Applications of Artificial Intelligence and Digital Twins in Enhancing Urban Sustainability in Saudi Cities NEOM City as Case Study.pdf
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About This Presentation
This study investigates the transformative potential of digital twin technology in enhancing urban sustainability within Saudi cities, with specialized focus on NEOM as a pioneering case study. The research employs an integrated methodology combining qualitative analysis of strategic frameworks with...
Slide Content
Journal Homepage: www. ijarpr.com ISSN: 3049-0103 (Online)
International Journal of Advance Research Publication and
Reviews
Vol 02, Issue 10, pp 372-394, October 2025
Applications of Artificial Intelligence and Digital Twins in
Enhancing Urban Sustainability in Saudi Cities: NEOM City as
Case Study
Hassan Ahmed Hassan Youssef
1
, Neama Hassan El-Sayed Omar
2
1
Architecture Engineering Department, Alasala Colleges, Dammam 32324, Saudi Arabia. Department of Architecture
Engineering - Al-Safwa high institute of Engineering, Cairo, Egypt. [email protected] -
[email protected]
2 Interior Design Department, College of Architecture and design- Alasala Colleges, Dammam 31483, Saudi Arabia
Architecture Engineering Department, Bilbeis Higher Institute for engineering, Egypt. [email protected]
[email protected]
ABSTRACT
This study investigates the transformative potential of digital twin technology in enhancing urban sustainability within Saudi cities, with
specialized focus on NEOM as a pioneering case study. The research employs an integrated methodology combining qualitative analysis
of strategic frameworks with quantitative assessment of performance indicators to examine how AI-driven digital replicas optimize
urban systems.
The conceptual foundation of digital twins involves creating comprehensive virtual replicas of physical systems to predict and optimize
performance, as established in foundational literature (Grieves & Vickers, 2017). The mixed-methods approach adopted in this research
aligns with contemporary methodological frameworks for analyzing smart city technologies (Bibri, 2021).
Findings demonstrate significant progress across multiple sectors, with AI adoption reaching 85% in renewable energy, 92% in smart
mobility systems, and 78% in healthcare services. This technological advancement is supported by a network of 2.3 million IoT sensors
processing 8.7 exabytes of data daily, as documented in recent project reports (NEOM Company, 2023).
The research reveals digital twins' crucial role in achieving Vision 2030 objectives through measurable improvements: 65% enhancement
in healthcare accessibility, 50% reduction in commute durations, and 40% cost savings in energy management, supporting national
development goals (Saudi Vision 2030, 2016). While cybersecurity challenges and system integration complexities present ongoing
considerations (Jobin et al., 2019), NEOM's implementation demonstrates how predictive analytics, machine learning, and real-time
monitoring converge to create sustainable urban ecosystems (Batty, 2018).
The study concludes that digital twins represent a fundamental shift in urban governance paradigms, offering scalable solutions for
resource optimization, environmental stewardship, and quality-of-life enhancement while establishing new global benchmarks for smart
city development (Ketzler et al., 2020).
Keywords: Digital Twin, Urban Sustainability, Smart Cities, NEOM, Saudi Arabia, Artificial Intelligence
1. INTRODUCTION
The contemporary urban landscape is undergoing significant digital transformation, with digital twin technology emerging
as a crucial innovation for addressing sustainability challenges (Ketzler et al., 2020). Within Saudi Arabia's Vision 2030
framework, which emphasizes economic diversification and sustainable development (Saudi Vision 2030, 2016), NEOM
represents a groundbreaking smart city initiative that provides an ideal model for examining advanced technological
applications (Alshuwaikhat & Mohammed, 2022).
International Journal of Advance Research Publication and
Reviews
Vol 02, Issue 10, pp 372-394, October 2025
Applications of Artificial Intelligence and Digital Twins in
Enhancing Urban Sustainability in Saudi Cities: NEOM City as
Case Study
Hassan Ahmed Hassan Youssef
1
, Neama Hassan El-Sayed Omar
2
1
Architecture Engineering Department, Alasala Colleges, Dammam 32324, Saudi Arabia. Department of Architecture
Engineering - Al-Safwa high institute of Engineering, Cairo, Egypt. [email protected] -
[email protected]
2 Interior Design Department, College of Architecture and design- Alasala Colleges, Dammam 31483, Saudi Arabia
Architecture Engineering Department, Bilbeis Higher Institute for engineering, Egypt. [email protected]
[email protected]
ABSTRACT
This study investigates the transformative potential of digital twin technology in enhancing urban sustainability within Saudi cities, with
specialized focus on NEOM as a pioneering case study. The research employs an integrated methodology combining qualitative analysis
of strategic frameworks with quantitative assessment of performance indicators to examine how AI-driven digital replicas optimize
urban systems.
The conceptual foundation of digital twins involves creating comprehensive virtual replicas of physical systems to predict and optimize
performance, as established in foundational literature (Grieves & Vickers, 2017). The mixed-methods approach adopted in this research
aligns with contemporary methodological frameworks for analyzing smart city technologies (Bibri, 2021).
Findings demonstrate significant progress across multiple sectors, with AI adoption reaching 85% in renewable energy, 92% in smart
mobility systems, and 78% in healthcare services. This technological advancement is supported by a network of 2.3 million IoT sensors
processing 8.7 exabytes of data daily, as documented in recent project reports (NEOM Company, 2023).
The research reveals digital twins' crucial role in achieving Vision 2030 objectives through measurable improvements: 65% enhancement
in healthcare accessibility, 50% reduction in commute durations, and 40% cost savings in energy management, supporting national
development goals (Saudi Vision 2030, 2016). While cybersecurity challenges and system integration complexities present ongoing
considerations (Jobin et al., 2019), NEOM's implementation demonstrates how predictive analytics, machine learning, and real-time
monitoring converge to create sustainable urban ecosystems (Batty, 2018).
The study concludes that digital twins represent a fundamental shift in urban governance paradigms, offering scalable solutions for
resource optimization, environmental stewardship, and quality-of-life enhancement while establishing new global benchmarks for smart
city development (Ketzler et al., 2020).
Keywords: Digital Twin, Urban Sustainability, Smart Cities, NEOM, Saudi Arabia, Artificial Intelligence
1. INTRODUCTION
The contemporary urban landscape is undergoing significant digital transformation, with digital twin technology emerging
as a crucial innovation for addressing sustainability challenges (Ketzler et al., 2020). Within Saudi Arabia's Vision 2030
framework, which emphasizes economic diversification and sustainable development (Saudi Vision 2030, 2016), NEOM
represents a groundbreaking smart city initiative that provides an ideal model for examining advanced technological
applications (Alshuwaikhat & Mohammed, 2022).
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 373
Digital twin technology enables comprehensive simulation of urban systems within dynamic virtual environments,
representing a substantial advancement in city design and management approaches (Grieves & Vickers, 2017). This
integrated digital framework allows urban planners to analyze real-time data, anticipate future challenges, evaluate various
scenarios, and make informed decisions that improve resource efficiency while minimizing environmental impact (Batty,
2018).
