing municipal information systems. During the administration, transport, environment and energy, and disaster are also
’Interact’ phase, a real-time, bidirectional communication is established discussed in studies by Yang and Kim (2021), Wang et al. (2023),
between city’s physical and virtual counterparts, employing cutting- Ferré-Bigorra et al. (2022) and (Elsehrawy et al., 2021). These various
edge communication and edge computing technologies. The ’Aggre­ application aspects are categorized into nine application areas in
gate’ stage then centralizes and refines this data, ensuring its readiness Table 5.
for analysis. In the ’Analyse’ stage, AI and cognitive computing tech­
niques are applied to extract meaningful insights. The ’Insight’ stage 4.4.1. Mobility management
takes these insights and renders them into visual formats, such as 3D DTs in mobility management involve a comprehensive approach to
models and dashboards, to highlight areas for potential improvement. optimizing urban traffic and transport systems (Elsehrawy et al., 2021).
Finally, the ’Decision’ stage translates these insights into actionable Utilizing data from video surveillance, public transport databases, and
strategies, aiming to enhance the physical city’s operations to mirror the sensors, DTs model and simulate traffic patterns, including peak hour
efficiency and optimization modelled in its DT (Petrova-Antonova & flows, across various modes of transportation like roads, private trans­
Ilieva, 2019). port, and public transport (Ferré-Bigorra et al., 2022). These simulations

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Table 5
Application aspects of urban DT.
Application Aspect DT layers Tools and
Technologies
Physical Data acquisition Modelling Simulation Service

Mobility Road infrastructure, Video surveillance, Mobility simulation traffic flow and peak Optimization of traffic GPS tracking, traffic
Management private transport, public transport model hour simulation flow, fleet operations, management software
public transport database, sensors public transport systems,
road infrastructure
management
Urban Planning Buildings, terrain, LiDAR point clouds, City information Visualisation and New development LiDAR, cyberGIS, BIM,
infrastructure IoT device data model, BIM models Scenario planning for planning, Participatory AI, Machine Learning,
urban development planning, policy IoT, Unity3D
proposals implementation
Public City infrastructure Sensors for structural Infrastructure BIM Simulations of Infrastructure maintenance IoT sensors, BIM, GIS,
Infrastructure (roads, bridges, health monitoring, models, Geospatial infrastructure usage, scheduling Structural analysis
Management utilities) environmental data data integration stress testing software, Simulation
collection software
Risk Mitigation Buildings, terrain, Sensors for Risk assessment Flood, earthquake, Emergency response GIS, Remote sensing,
and disaster infrastructure, environmental models and extreme weather coordination, disaster Structural analysis
management transport monitoring, structural simulations mitigation strategies software, Simulation
health monitoring platforms
systems
Energy Electrical grid Electric meters, energy Energy simulation Simulate energy Real-time energy usage AI, machine learning
Management (electrical power consumption sensors model distribution and monitoring and
transmission and consumption benchmarking, automated
distribution network) energy conservation
measures
Water and Water distribution Water meters, water Hydrological models Fluids dynamic Water supply and sewage GIS, IoT
resource network, sewerage, quality monitors and model treatment and stormwater
management and stormwater sensors management
network
Environmental Transport, buildings, CO2 sensors, air Atmospheric Pollution and carbon Pollution monitoring and Environmental
and carbon factories quality monitors, noise pollution, emission simulations control, carbon emission monitoring sensors
management detectors meteorology, and reduction strategies, waste
climatology management,
modelling