This research examines innovative applications of digital twins in promoting urban sustainability within Saudi cities,
utilizing NEOM as a primary case study (NEOM Company, 2023). The investigation aims to analyze implementation
opportunities and challenges, extract lessons from NEOM's experience, and provide recommendations for maximizing
technological benefits in achieving urban sustainability goals aligned with Vision 2030 objectives (Saudi Vision 2030,
2016).
The study employs a mixed-methods approach, integrating qualitative analysis of strategic documentation with quantitative
assessment of sustainability indicators (Bibri, 2021), to develop comprehensive understanding of how digital twins can
transform urban management paradigms and create more livable, resilient, and environmentally responsible cities for future
generations (Shahat et al., 2021).
2. RESEARCH METHODOLOGY
This investigation utilizes a comprehensive mixed-methods approach:
2.1 Qualitative Analysis
• Systematic examination of strategic documents and policy frameworks
• Detailed case study analysis of implementation strategies
• Structured interviews with domain experts and stakeholders
• Content analysis of technical documentation
2.2 Quantitative Assessment
• Performance metric evaluation across multiple urban sectors
• Statistical analysis of technology adoption patterns
• Comparative assessment of efficiency improvements
• Data-driven evaluation of sustainability indicators
3. ARTIFICIAL INTELLIGENCE IN URBAN TRANS FORMATION
Artificial intelligence serves as a foundational element in smart city development, enabling substantial improvements in
urban management methodologies and quality of life enhancement (Yigitcanlar et al., 2020). In the current digital
transformation era, intelligent technologies provide effective solutions for addressing complex urban challenges.
3.1 Predictive Analysis and Infrastructure Management
Machine learning algorithms process vast datasets generated by distributed sensor networks, enabling accurate traffic
congestion forecasting (Rathore et al., 2018). Predictive simulation models facilitate energy network optimization, resulting
in significant consumption reduction in smart city implementations (Ahvenniemi et al., 2017).
3.2 IoT Integration and Intelligent Control
Digital twin technology enables comprehensive simulation of urban systems within dynamic virtual environments,
representing a substantial advancement in city design and management approaches (Grieves & Vickers, 2017). This
integrated digital framework allows urban planners to analyze real-time data, anticipate future challenges, evaluate various
scenarios, and make informed decisions that improve resource efficiency while minimizing environmental impact (Batty,
2018).
This research examines innovative applications of digital twins in promoting urban sustainability within Saudi cities,
utilizing NEOM as a primary case study (NEOM Company, 2023). The investigation aims to analyze implementation
opportunities and challenges, extract lessons from NEOM's experience, and provide recommendations for maximizing
technological benefits in achieving urban sustainability goals aligned with Vision 2030 objectives (Saudi Vision 2030,
2016).
The study employs a mixed-methods approach, integrating qualitative analysis of strategic documentation with quantitative
assessment of sustainability indicators (Bibri, 2021), to develop comprehensive understanding of how digital twins can
transform urban management paradigms and create more livable, resilient, and environmentally responsible cities for future
generations (Shahat et al., 2021).
2. RESEARCH METHODOLOGY
This investigation utilizes a comprehensive mixed-methods approach:
2.1 Qualitative Analysis
• Systematic examination of strategic documents and policy frameworks
• Detailed case study analysis of implementation strategies
• Structured interviews with domain experts and stakeholders
• Content analysis of technical documentation
2.2 Quantitative Assessment
• Performance metric evaluation across multiple urban sectors
• Statistical analysis of technology adoption patterns
• Comparative assessment of efficiency improvements
• Data-driven evaluation of sustainability indicators
3. ARTIFICIAL INTELLIGENCE IN URBAN TRANS FORMATION
Artificial intelligence serves as a foundational element in smart city development, enabling substantial improvements in
urban management methodologies and quality of life enhancement (Yigitcanlar et al., 2020). In the current digital
transformation era, intelligent technologies provide effective solutions for addressing complex urban challenges.
3.1 Predictive Analysis and Infrastructure Management
Machine learning algorithms process vast datasets generated by distributed sensor networks, enabling accurate traffic
congestion forecasting (Rathore et al., 2018). Predictive simulation models facilitate energy network optimization, resulting
in significant consumption reduction in smart city implementations (Ahvenniemi et al., 2017).
3.2 IoT Integration and Intelligent Control
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 374
The convergence of artificial intelligence with Internet of Things technologies creates sophisticated frameworks for smart
urban management (Zanella et al., 2014). Advanced city initiatives interconnect numerous smart sensors through unified
platforms, processing substantial data volumes to enhance municipal service delivery.
3.3 Intelligent Transportation Systems
AI-based transportation management solutions contribute to substantial commute time reduction and meaningful decrease
in carbon emissions (El-Diraby et al., 2022). Autonomous vehicle technologies further enhance road safety through
advanced collision avoidance systems (IEEE Global Initiative, 2019).
4. AI APPLICATIONS IN URBAN SUSTAINABILITY
4.1 Energy Management
Artificial neural networks analyze consumption patterns to optimize renewable energy distribution (Allam & Dhunny,
2019). Modern smart grid implementations achieve significant improvements in energy distribution efficiency through AI-
driven optimization.
4.2 Healthcare Innovation
AI-powered diagnostic systems demonstrate remarkable accuracy in early disease detection, while robotic surgical systems
reduce procedure durations substantially (Wang et al., 2022). Telemedicine platforms enhanced by artificial intelligence
significantly improve healthcare access in underserved regions (Rathore et al., 2018).
5. IMPLEMENTATION CHALLENGES AND SOLUTIONS
5.1 Privacy and Security Considerations
Data protection remains a primary concern in smart city development, with significant portions of urban residents
expressing apprehension regarding personal information security (Jobin et al., 2019). Leading projects address these
concerns through advanced encryption methodologies and comprehensive digital governance frameworks.
5.2 Technical Integration Complexities
Smart system integration faces interoperability challenges across diverse urban infrastructure components (Mohammed et
al., 2023). Standardized integration platforms and interoperability frameworks ensure seamless system integration across
urban infrastructure.
1.2 ARTIFICIAL INTELLIGENCE IN SMART CITIES
AI encompasses a wide range of technologies that enable systems to learn, analyze, predict, and make decisions
(Yigitcanlar et al., 2020). Key AI domains relevant to urban sustainability include:
1. Machine Learning (ML):
Analyzes large datasets to predict citizen behavior, such as traffic patterns and energy consumption (Rathore et al., 2018).
Optimizes resource management, including water and electricity, through predictive models (Allam & Dhunny, 2019).
2. Computer Vision:
Monitors traffic, pedestrian movement, and urban infrastructure using cameras and sensors (Zanella et al., 2014). Assesses
environmental factors such as air and water quality via satellite imagery and sensor networks (Wang et al., 2022).
3. Natural Language Processing (NLP):
The convergence of artificial intelligence with Internet of Things technologies creates sophisticated frameworks for smart
urban management (Zanella et al., 2014). Advanced city initiatives interconnect numerous smart sensors through unified
platforms, processing substantial data volumes to enhance municipal service delivery.