enable effective traffic flow optimization, fleet operations management, GIS, and structural analysis software enables effective management of
and public transport system enhancements (Mylonas et al., 2021). Tools urban infrastructure, ensuring its longevity and optimal performance
like GPS tracking and traffic management software play a crucial role in (Elsehrawy et al., 2021).
analyzing data and implementing changes to improve overall mobility
and reduce congestion (Wang et al., 2023). 4.4.4. Resilience and disaster management
DTs are emerging as a powerful tool in risk and resilience manage­
4.4.2. Urban planning ment within urban environments, demonstrating a strong capacity to
In urban planning, DTs leverage innovative methods like LiDAR- simulate and mitigate infrastructure-related risks (Elsehrawy et al.,
generated point clouds and IoT device data to create spatial 3D city 2021). Water-related risks, particularly flooding, have been a focus for
models of cities, enabling the unsupervised and detailed representation hydrological DTs, which help create safer urban spaces by linking with
of urban environments (Mylonas et al., 2021). This approach is pivotal traffic and urban planning DTs (Mylonas et al., 2021). White et al.
for addressing complex city modelling challenges, such as unknown (2021) in their research, presents DT utilization to model flood sce­
taxonomies of city objects, thereby enriching the accuracy and utility of narios, providing valuable insights into flood dynamics and potential
urban DTs. This enables visualizations and scenario planning for urban preventive measures. DTs utilize sensors for environmental monitoring
development proposals, facilitating participatory planning and informed and structural health to model risks associated with natural disasters
policy implementation (Alva et al., 2022). Furthermore, the incorpora­ such as floods, earthquakes, and extreme weather events. These models
tion of additional advanced technologies like cyberGIS, BIM, AI, and are crucial for running simulations that inform emergency response
machine learning technologies (Banihashemi & Khalili et al., 2022) form strategies and disaster mitigation plans (Alva et al., 2022). Tools like
an integrated data management system, crucial for creating dynamic, GIS, remote sensing, and structural analysis software aid in simulating
responsive DTs of cities. The multilayered nature of DTs allows for potential disaster scenarios, enabling cities to prepare and coordinate
comprehensive simulations, offering visual feedback on proposed urban responses effectively, thus enhancing urban resilience.
changes and potential consequences, thus making urban planning more
dynamic, inclusive, and effective (Mylonas et al., 2021). 4.5. Sustainability aspects of digital twins in urban development

4.4.3. Public infrastructure management The integration of DTs in smart city frameworks facilitates the
DTs in public infrastructure management focus on monitoring and enhancement of urban sustainability by enabling real-time monitoring
maintaining critical city infrastructure like roads, bridges, and utilities and city infrastructures administration (Shahat et al., 2021). This is
(Wang et al., 2023). They employ sensors for structural health moni­ particularly evident in the application of DTs for urban planning within
toring and environmental data collection, integrating this data into BIM the Green Deal era, where they bridge global sustainability policies with
models and geospatial information systems (Khoshamadi et al., 2023). local urban needs (Caprari et al., 2022). The DTs’ sustainability aspects
Simulation tools are used to test infrastructure under various stress in urban planning and design include energy management, water
scenarios, guiding maintenance scheduling and real-time service ad­ management and resource management, and environmental and carbon
justments (Mylonas et al., 2021). The integration of IoT sensors, BIM, management. Fig. 7 shows these sustainability aspects and their

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D. Peldon et al. Sustainable Cities and Society 111 (2024) 105583

Fig. 7. DT Sustainability aspects (adapted from Ferré-Bigorra et al. (2022)).