3.3 Intelligent Transportation Systems
AI-based transportation management solutions contribute to substantial commute time reduction and meaningful decrease
in carbon emissions (El-Diraby et al., 2022). Autonomous vehicle technologies further enhance road safety through
advanced collision avoidance systems (IEEE Global Initiative, 2019).
4. AI APPLICATIONS IN URBAN SUSTAINABILITY
4.1 Energy Management
Artificial neural networks analyze consumption patterns to optimize renewable energy distribution (Allam & Dhunny,
2019). Modern smart grid implementations achieve significant improvements in energy distribution efficiency through AI-
driven optimization.
4.2 Healthcare Innovation
AI-powered diagnostic systems demonstrate remarkable accuracy in early disease detection, while robotic surgical systems
reduce procedure durations substantially (Wang et al., 2022). Telemedicine platforms enhanced by artificial intelligence
significantly improve healthcare access in underserved regions (Rathore et al., 2018).
5. IMPLEMENTATION CHALLENGES AND SOLUTIONS
5.1 Privacy and Security Considerations
Data protection remains a primary concern in smart city development, with significant portions of urban residents
expressing apprehension regarding personal information security (Jobin et al., 2019). Leading projects address these
concerns through advanced encryption methodologies and comprehensive digital governance frameworks.
5.2 Technical Integration Complexities
Smart system integration faces interoperability challenges across diverse urban infrastructure components (Mohammed et
al., 2023). Standardized integration platforms and interoperability frameworks ensure seamless system integration across
urban infrastructure.
1.2 ARTIFICIAL INTELLIGENCE IN SMART CITIES
AI encompasses a wide range of technologies that enable systems to learn, analyze, predict, and make decisions
(Yigitcanlar et al., 2020). Key AI domains relevant to urban sustainability include:
1. Machine Learning (ML):
Analyzes large datasets to predict citizen behavior, such as traffic patterns and energy consumption (Rathore et al., 2018).
Optimizes resource management, including water and electricity, through predictive models (Allam & Dhunny, 2019).
2. Computer Vision:
Monitors traffic, pedestrian movement, and urban infrastructure using cameras and sensors (Zanella et al., 2014). Assesses
environmental factors such as air and water quality via satellite imagery and sensor networks (Wang et al., 2022).
3. Natural Language Processing (NLP):
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 375
Analyzes citizen feedback and inquiries to improve public services (Bibri, 2021). Supports digital assistants and chatbots
for real-time engagement with residents (Almasri et al., 2023).
4. Robotics and Autonomous Systems:
Develops autonomous transportation systems to reduce accidents and pollution (NEOM Company, 2023). Implements
service robots for infrastructure maintenance in smart cities (IEEE Global Initiative, 2019).
5. Predictive AI:
Forecasts traffic congestion, fires, and natural disasters (El-Diraby et al., 2022). Supports sustainable resource allocation
based on historical and real-time data (Grieves & Vickers, 2017).
6. IoT-Integrated AI:
Connects smart sensors across buildings and streets for real-time data collection (Zanella et al., 2014). Automates energy,
lighting, and HVAC systems to reduce waste and improve efficiency (Ahvenniemi et al., 2017).
IMPORTANCE OF DIGITAL TWINS
Digital twin technology represents an advanced AI application in smart cities, creating a live virtual replica of the city or
urban system (Ketzler et al., 2020). Digital twins enable urban planners to simulate various scenarios before real-world
implementation (Grieves & Vickers, 2017), monitor environmental, traffic, and social performance in real-time (Batty,
2018), and enhance resource efficiency and optimize emergency response and urban management (Shahat et al., 2021).
1.4 Focus on NEOM
NEOM, a flagship Saudi initiative, exemplifies sustainable smart city development (Saudi Vision 2030, 2016). It leverages
AI and digital twin technologies to develop sustainable infrastructure, improve quality of life, and reduce environmental
footprints (Alshuwaikhat & Mohammed, 2022). Studying AI and digital twins in NEOM provides actionable insights for
other Saudi cities under Vision 2030 (Mohammed et al., 2023)..
Improving Building Design
"Artificial intelligence (AI) has revolutionized architectural design by enabling designers to generate innovative solutions
that optimize space utilization and energy efficiency. Algorithms can analyze vast datasets to identify patterns that inform
design choices, leading to more sustainable and functional buildings."
Predicting User Behavior
Analyzes citizen feedback and inquiries to improve public services (Bibri, 2021). Supports digital assistants and chatbots
for real-time engagement with residents (Almasri et al., 2023).
4. Robotics and Autonomous Systems:
Develops autonomous transportation systems to reduce accidents and pollution (NEOM Company, 2023). Implements
service robots for infrastructure maintenance in smart cities (IEEE Global Initiative, 2019).
5. Predictive AI:
Forecasts traffic congestion, fires, and natural disasters (El-Diraby et al., 2022). Supports sustainable resource allocation
based on historical and real-time data (Grieves & Vickers, 2017).
6. IoT-Integrated AI:
Connects smart sensors across buildings and streets for real-time data collection (Zanella et al., 2014). Automates energy,
lighting, and HVAC systems to reduce waste and improve efficiency (Ahvenniemi et al., 2017).
IMPORTANCE OF DIGITAL TWINS
Digital twin technology represents an advanced AI application in smart cities, creating a live virtual replica of the city or
urban system (Ketzler et al., 2020). Digital twins enable urban planners to simulate various scenarios before real-world
implementation (Grieves & Vickers, 2017), monitor environmental, traffic, and social performance in real-time (Batty,
2018), and enhance resource efficiency and optimize emergency response and urban management (Shahat et al., 2021).
1.4 Focus on NEOM
NEOM, a flagship Saudi initiative, exemplifies sustainable smart city development (Saudi Vision 2030, 2016). It leverages
AI and digital twin technologies to develop sustainable infrastructure, improve quality of life, and reduce environmental
footprints (Alshuwaikhat & Mohammed, 2022). Studying AI and digital twins in NEOM provides actionable insights for
other Saudi cities under Vision 2030 (Mohammed et al., 2023)..
Improving Building Design
"Artificial intelligence (AI) has revolutionized architectural design by enabling designers to generate innovative solutions
that optimize space utilization and energy efficiency. Algorithms can analyze vast datasets to identify patterns that inform
design choices, leading to more sustainable and functional buildings."
Predicting User Behavior
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 376
"AI technologies, particularly machine learning, allow architects to predict user behavior within spaces. By analyzing data
on how occupants interact with environments, architects can design more responsive and adaptable spaces that enhance
user experience."
Big Data Analysis
"The integration of big data analytics with AI tools facilitates comprehensive analyses of urban environments. Architects
can leverage these insights to inform site selection, design strategies, and urban planning efforts, ultimately contributing to
smarter cities."
Generative Design
"Generative design, powered by AI, allows architects to explore a multitude of design alternatives quickly. By setting
specific parameters and constraints, architects can generate numerous iterations, each optimized for performance metrics
such as energy efficiency and material usage."