corresponding layers and elements within a DT framework. carbon management efforts primarily through pollution monitoring,
waste management and real-time monitoring and benchmarking of en­
4.5.1. Energy management ergy use within urban settings (Elsehrawy et al., 2021). They utilize a
As detailed by Mylonas et al. (2021), DTs leverage advanced tech­ variety of data, including traffic dynamics, to predict and mitigate air
nologies like AI, ML, and semantic modelling to enhance energy effi­ pollution. This predictive capability allows for proactive measures in
ciency and decision-making processes in smart cities. Semantic traffic management, potentially leading to reduced emissions (Else­
modelling and data-driven rule-based reasoning are used to collect and hrawy et al., 2021). Furthermore, GIS-based DT applications integrate
process data, enabling the identification of patterns and automating GIS, BIM, and IoT to deliver real-time pollution data, which is integrated
decisions. This approach generates an intricate model, which can be into the model’s data layer, enhancing urban monitoring, and offering
utilized to classify buildings and urban areas for energy usage, aiding in technology-independent solutions (Mylonas et al., 2021). Herrenberg’s
the categorization of buildings and urban blocks for better energy city-scale DT prototype incorporates sensor data into computational
management (Banihashemi & Golizadeh et al., 2022). Furthermore, models for air pollution simulation to visualize pollution levels,
urban scale models use city building datasets and 3D representations to addressing the city’s main environmental issues and promoting digital
simulate energy consumption, providing a basis for evaluating energy solutions for urban planning (Dembski et al., 2020).
efficiency at the city level. This helps in identifying the best strategies for Furthermore, the potential of DTs in revolutionizing waste man­
sustainable urban development and supports the implementation of agement within smart cities points towards a zero-waste sustainability
energy and environmental policies. goal, reflecting the current research interest in sustainability and
Moreover, DT platforms support real-time energy benchmarking by circularity (Mylonas et al., 2021). In waste management, DTs are facil­
transforming raw energy data into valuable insights, that city energy itating more efficient processes through the integration of IoT and
managers can use to visualize energy usage and formulate effective computer vision technologies (Elsehrawy et al., 2021). By monitoring
energy conservation strategies. Additionally, integrations of BIM and the fill levels of smart bins, DTs can inform waste collection teams about
IoT technologies within DTs monitor and regulate building energy ef­ the optimal times for collection and propose the most efficient collection
ficiency and indoor environmental quality, aligning with broader envi­ routes. This not only streamlines waste management logistics but also
ronmental protection and social well-being goals (Mylonas et al., 2021). contributes to the reduction of the carbon footprint associated with
waste collection services. Additionally, in the realm of energy con­
4.5.2. Water and resource management sumption, DTs enable real-time monitoring and benchmarking of
DTs are increasingly pivotal in resource management as they aid in building energy use, aiding in the optimization of environmental per­
identifying optimal areas for infrastructure investment. DT technology is formance and supporting the achievement of carbon reduction targets
also applied to the real-time modelling of urban stormwater networks, (Elsehrawy et al., 2021).
offering a proactive approach to managing overflow and water quality These examples underscore the versatility of DTs in enhancing
events (Elsehrawy et al., 2021). A DT prototype in Newcastle, UK, aims various aspects of urban environmental management, from improving
to monitor water network malfunctions and manage heavy rainfall im­ air quality to optimizing energy usage and revolutionizing waste man­
pacts, while Valencia, Spain, has implemented a DT for its water dis­ agement practices. By leveraging DT technology, cities can take signif­
tribution network management (Mylonas et al., 2021). icant strides towards sustainability and more effective carbon
management.
4.5.3. Environmental and carbon management In summary, DTs offer a promising avenue for supporting sustain­
DTs are proving to be instrumental in advancing environmental and ability in urban environments. Their ability to simulate, predict, and