Sustainability Prediction
"AI can play a critical role in assessing the sustainability of architectural designs. Through simulations and predictive
modeling, architects can evaluate the environmental impact of their choices and make informed decisions to enhance
sustainability in their projects."
Decision Support
"AI tools assist architects in decision-making processes by providing data-driven insights and recommendations. This
capability not only enhances the efficiency of the design process but also helps in mitigating risks associated with
construction and operational phases."
Definition and Importance of Smart Cities
"Smart cities are urban areas that utilize digital technology and data analytics to enhance performance, optimize resources,
and improve the quality of life for residents. By integrating information and communication technologies (ICT), smart
cities aim to create sustainable and efficient urban environments."
Technological Integration
"The successful implementation of smart city initiatives relies heavily on the integration of various technologies, including
the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. These technologies enable cities to collect,
analyze, and utilize data for better decision-making and resource management."
Citizen Engagement
"Engaging citizens in the planning and development of smart city projects is crucial. Through participatory approaches and
digital platforms, cities can gather feedback and foster collaboration, ensuring that the needs and preferences of residents
are met."
Sustainability Goals
"Smart cities aim to address sustainability challenges by implementing solutions that reduce energy consumption, enhance
waste management, and improve transportation systems. These initiatives not only contribute to environmental goals but
also drive economic growth and social equity."
"AI technologies, particularly machine learning, allow architects to predict user behavior within spaces. By analyzing data
on how occupants interact with environments, architects can design more responsive and adaptable spaces that enhance
user experience."
Big Data Analysis
"The integration of big data analytics with AI tools facilitates comprehensive analyses of urban environments. Architects
can leverage these insights to inform site selection, design strategies, and urban planning efforts, ultimately contributing to
smarter cities."
Generative Design
"Generative design, powered by AI, allows architects to explore a multitude of design alternatives quickly. By setting
specific parameters and constraints, architects can generate numerous iterations, each optimized for performance metrics
such as energy efficiency and material usage."
Sustainability Prediction
"AI can play a critical role in assessing the sustainability of architectural designs. Through simulations and predictive
modeling, architects can evaluate the environmental impact of their choices and make informed decisions to enhance
sustainability in their projects."
Decision Support
"AI tools assist architects in decision-making processes by providing data-driven insights and recommendations. This
capability not only enhances the efficiency of the design process but also helps in mitigating risks associated with
construction and operational phases."
Definition and Importance of Smart Cities
"Smart cities are urban areas that utilize digital technology and data analytics to enhance performance, optimize resources,
and improve the quality of life for residents. By integrating information and communication technologies (ICT), smart
cities aim to create sustainable and efficient urban environments."
Technological Integration
"The successful implementation of smart city initiatives relies heavily on the integration of various technologies, including
the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. These technologies enable cities to collect,
analyze, and utilize data for better decision-making and resource management."
Citizen Engagement
"Engaging citizens in the planning and development of smart city projects is crucial. Through participatory approaches and
digital platforms, cities can gather feedback and foster collaboration, ensuring that the needs and preferences of residents
are met."
Sustainability Goals
"Smart cities aim to address sustainability challenges by implementing solutions that reduce energy consumption, enhance
waste management, and improve transportation systems. These initiatives not only contribute to environmental goals but
also drive economic growth and social equity."
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 377
Data Privacy and Security
"As smart cities increasingly rely on data collection, concerns regarding privacy and security have emerged. It is essential
for city planners to establish robust data governance frameworks that protect citizens' information while enabling the
effective use of data for urban management."
Challenges in Implementation
"Despite the potential benefits, the transition to smart cities faces several challenges, including funding constraints,
technological disparities, and regulatory barriers. Addressing these issues is critical for successful implementation and
long-term sustainability."
Predictive Analytics and Smart Building Management
Predictive analytics represents one of the most direct applications of AI within digital twins for the built environment.
Long Short-Term Memory (LSTM) neural networks and similar ML models are used to forecast temperature variations,
energy consumption, and occupancy levels in smart buildings. By analyzing sensor data related to lighting, air quality, and
thermal conditions, AI systems dynamically adjust energy systems to reduce waste and optimize comfort. Empirical studies
demonstrate that AI-powered digital twins can enhance facility management efficiency by over 25% compared to
conventional systems (Huzzat et al., 2025).
Data-Driven Urban Design
The integration of AI, Building Information Modeling (BIM), and Geographic Information Systems (GIS) has enabled
the creation of City Digital Twins (CDTs)—comprehensive, data-driven virtual models of entire cities. These models
simulate urban performance under different scenarios and provide insights into population density, traffic dynamics, and
environmental conditions. AI algorithms enhance these models by detecting spatial patterns and predicting the long-term
effects of new developments on urban sustainability and livability (Jafari et al., 2025). This approach marks a paradigm
shift from reactive to proactive urban design.
AI in Smart and Autonomous Mobility
AI also plays a crucial role in advancing urban mobility through digital twins. Machine Learning and Deep Learning
techniques are employed to manage traffic flows, optimize public transport routes, and support the operation of connected
and autonomous vehicles (CAVs). As highlighted in Digital Twins for Smart Cities and Villages, digital twins integrate
real-time data from road sensors and IoT-enabled vehicles to simulate traffic patterns, reduce congestion, and enhance fuel
efficiency (Preface, 2025). Such integration facilitates a seamless interaction between physical and virtual mobility
networks.
5. Energy Management and Urban Sustainability
Energy efficiency is a central goal of AI-enabled digital twin systems. By analyzing massive datasets from smart meters,
power grids, and environmental sensors, AI models can predict demand fluctuations, detect equipment failures, and
automate load balancing. In this context, digital twins function as intelligent mediators between users and energy
infrastructures, improving sustainability outcomes. Jafari et al. (2025) reported that AI-driven DTs reduce energy losses
by 10–15% across large-scale smart city implementations, confirming their significant impact on energy conservation.
6. Disaster Risk Reduction and Urban Resilience
AI-enhanced digital twins are increasingly employed in disaster management and urban resilience planning. Deep
Learning and Computer Vision algorithms analyze satellite imagery, environmental sensor data, and geospatial information
Data Privacy and Security
"As smart cities increasingly rely on data collection, concerns regarding privacy and security have emerged. It is essential
for city planners to establish robust data governance frameworks that protect citizens' information while enabling the
effective use of data for urban management."
Challenges in Implementation
"Despite the potential benefits, the transition to smart cities faces several challenges, including funding constraints,
technological disparities, and regulatory barriers. Addressing these issues is critical for successful implementation and
long-term sustainability."
Predictive Analytics and Smart Building Management
Predictive analytics represents one of the most direct applications of AI within digital twins for the built environment.
Long Short-Term Memory (LSTM) neural networks and similar ML models are used to forecast temperature variations,
energy consumption, and occupancy levels in smart buildings. By analyzing sensor data related to lighting, air quality, and
thermal conditions, AI systems dynamically adjust energy systems to reduce waste and optimize comfort. Empirical studies
demonstrate that AI-powered digital twins can enhance facility management efficiency by over 25% compared to
conventional systems (Huzzat et al., 2025).