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manage urban systems presents a forward-thinking approach to 4.6.2. Agent-based modeling and spatial behaviour
achieving resilient and sustainable urban ecosystems. As the technology Agent-based models (ABMs) have been effectively used to not only
matures, it is imperative to develop common metrics and computational simulate pedestrian flows but also to delve into more nuanced spatial
tools to support the diverse stakeholders involved in urban behaviours and relationships within urban areas. These models, as
development. described by Bonabeau (2002), represent individuals as autonomous
agents, but their potential extends to capturing complex interactions
4.6. Integrating social-community dynamics into urban DT within specific urban contexts—such as interactions within public
spaces, responses to urban changes, and the dynamics of crowds during
As discussed in preceding sections, DTs have emerged as powerful various urban events (Ye et al., 2023). Simulating the behaviours and
tools for simulating and managing cities’ infrastructures and environ­ interactions of individual agents based on different scenarios can help
mental systems (Xu & Liu, 2024). However, to truly reflect the predict how changes in the urban environment might affect human
complexity of urban environments, DTs must also incorporate the social behaviour and social structures (Ye et al., 2023). So, integrating ABMs
and community dimensions that underlie the human experience in cities into DTs allows for the simulation of social interactions and mobility
(Shahat et al., 2021). This integration ensures that this technology re­ patterns, thereby enhancing the predictive capabilities of digital twins
flects not only the physical characteristics of urban environments but (O’Sullivan & Perry, 2013).
also the vibrant social interactions and community behaviours funda­ To enhance the predictive capabilities of digital twins and their
mental to urban vitality (Dembski et al., 2020). Recognising this need applicability to real-world scenarios, advanced ABM techniques have
also reflects a growing trend in urban systems modelling that seeks to been implemented:
combine technological advances with insights from social sciences
(Batty, 2018). • Modelling Interactions in Varied Environments: Urban settings such
Cities are complex ecosystems characterized by diverse social in­ as residential areas, commercial zones, and recreational spaces are
teractions and relationships (Shahat et al., 2021). By embedding these modelled to understand how spatial configurations influence social
elements into DTs, planners gain deeper insights into how urban spaces interactions and community engagement (Crooks & Heppenstall,
are utilized and how various planning decisions impact community 2011).
well-being. These elements of social dynamics can be incorporated from • Simulating Scenario-based Planning: Various "what-if" scenarios are
various sources of data and methodologies. Social data integration uti­ simulated using ABM to observe how changes in urban design, like
lizes surveys, social media, and public forums to gather qualitative in­ new pedestrian zones or public transit routes, impact community
sights into community sentiments and community needs, while IoT behaviour and spatial usage (Batty, 2008).
devices and sensors can help collect data on social interactions and • Integrating Real-time Data: The granularity and accuracy of simu­
human activity patterns within urban spaces (Sohi et al., 2024). These lations are enhanced by incorporating real-time data from sensors
data can help simulate the social behaviours of urban populations and be and IoT devices, allowing urban planners to observe and analyze the
analyzed to improve public areas and services (Shahat et al., 2021). immediate impacts of spatial changes on community behaviour (Ye
Additionally, agent-based modelling can be utilized in simulating indi­ et al., 2023; Zheng et al., 2014).
vidual and collective behaviours to predict social outcomes of urban
planning decisions (Batty, 2013). This can further promote participatory Furthermore, these sophisticated simulations are integral in partici­
design by actively engaging citizens directly in the urban planning patory planning processes. They provide stakeholders with visual and
process through interactive platforms and virtual reality simulations data-driven insights into potential urban developments and their im­
(Elsehrawy et al., 2021). pacts, fostering a collaborative environment for urban design. Projects
like the Virtual Singapore initiative, underscore the practical benefits of
4.6.1. Social behaviours simulation this approach by enabling more informed decision-making regarding
DTs can simulate social behaviours and interactions within urban urban planning and development.
spaces, which can help urban planners understand how people use
public spaces, their movement patterns during different times of the day 4.6.3. Participatory and collaborative planning
or special events, and their interactions within the community (Dembski Beyond social behaviour simulation and ABM, DTs empower citizens
et al., 2020). This understanding can lead to better design of public and encourage their active participation in the decision-making pro­
spaces that cater to the needs of the population, improve pedestrian cesses that shape their future cities, promoting a more human- or citizen-
flows, enhance safety, and increase the usability of urban areas (Weil centric approach to urban planning (Elsehrawy et al., 2021; Nochta
et al., 2023b). For instance, data-driven simulations determine the et al., 2021). Engaging citizens directly in urban planning through
optimal locations for public amenities like parks, benches, and play­ interactive platforms and virtual reality simulations allows them to
grounds, or identify areas where improvements are necessary to visualize changes and contribute ideas. This approach not only de­
enhance accessibility and encourage more community interaction mocratizes urban planning but also ensures that a DT’s outputs align
(White et al., 2021). This is particularly beneficial in designing neigh­ with the residents’ expectations and lived experiences(Dembski et al.,
bourhoods that promote social interaction among residents, which is a 2020). In Herrenberg, Germany, the city leveraged a DT to engage the
key component of mental health and well-being. community in the urban planning process. Through VR and AR tech­
Additionally, modelling crowd movements during public events or nologies, residents were able to visualize and provide feedback on urban
even daily commutes allows urban layouts to be optimized for improved development proposals, leading to designs that closely matched com­
accessibility and reduced congestion, enhancing the quality of urban life munity preferences (Dembski et al., 2020).
(Batty, 2013; Townsend, 2013; White et al., 2021). By modelling how Furthermore, the integration of DTs also facilitate a collaborative
these events affect traffic, pedestrian flows, and public transportation, environment where stakeholders from different city domains can co-
planners can make informed decisions about event locations, timings, create and develop the digital twin (Shahat et al., 2021). Online and
and the necessary infrastructure improvements to support activities open platforms enhance data sharing and stakeholder inclusion in urban
(Elsehrawy et al., 2021; White et al., 2021). The use of DTs to model planning, policy design, and evaluation. This collaborative approach
social dynamics can be seen in projects such as the Virtual Singapore ensures that DTs are not only tools for visualization but also platforms
initiative. This digital twin incorporates human activity data to simulate for community interaction and public decision-making (Shahat et al.,
scenarios like public gatherings and emergency evacuations, providing 2021). By providing various levels of authorization, this technology can
valuable insights into urban management and planning. be made accessible to different stakeholders, allowing them to navigate

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and discuss urban planning issues openly. This accessibility is crucial for understanding the sustainability and practicality of DTs, especially
fostering public engagement and incorporating a wide range of per­ when applied to complex systems like cities and urban development.
spectives in urban development (Shahat et al., 2021). These limitations as indicated in Fig. 8, include data management and
By integrating these methodologies, DTs not only simulate the integration, data quality and synchronisation, interoperability, stan­
physical and environmental aspects of urban life but also actively dards and connectivity, cybersecurity and data privacy, data modelling,
involve citizens in shaping their urban environments. This approach not utilization and visualisation, technical complexity and infrastructure,
only enriches the DT functionality but also ensures urban interventions financial constraints and resource allocation, and socio-political impli­
are socially inclusive and aligned with the community’s aspirations. This cations and public engagement (Fig. 8).
approach promotes greater public engagement and acceptance, result­
ing in urban environments that are not only efficient but also vibrant and 4.8.1. Data management and integration
inclusive. The city data is often large, complex, and heterogeneous, requiring
high computing power and interoperability for effective acquisition and
4.7. DT in urban planning and development- case studies processing. There is a need for universally acknowledged standards for
data models and design schemas to streamline city modelling and to
The concept of an urban DT, applying DT technology to urban en­ reduce time, cost, and errors (Shahat et al., 2021). Lu et al. (2020)
vironments, is increasingly seen as a transformative tool for enhancing discuss the multifaceted nature of data integration, which is a founda­
urban planning and fostering the creation of successful smart cities tional challenge in DT development. The disparate nature of data
(Yang & Kim, 2021). As DT offers new vistas for urban planning sources requires sophisticated Extract, Transform, and Load processes,
(Schrotter & Hürzeler, 2020), this industry is increasingly intrigued by service-oriented architectures, and data virtualization techniques. The
the potential of DT implementation in enhancing planning and asset authors stress that the heterogeneity of source data systems and the lack
management in addition to developing safe and sustainable urban of standard identifiers across these systems make efficient data extrac­
spaces (Ferré-Bigorra et al., 2022). tion and linkage a complex task.
Even though DT is an emerging field with a wide array of potential
use cases (Barresi, 2023; Dembski et al., 2019), it is relatively new in the 4.8.2. Data quality and synchronization
urban management field, with only a handful of cities and towns Data quality which is a critical factor for the utility of DTs is another
currently utilizing operational urban DTs (Dembski et al., 2020; concern. The data encapsulation within each DT is necessary to maintain
Ferré-Bigorra et al., 2022). To understand the real-world implementa­ quality, as the responsibility for data integrity cannot be offloaded (Weil
tion of DTs at the city level and within the urban planning context, a list et al., 2023a). The data synchronization, especially in real-time moni­
of five case studies, mostly prototypes, from the existing literature is toring scenarios, poses challenges where the quality of data can be
summarized in Table 6. It demonstrates their purpose, technologies used compromised by the need for speed and efficiency (Lu et al., 2020).
and applications along with the limitations of each case.
4.8.3. Interoperability, standards, and connectivity
A significant limitation is the absence of widely accepted standards,
4.8. Limitations and barriers
impacting urban digital twins’ capacity to exchange data with other
municipalities or entities (Weil et al., 2023a). This lack of interopera­
Despite the benefits and various potential application aspects of DTs,
bility and standardization means that only a few urban DTs can ex­
there is a multitude of challenges associated with their development and
change data effectively (Ferré-Bigorra et al., 2022). It can result in
implementation for smart city governance and urban planning (Zhang
misinterpretation of data, leading to potential safety and security risks,
et al., 2022). This is evident in the preceding section outlining some of
as well as suboptimal city performance across various sectors
the real-world case studies. These challenges are crucial for