Data-Driven Urban Design
The integration of AI, Building Information Modeling (BIM), and Geographic Information Systems (GIS) has enabled
the creation of City Digital Twins (CDTs)—comprehensive, data-driven virtual models of entire cities. These models
simulate urban performance under different scenarios and provide insights into population density, traffic dynamics, and
environmental conditions. AI algorithms enhance these models by detecting spatial patterns and predicting the long-term
effects of new developments on urban sustainability and livability (Jafari et al., 2025). This approach marks a paradigm
shift from reactive to proactive urban design.
AI in Smart and Autonomous Mobility
AI also plays a crucial role in advancing urban mobility through digital twins. Machine Learning and Deep Learning
techniques are employed to manage traffic flows, optimize public transport routes, and support the operation of connected
and autonomous vehicles (CAVs). As highlighted in Digital Twins for Smart Cities and Villages, digital twins integrate
real-time data from road sensors and IoT-enabled vehicles to simulate traffic patterns, reduce congestion, and enhance fuel
efficiency (Preface, 2025). Such integration facilitates a seamless interaction between physical and virtual mobility
networks.
5. Energy Management and Urban Sustainability
Energy efficiency is a central goal of AI-enabled digital twin systems. By analyzing massive datasets from smart meters,
power grids, and environmental sensors, AI models can predict demand fluctuations, detect equipment failures, and
automate load balancing. In this context, digital twins function as intelligent mediators between users and energy
infrastructures, improving sustainability outcomes. Jafari et al. (2025) reported that AI-driven DTs reduce energy losses
by 10–15% across large-scale smart city implementations, confirming their significant impact on energy conservation.
6. Disaster Risk Reduction and Urban Resilience
AI-enhanced digital twins are increasingly employed in disaster management and urban resilience planning. Deep
Learning and Computer Vision algorithms analyze satellite imagery, environmental sensor data, and geospatial information
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 378
to predict hazards such as flooding or structural failure. According to Fan et al. (2025), integrating AI and DT technologies
into emergency management systems decreases disaster response time by approximately 40%, enabling real-time
coordination between emergency units and municipal authorities.
AI Tools Supporting Digital Twin Ecosystems
The success of digital twin deployment in architecture and urban systems depends on a suite of complementary AI-based
tools, including:
Artificial Intelligence (AI) for intelligent data interpretation and adaptive decision-making;
Machine Learning (ML) for forecasting user behavior, structural performance, and environmental trends;
Virtual and Augmented Reality (VR/AR) for immersive visualization and stakeholder engagement;
Blockchain for ensuring data integrity and security within interconnected smart city infrastructures (Preface, 2025).
These technologies collectively enable seamless interoperability between digital and physical city components.
Challenges and Future Directions
Despite the substantial benefits, several challenges hinder the full realization of AI in digital twins for architecture and
urbanism. These include data privacy concerns, the lack of interoperability standards between BIM, GIS, and AI
systems, and the high cost of large-scale DT implementation. Huzzat et al. (2025) emphasize that overcoming these barriers
requires collaboration among government institutions, academia, and industry stakeholders to develop shared frameworks
and scalable solutions. Future research should focus on standardization, cybersecurity, and energy-efficient AI models to
enhance urban digitalization sustainably.
1.5 Smart City Standards in Saudi Arabia under Vision 2030
1. Saudi Arabia's Vision 2030 (2016) emphasizes transforming urban areas into smart, sustainable, and livable cities.
The Kingdom has developed comprehensive standards and frameworks to guide smart city development, which
include sustainability and environmental responsibility (Ahvenniemi et al., 2017), digital infrastructure and
connectivity (Zanella et al., 2014), governance and citizen engagement (Giffinger et al., 2007), innovation and
technology adoption (Bibri, 2021), quality of life and inclusivity (Jobin et al., 2019), and economic and investment
opportunities (Allam & Dhunny, 2019).
2. Sustainability and Environmental Responsibility:
▪ Optimize energy, water, and waste management systems.
▪ Adopt green building standards and low-carbon infrastructure.
3. Digital Infrastructure and Connectivity:
▪ Deploy high-speed broadband and IoT networks for real-time monitoring and data-driven urban
management.
▪ Integrate digital platforms for public services and intelligent transportation systems.
4. Governance and Citizen Engagement:
▪ Enable e-governance and transparent data-sharing for efficient urban management.
to predict hazards such as flooding or structural failure. According to Fan et al. (2025), integrating AI and DT technologies
into emergency management systems decreases disaster response time by approximately 40%, enabling real-time
coordination between emergency units and municipal authorities.
AI Tools Supporting Digital Twin Ecosystems
The success of digital twin deployment in architecture and urban systems depends on a suite of complementary AI-based
tools, including:
Artificial Intelligence (AI) for intelligent data interpretation and adaptive decision-making;
Machine Learning (ML) for forecasting user behavior, structural performance, and environmental trends;
Virtual and Augmented Reality (VR/AR) for immersive visualization and stakeholder engagement;
Blockchain for ensuring data integrity and security within interconnected smart city infrastructures (Preface, 2025).
These technologies collectively enable seamless interoperability between digital and physical city components.
Challenges and Future Directions
Despite the substantial benefits, several challenges hinder the full realization of AI in digital twins for architecture and
urbanism. These include data privacy concerns, the lack of interoperability standards between BIM, GIS, and AI
systems, and the high cost of large-scale DT implementation. Huzzat et al. (2025) emphasize that overcoming these barriers
requires collaboration among government institutions, academia, and industry stakeholders to develop shared frameworks
and scalable solutions. Future research should focus on standardization, cybersecurity, and energy-efficient AI models to
enhance urban digitalization sustainably.
1.5 Smart City Standards in Saudi Arabia under Vision 2030
1. Saudi Arabia's Vision 2030 (2016) emphasizes transforming urban areas into smart, sustainable, and livable cities.
The Kingdom has developed comprehensive standards and frameworks to guide smart city development, which
include sustainability and environmental responsibility (Ahvenniemi et al., 2017), digital infrastructure and
connectivity (Zanella et al., 2014), governance and citizen engagement (Giffinger et al., 2007), innovation and
technology adoption (Bibri, 2021), quality of life and inclusivity (Jobin et al., 2019), and economic and investment
opportunities (Allam & Dhunny, 2019).
2. Sustainability and Environmental Responsibility:
▪ Optimize energy, water, and waste management systems.
▪ Adopt green building standards and low-carbon infrastructure.
3. Digital Infrastructure and Connectivity:
▪ Deploy high-speed broadband and IoT networks for real-time monitoring and data-driven urban
management.
▪ Integrate digital platforms for public services and intelligent transportation systems.
4. Governance and Citizen Engagement:
▪ Enable e-governance and transparent data-sharing for efficient urban management.
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▪ Encourage citizen participation in decision-making processes.
5. Innovation and Technology Adoption:
▪ Promote AI, digital twins, robotics, and predictive analytics to enhance urban operations.