Table 6
Case studies and their DT use cases.
Case Studies Purpose Technology Application Limitation

Herrenberg, Urban planning, urban design, and 3D model (DEM), Laser Scan, Participatory and collaborative processes The model does not encompass the
Germany ( decision support; to improve BIM, VR and AR for in urban planning, urban design, decision- entire information in the actual
Dembski et al., collaborative planning processes visualization, making support, and visualization in VR environment; the need for
2020) for public participation. Traffic planning additional socio-economic and
scenarios, urban mobility simulation, environmental data
airflow simulation
Zurich, Switzerland Urban planning, decision-making, 3D spatial data models, Java Urban planning, decision-making, scenario Complexity in modelling urban
(Schrotter & public awareness, and participation Script API from Esri, BIM development, public participation, climate objects, integration of real-time
Hürzeler, 2020) models, mobile mapping, point analyses, architectural competitions data, high computational
cloud data, LIDAR requirements
Kalasatama district, To monitor the entire lifecycle of 3D modelling, IoT, data Simulating and observing the impact of High computing capacity required
Helsinki, Finland the district’s-built environment. To analytics, AI, ContextCapture changing weather conditions on the for generating 3D models,
(Hämäläinen, offer an avenue for smart city design application for 3D mesh model district, evaluating solar energy potential, laborious data cleaning and
2021) and testing, application, and service creation analyzing storm wind influence, preparation for 3D models
development stakeholder collaboration in urban
development
Docklands area, Urban planning and policy Unity3D Software, IoT devices, Designing cityscape and green spaces; The initial approach did not
Dublin, Ireland ( decisions; citizen feedback on WebGL, Stereoscopy, SUMO, flood and crowd simulations; engaging include urban mobility data; the
White et al., planned changes; tagging and Online Digital Twin, 3D, and citizens in urban planning decisions model was not publicly available
2021) reporting problems in the area Urban Mobility Model initially, limiting citizen
interaction and feedback
Cambridge (Nochta To support urban planning and Data and modelling insights, Conceptualizing, designing, and Obstacles in collating data from
et al., 2021) management, improve decision- sensory technology, 3D implementing data-driven solutions and diverse sources, complexity in
making, and contribute to urban visualization, big data digital tools for urban "smartification" modelling, and demand for
sustainability goals analytics outcome-oriented strategies and
policies.

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Fig. 8. Limitations of DTs for smart city governance and urban planning.

(Petrova-Antonova & Ilieva, 2019). Interoperability issues affect data required for particular tasks is a critical challenge. In DT-supported
connectivity, making it difficult to exchange data between Smart Cities cities, there is a neglect in data utilization, primarily due to the insuf­
(Wang et al., 2023). While establishing data standards is a current so­ ficiency of computing resources to handle city-wide and unstructured
lution, the complexity and diversity of systems involved in smart cities data (Wang et al., 2023).
make it challenging to create a unified city model standard.
4.8.6. Technical complexity and infrastructure
4.8.4. Cybersecurity and data privacy The technical complexity of DTs necessitates a workforce that is not
Cybersecurity is a major concern as a breach and sensitive data leaks only skilled but also sizable enough to handle the design, installation,
could have far-reaching consequences, not just in terms of data privacy and ongoing maintenance (Ferré-Bigorra et al., 2022). The infrastruc­
but also in the potential for malicious control over critical urban infra­ ture required for Urban DTs is not just about the physical hardware but
structure. This raises questions about the legal accountability for de­ also encompasses the software and networking capabilities that are
cisions made by DT and the security of citizens’ data (Ferré-Bigorra needed to process and manage large and complex datasets in real-time
et al., 2022). Data ownership often remains ambiguous, and concerns (Weil et al., 2023a).
about data privacy and security complicate the understanding of data
limitations and permissions for use in various applications (Petro­ 4.8.7. Financial constraints and resource allocation
va-Antonova & Ilieva, 2019). The budget set aside for the deployment and administration of Urban
DTs is constrained, necessitating a balance between cost and function­
4.8.5. Data modelling, utilization and visualization ality. This financial limitation can affect the extent to which DTs can be
GIS and BIM are limited to static data management and require utilized for city management (Ferré-Bigorra et al., 2022).
additional tools to handle real-time data (Wang et al., 2023). Efforts to
integrate GIS and BIM with sensor data for dynamic data management 4.8.8. Socio-Political implications and public engagement
have led to the development of integrated models that encompass ge­ Involving the public and various city departments while ensuring
ography, buildings, and cities. Augmented Reality (AR) and Virtual model access presents challenges due to the absence of governance
Reality (VR) can represent 3D physical entities but are not equipped to frameworks, data-sharing agreements, and perception of stakeholders
handle real-time data flows between the real and its virtual counter­ (Shahat et al., 2021). The socio-political implications of using Urban DTs
parts. The challenge lies in integrating BIM, GIS, AR, and VR to manage have not been fully explored. There is a need for socio-political analysis
data in an unstructured environment, with data privacy and security to understand their nature and implications, and to identify problems
issues being somewhat overlooked in current research (Wang et al., and obstacles that could hinder their broad acceptance (Ferré-Bigorra
2023). et al., 2022)
According to Shahat et al. (2021), current Urban DTs may experience The above limitations underscore the multifaceted nature of the
limited model precision, comprehensiveness, and visual depiction challenges facing DTs and Urban DTs. Addressing these challenges re­
quality. Employing participatory sensing and crowdsourced information quires a concerted effort to improve data management, enhance inter­
to overcome challenges in sensory data has resulted in localization operability, ensure cybersecurity, manage financial resources
inaccuracies and inconsistent data. Inaccuracies and errors in modelling effectively, engage the public and stakeholders, and develop compre­
impacts both the visual representation and city’s actual condition hensive and accurate models.
(Shahat et al., 2021). Moreover, identifying the specific types of data