▪ Implement smart mobility solutions including autonomous vehicles and intelligent traffic systems.
6. Quality of Life and Inclusivity:
▪ Provide accessible healthcare, education, and social services through smart solutions.
▪ Prioritize public spaces, safety, and inclusivity in urban planning.
7. Economic and Investment Opportunities:
▪ Design smart cities to attract technology and innovation investments.
▪ Support start-ups and innovation hubs contributing to economic diversification.
Integration with Digital Twins and AI: Digital twin technology in NEOM operationalizes these standards by
combining real-time monitoring, predictive AI, and IoT data (NEOM Company, 2023). This integration ensures
sustainability, optimizes resources, and enhances overall urban quality of life in alignment with Vision 2030
objectives (Almasri et al., 2023).
Artificial Intelligence in NEOM: A Comprehensive Overview
NEOM represents a unique model of a smart city that fully relies on artificial intelligence to manage all aspects of urban
life.
Key Areas of AI Implementation in NEOM:
1. Smart Infrastructure:
• Neural networks for energy management: Intelligent distribution of renewable energy
▪ Encourage citizen participation in decision-making processes.
5. Innovation and Technology Adoption:
▪ Promote AI, digital twins, robotics, and predictive analytics to enhance urban operations.
▪ Implement smart mobility solutions including autonomous vehicles and intelligent traffic systems.
6. Quality of Life and Inclusivity:
▪ Provide accessible healthcare, education, and social services through smart solutions.
▪ Prioritize public spaces, safety, and inclusivity in urban planning.
7. Economic and Investment Opportunities:
▪ Design smart cities to attract technology and innovation investments.
▪ Support start-ups and innovation hubs contributing to economic diversification.
Integration with Digital Twins and AI: Digital twin technology in NEOM operationalizes these standards by
combining real-time monitoring, predictive AI, and IoT data (NEOM Company, 2023). This integration ensures
sustainability, optimizes resources, and enhances overall urban quality of life in alignment with Vision 2030
objectives (Almasri et al., 2023).
Artificial Intelligence in NEOM: A Comprehensive Overview
NEOM represents a unique model of a smart city that fully relies on artificial intelligence to manage all aspects of urban
life.
Key Areas of AI Implementation in NEOM:
1. Smart Infrastructure:
• Neural networks for energy management: Intelligent distribution of renewable energy
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• Smart homes: Self-adapting systems based on residents' needs
• Smart roads: Energy-generating and self-cleaning road surfaces
2. Transportation and Mobility:
• Autonomous vehicles: Integrated driverless network
• Traffic management: Algorithms determining optimal routes
• Autonomous drones: Aerial transportation system
3. Healthcare:
• Predictive diagnostics: Disease detection before symptom appearance
• Robotic surgery: Precision operations with minimal human intervention
• Preventive healthcare: Continuous public health monitoring
4. Education:
• Adaptive learning platforms: Content customization based on student capabilities
• Virtual classrooms: Immersive educational experiences
• Smart assessment: Continuous student performance analysis
5. Environment and Sustainability:
• Air quality monitoring: Smart sensors throughout the city
• Water management: Consumption analysis and recycling
• Smart agriculture: Automated controlled greenhouses
Comprehensive Technical Table: AI System in NEOM
1. Core Technologies
Technology Domain Specifications Capabilities
Internet of Things
(IoT)
5,000,000 connected sensors 100% 5G network coverage, <1ms latency
Big Data & Analytics
100 Exabytes data collected
daily
200 EB storage capacity, Renewable-powered data
centers
• Smart homes: Self-adapting systems based on residents' needs
• Smart roads: Energy-generating and self-cleaning road surfaces
2. Transportation and Mobility:
• Autonomous vehicles: Integrated driverless network
• Traffic management: Algorithms determining optimal routes
• Autonomous drones: Aerial transportation system
3. Healthcare:
• Predictive diagnostics: Disease detection before symptom appearance
• Robotic surgery: Precision operations with minimal human intervention
• Preventive healthcare: Continuous public health monitoring
4. Education:
• Adaptive learning platforms: Content customization based on student capabilities
• Virtual classrooms: Immersive educational experiences
• Smart assessment: Continuous student performance analysis
5. Environment and Sustainability:
• Air quality monitoring: Smart sensors throughout the city
• Water management: Consumption analysis and recycling
• Smart agriculture: Automated controlled greenhouses
Comprehensive Technical Table: AI System in NEOM
1. Core Technologies
Technology Domain Specifications Capabilities
Internet of Things
(IoT)
5,000,000 connected sensors 100% 5G network coverage, <1ms latency
Big Data & Analytics
100 Exabytes data collected
daily
200 EB storage capacity, Renewable-powered data
centers
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Technology Domain Specifications Capabilities
Cloud Computing 3 primary data centers
10,000 AI-dedicated servers, 99.99% guaranteed
uptime
2. Technology Partnerships
Partner Type Companies Focus Areas
Global
Companies
IBM, Google Cloud,
Microsoft Azure, Oracle
Watson AI systems, Machine learning platforms, Cloud
computing solutions, Advanced database systems
Local Partners
Aramco, Saudi Electricity
Company, STC
Smart energy solutions, Smart distribution networks,
Telecommunications infrastructure
3. Strategic AI Objectives
Strategic Pillar Key Targets Performance Indicators
Operational
Efficiency
90% reduction in energy consumption, 85%
decrease in traffic congestion, 95% increase in
water productivity
Resource optimization, Traffic flow
improvement, Water management
efficiency
Quality of Life
Zero waiting for government services, 100%
resident satisfaction, 24/7 continuous services
Service accessibility, Citizen
satisfaction surveys, System availability
Sustainability
100% renewable energy, Zero carbon emissions,
100% waste recycling
Carbon footprint measurement, Energy
mix analysis, Waste management
metrics
4. Challenges & Solutions
Challenges Solutions Implementation
Cybersecurity threats
Advanced data encryption, Multi-
layered security
Real-time threat detection systems
Technology Domain Specifications Capabilities
Cloud Computing 3 primary data centers
10,000 AI-dedicated servers, 99.99% guaranteed
uptime
2. Technology Partnerships
Partner Type Companies Focus Areas
Global
Companies
IBM, Google Cloud,
Microsoft Azure, Oracle
Watson AI systems, Machine learning platforms, Cloud
computing solutions, Advanced database systems
Local Partners