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5. Research gaps and future directions effectively simulated and integrated into DTs to enhance their realism
and applicability in urban planning. It should emphasize the develop­
The limitations in the previous section highlight the nascent stage of ment of frameworks that not only consider the technological and envi­
urban DT technology and the need for further research and development ronmental aspects but also deeply integrate the social community
to address these challenges. To tackle these issues and gaps, upcoming dimensions that significantly influence urban development. This
studies could concentrate on the development of sophisticated data approach deepens our understanding of urban complexities and im­
management frameworks capable of handling the complexity and het­ proves the participatory design processes that are crucial for creating
erogeneity of urban DT data, ensuring its quality, and achieving real- inclusive and resilient urban environments.
time synchronisation. Additionally, there is a critical need for the Fig. 10 offers a comprehensive view of the multifaceted role that DT
establishment of universal standards that promote interoperability technology plays in urban planning and development. The following
across diverse systems and cities, thereby enhancing the scalability and presents a concise discussion of the various components of the frame­
transferability of DT solutions (Fig. 9). work and their interplays. The model begins by tracing the origin of DT
Furthermore, future research can also investigate how emerging in aeronautics, showing its initial use in creating and testing aircraft
technologies like 5 G, edge computing, and AI can be incorporated models, then moves to its expansion to other sectors. This leads to its
within DT architecture to enhance data processing and real-time ana­ application in urban development, where DTs serve as dynamic and
lytics. Additionally, the study could explore the potential of advanced interactive models for planning and managing cities.
machine learning techniques to enhance the forecasting accuracy of Central to the DT model are key technologies including BIM, GIS and
Digital Twins, enabling better prediction of city dynamics. This can IoT. With this application lens, BIM provides detailed digital represen­
include accurate prediction of environmental conditions, such as air tations of buildings’ physical and functional traits, where GIS offers
quality and water levels, allowing cities to pre-emptively respond to spatial analysis and mapping, and IoT enables continuous and instan­
potential issues and mitigate risks associated with climate change. taneous data collection from a myriad of sensors across the urban
Another research gap that the future study should investigate is the landscape.
carbon emissions and environmental impacts of DTs. The research These key technologies, collectively, deliver the smart city integra­
should delve into the role of DTs in promoting urban sustainability by tion and real-time applications grounded on the operational layers. DTs
monitoring environmental indicators, focusing on reducing the carbon facilitate an interconnected urban environment where real-time data
footprint and promoting environmental and infrastructure resilience. enhances decision-making and efficient utilization of resources.
This can include creating and designing DT modules specifically for Through dynamic simulation and predictive analysis, DTs allow for the
renewable energy management, waste reduction, and optimizing the use real-time application in urban scenarios, significantly improving urban
of natural resources. The DTs could also be used to simulate the impacts planning and public policy support. The operational layers in DT high­
of various green infrastructure and low-impact development initiatives, light the process from data collection to analysis and application, ulti­
such as widespread tree planting or the creation of green roofs, on urban mately leading to informed decisions in urban planning and governance.
heat islands and overall city temperatures (Fig. 9). Hence, DTs make key contributions by enhancing urban planning
Additionally, while our review highlights the integration of social through improved design processes and by promoting stakeholder
and community dynamics within DTs, it is pertinent to note that engagement. They also contribute significantly to resource manage­
research in this area, particularly how these dynamics interact within ment, where monitoring and control of resource distribution become
digital simulations, is still emerging. Future research should focus on more efficient. However, there are multiple challenges and limitations in
how social interactions and community behaviours can be more this process which should be taken into account. The framework

Fig. 9. Research gap identification and future research agenda.

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D. Peldon et al. Sustainable Cities and Society 111 (2024) 105583

Fig. 10. DT application and implementation model in urban environments.

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D. Peldon et al. Sustainable Cities and Society 111 (2024) 105583

identifies the data management complexity arising from the volume and Declaration of generative AI and AI-assisted technologies in the
variety of data, interoperability issues due to system integration chal­ writing process
lenges, and cybersecurity concerns that pose risks to data security and
privacy. The limitations include the technical complexity of DT systems, In preparing this document, the authors employed ChatGPT to
the socio-political implications of their adoption, and the standardiza­ enhance the language and readability of the text. Subsequently, the
tion needs to ensure DT systems can work across various platforms and authors conducted a comprehensive review and revision of the content
cities. as required, taking full responsibility for the publication’s accuracy and
Therefore, looking forward, the model outlines the future research integrity.
trajectory that includes:
CRediT authorship contribution statement
• Developing sophisticated data management frameworks to handle
the complexity of urban DT data. Dechen Peldon: Writing – review & editing, Writing – original draft,
• Establishing universal standards to promote interoperability and Visualization, Validation, Supervision, Project administration, Method­
enhance scalability. ology, Conceptualization. Saeed Banihashemi: Writing – review &
• Integrating emerging technologies such as 5G, edge computing, and editing, Writing – original draft, Visualization, Validation, Supervision,
AI to advance data processing and analytics. Project administration, Methodology, Conceptualization. Khuong
• Applying advanced machine learning techniques for better predic­ LeNguyen: Writing – review & editing, Validation. Sybil Derrible:
tive capabilities concerning environmental conditions and urban Writing – review & editing, Validation.
dynamics.
• Focusing on the sustainability and environmental impact of DTs,
Declaration of competing interest
such as net-zero carbon initiatives and the optimization of renewable
resources.
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
All these components contribute to the overall goal of enhanced
the work reported in this paper.
urban structures, where DT technology not only improves the design and
operation of urban spaces but also leads to sustainable and resilient
Data availability
cities equipped to tackle future challenges. This framework provides a
clear path for understanding and developing DT in urban environments,
No data was used for the research described in the article.
highlighting the interconnectedness of technology, policy, resource
management, and the challenges to be addressed through future
research and innovation.
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