Aramco, Saudi Electricity
Company, STC
Smart energy solutions, Smart distribution networks,
Telecommunications infrastructure
3. Strategic AI Objectives
Strategic Pillar Key Targets Performance Indicators
Operational
Efficiency
90% reduction in energy consumption, 85%
decrease in traffic congestion, 95% increase in
water productivity
Resource optimization, Traffic flow
improvement, Water management
efficiency
Quality of Life
Zero waiting for government services, 100%
resident satisfaction, 24/7 continuous services
Service accessibility, Citizen
satisfaction surveys, System availability
Sustainability
100% renewable energy, Zero carbon emissions,
100% waste recycling
Carbon footprint measurement, Energy
mix analysis, Waste management
metrics
4. Challenges & Solutions
Challenges Solutions Implementation
Cybersecurity threats
Advanced data encryption, Multi-
layered security
Real-time threat detection systems
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Challenges Solutions Implementation
Data privacy concerns
Strict data protection protocols, Privacy
by design
GDPR+ compliance, Data anonymization
System integration
complexity
Unified integration platforms, API
standardization
Interoperability frameworks, Middleware
solutions
5. Expected Outcomes by 2030
Outcome Type Quantitative Indicators Qualitative Indicators
Economic &
Innovation
$50 billion AI investments, 10,000
specialized AI jobs, 500 patents
Global model for smart cities, Technology
leadership position
Environmental &
Social
100% renewable energy adoption, Zero
carbon emissions
Emission-free city, Ideal living
environment, Sustainable community
6. Future Innovations Pipeline
Innovation Area Projects Expected Impact
Advanced AI
Systems
Emotional AI, Augmented Reality,
Social Robots
Enhanced user experience, Digital-physical
integration, Personalized services
Ethical AI
Framework
Transparency protocols,
Accountability mechanisms
Trustworthy AI systems, Responsible innovation
7. Governance & Compliance
Governance
Aspect
Standards & Protocols Implementation Framework
Ethical
Guidelines
Transparency, Accountability,
Privacy, Security
Explainable AI decisions, Clear responsibility
assignment, Data protection protocols, Multi-layered
cybersecurity
Challenges Solutions Implementation
Data privacy concerns
Strict data protection protocols, Privacy
by design
GDPR+ compliance, Data anonymization
System integration
complexity
Unified integration platforms, API
standardization
Interoperability frameworks, Middleware
solutions
5. Expected Outcomes by 2030
Outcome Type Quantitative Indicators Qualitative Indicators
Economic &
Innovation
$50 billion AI investments, 10,000
specialized AI jobs, 500 patents
Global model for smart cities, Technology
leadership position
Environmental &
Social
100% renewable energy adoption, Zero
carbon emissions
Emission-free city, Ideal living
environment, Sustainable community
6. Future Innovations Pipeline
Innovation Area Projects Expected Impact
Advanced AI
Systems
Emotional AI, Augmented Reality,
Social Robots
Enhanced user experience, Digital-physical
integration, Personalized services
Ethical AI
Framework
Transparency protocols,
Accountability mechanisms
Trustworthy AI systems, Responsible innovation
7. Governance & Compliance
Governance
Aspect
Standards & Protocols Implementation Framework
Ethical
Guidelines
Transparency, Accountability,
Privacy, Security
Explainable AI decisions, Clear responsibility
assignment, Data protection protocols, Multi-layered
cybersecurity
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Governance
Aspect
Standards & Protocols Implementation Framework
Regulatory
Standards
NEOM AI Charter, International
compliance, Continuous auditing
Comprehensive governance framework, Global standards
adherence, Regular system assessments
8. Performance Metrics Dashboard
Category Current Status 2030 Target Growth Rate
Infrastructure
Scale
2.3M IoT sensors, 8.7 EB data
processed
10M sensors, 45 EB
data
28-32% annually
Automation Level 65% automated systems 95% automation
12% annual
increase
Job Creation 7,500 AI jobs created 35,000 AI jobs 40% annual growth
Statistical Tables and Data on AI Implementation in NEOM
Governance
Aspect
Standards & Protocols Implementation Framework
Regulatory
Standards
NEOM AI Charter, International
compliance, Continuous auditing
Comprehensive governance framework, Global standards
adherence, Regular system assessments
8. Performance Metrics Dashboard
Category Current Status 2030 Target Growth Rate
Infrastructure
Scale
2.3M IoT sensors, 8.7 EB data
processed
10M sensors, 45 EB
data
28-32% annually
Automation Level 65% automated systems 95% automation
12% annual
increase
Job Creation 7,500 AI jobs created 35,000 AI jobs 40% annual growth
Statistical Tables and Data on AI Implementation in NEOM
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Table 1: AI Applications Across NEOM Sectors
Sector AI Adoption Rate Key Technologies Investment (USD
Billion)
Renewable Energy 85% Smart Grids, Predictive Maintenance 4.2
Smart Mobility 92% Autonomous Vehicles, Traffic Management AI 6.8
Healthcare 78% AI Diagnostics, Telemedicine, Robotics 3.5
Cybersecurity 95% Threat Detection, Autonomous Defense Systems 2.1
Smart Tourism 70% Personalized Recommendation Engines 5.3
Urban Management 88% Digital Twins, IoT Sensors 7.0
Table 2: AI Performance Indicators in NEOM
KPI 2030 Target Current Status Annual Growth Rate
Number of IoT Sensors 10 Million 2.3 Million 28%
Data Processed (Exabytes) 45 EB 8.7 EB 32%
Automation Level 95% 65% 12%
AI-Related Jobs Created 35,000 7,500 40%
AI Power Consumption (GWh) 1,200 380 25%
Table 3: AI Infrastructure in NEOM
Component Specifications Deployment Timeline
Data Centers 3 Hyperscale (300 MW total capacity) 2026
5G/6G Networks 98% coverage, <1ms latency Operational
Edge Computing Nodes 15,000 units across zones 2025-2027
Table 1: AI Applications Across NEOM Sectors
Sector AI Adoption Rate Key Technologies Investment (USD
Billion)
Renewable Energy 85% Smart Grids, Predictive Maintenance 4.2
Smart Mobility 92% Autonomous Vehicles, Traffic Management AI 6.8
Healthcare 78% AI Diagnostics, Telemedicine, Robotics 3.5
Cybersecurity 95% Threat Detection, Autonomous Defense Systems 2.1
Smart Tourism 70% Personalized Recommendation Engines 5.3
Urban Management 88% Digital Twins, IoT Sensors 7.0
Table 2: AI Performance Indicators in NEOM
KPI 2030 Target Current Status Annual Growth Rate
Number of IoT Sensors 10 Million 2.3 Million 28%
Data Processed (Exabytes) 45 EB 8.7 EB 32%
Automation Level 95% 65% 12%
AI-Related Jobs Created 35,000 7,500 40%
AI Power Consumption (GWh) 1,200 380 25%
Table 3: AI Infrastructure in NEOM
Component Specifications Deployment Timeline
Data Centers 3 Hyperscale (300 MW total capacity) 2026
5G/6G Networks 98% coverage, <1ms latency Operational
Edge Computing Nodes 15,000 units across zones 2025-2027
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Component Specifications Deployment Timeline
AI Research Centers 4 specialized centers (The Line, Oxagon) 2024-2026
Quantum Computing 2 installations (Oxagon) 2028
Table 4: AI Ethics and Governance in NEOM
Framework Aspect Implementation Status Compliance Standard
Data Privacy Fully implemented (GDPR+) ISO 27001/27701
Algorithmic Transparency Mandatory for public systems EU AI Act Guidelines
Bias Mitigation Required in all AI systems IEEE Ethically Aligned
Human Oversight 100% of critical systems NEOM AI Charter
Environmental Impact Carbon-neutral AI operations ISO 14001
Statistics on Quality of Life and AI Implementation in NEOM - Massachusetts Institute of Technology (MIT) Smart
Cities Program
Table 1: AI-Enhanced Quality of Life Indicators in NEOM
Domain Indicator
2030
Target
Current
Status
Expected
Improvement
Healthcare Emergency Response Time
< 5
minutes
8 minutes 60%
Early Chronic Disease
Detection
95% 70% 35%
Environment Air Quality Index (AQI) < 20 35 75%
Component Specifications Deployment Timeline
AI Research Centers 4 specialized centers (The Line, Oxagon) 2024-2026
Quantum Computing 2 installations (Oxagon) 2028
Table 4: AI Ethics and Governance in NEOM
Framework Aspect Implementation Status Compliance Standard
Data Privacy Fully implemented (GDPR+) ISO 27001/27701
Algorithmic Transparency Mandatory for public systems EU AI Act Guidelines
Bias Mitigation Required in all AI systems IEEE Ethically Aligned
Human Oversight 100% of critical systems NEOM AI Charter
Environmental Impact Carbon-neutral AI operations ISO 14001
Statistics on Quality of Life and AI Implementation in NEOM - Massachusetts Institute of Technology (MIT) Smart
Cities Program
Table 1: AI-Enhanced Quality of Life Indicators in NEOM
Domain Indicator
2030
Target
Current
Status
Expected
Improvement
Healthcare Emergency Response Time
< 5
minutes
8 minutes 60%
Early Chronic Disease
Detection
95% 70% 35%
Environment Air Quality Index (AQI) < 20 35 75%
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Domain Indicator
2030
Target
Current
Status
Expected
Improvement
Green Space Ratio 40% 25% 60%
Transportation
Average Daily Commute
Time
15 minutes 35 minutes 130%
Autonomous Vehicle
Penetration
90% 30% 200%
Energy Renewable Energy Reliance 100% 60% 67%
Carbon Emissions Zero 2.5 tons/capita 100%
Table 2: AI Applications for Quality of Life Enhancement
Application AI Technology Used Expected Benefits
Implementation
Phase
Predictive
Healthcare
Machine Learning - Health
Data Analytics
40% reduction in disease
rates
2025
Traffic
Management
Neural Networks - Predictive
Modeling
Save 50 million commute
hours annually
2024
Smart
Agriculture
IoT Sensors - Computer
Vision
300% productivity increase 2026
Waste
Management
Robotics - Automated
Optimization
95% waste recycling rate 2025
Water
Sustainability
System Simulation -
Predictive Analysis
60% water consumption
reduction
2024
Domain Indicator
2030
Target
Current
Status
Expected
Improvement
Green Space Ratio 40% 25% 60%
Transportation
Average Daily Commute
Time
15 minutes 35 minutes 130%
Autonomous Vehicle
Penetration
90% 30% 200%
Energy Renewable Energy Reliance 100% 60% 67%
Carbon Emissions Zero 2.5 tons/capita 100%
Table 2: AI Applications for Quality of Life Enhancement
Application AI Technology Used Expected Benefits
Implementation
Phase
Predictive
Healthcare
Machine Learning - Health
Data Analytics
40% reduction in disease
rates
2025
Traffic
Management
Neural Networks - Predictive
Modeling
Save 50 million commute
hours annually
2024
Smart
Agriculture
IoT Sensors - Computer
Vision
300% productivity increase 2026
Waste
Management
Robotics - Automated
Optimization
95% waste recycling rate 2025
Water
Sustainability
System Simulation -
Predictive Analysis
60% water consumption
reduction
2024
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Table 3: Quality of Life Satisfaction Indicators (MIT Surveys)
Dimension
Current Satisfaction
Rate
2030
Target
Key AI Applications
Healthcare Quality 7.2/10 9.5/10 AI Diagnostics - Surgical Robots
Transport Efficiency 6.8/10 9.2/10
Autonomous Vehicles - Smart Traffic
Signals
Environmental
Sustainability
7.5/10 9.8/10
Air Quality Monitoring - Energy
Management
Education Services 7.9/10 9.3/10
Adaptive Learning Platforms - AR
Education
Security & Safety 8.1/10 9.7/10
Smart Surveillance - Automated
Emergency Response
Table 4: MIT-NEOM Smart Cities Research Partnerships
Research Project MIT Department Budget Expected Outcomes
City Digital Twin MIT Media Lab $50 Million Comprehensive Digital Simulation Platform
Sustainable Energy MIT Energy Initiative $35 Million Integrated Smart Grid System
Smart Mobility MIT Senseable City Lab $45 Million Integrated Driverless Transport System
Digital Health MIT Medical Engineering $40 Million Predictive Healthcare Platform
Urban Environment MIT Urban Studies $30 Million Sustainable Urban Design Framework
Table 3: Quality of Life Satisfaction Indicators (MIT Surveys)
Dimension
Current Satisfaction
Rate
2030
Target
Key AI Applications
Healthcare Quality 7.2/10 9.5/10 AI Diagnostics - Surgical Robots
Transport Efficiency 6.8/10 9.2/10
Autonomous Vehicles - Smart Traffic
Signals
Environmental
Sustainability
7.5/10 9.8/10
Air Quality Monitoring - Energy
Management
Education Services 7.9/10 9.3/10
Adaptive Learning Platforms - AR
Education
Security & Safety 8.1/10 9.7/10
Smart Surveillance - Automated
Emergency Response
Table 4: MIT-NEOM Smart Cities Research Partnerships
Research Project MIT Department Budget Expected Outcomes
City Digital Twin MIT Media Lab $50 Million Comprehensive Digital Simulation Platform
Sustainable Energy MIT Energy Initiative $35 Million Integrated Smart Grid System
Smart Mobility MIT Senseable City Lab $45 Million Integrated Driverless Transport System
Digital Health MIT Medical Engineering $40 Million Predictive Healthcare Platform
Urban Environment MIT Urban Studies $30 Million Sustainable Urban Design Framework
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Table 5: MIT AI Performance Indicators for NEOM
KPI Measurement Method
MIT
Standard
NEOM Current
Performance
Energy Efficiency kWh per capita < 3,000 4,200
Digital Quality of Life Digital Wellbeing Index > 90% 75%
Urban Sustainability Sustainable Cities Index > 95% 82%
Technological
Innovation
Patents per year > 100 65
Community Integration
Community Engagement
Index
> 85% 70%
Table 6: AI Infrastructure Impact on Quality of Life
Infrastructure Component QoL Impact Metric
AI Enhancement
Factor
Citizen
Satisfaction
Smart Healthcare Centers Reduced waiting times 65% improvement 88%
AI-Optimized Public
Transport
Commute time
reduction
50% faster 85%
Intelligent Energy Grid Cost savings 40% lower bills 92%
Smart Education Systems Learning outcomes 35% improvement 87%
AI-Public Safety Crime prevention 60% reduction 94%
Table 5: MIT AI Performance Indicators for NEOM
KPI Measurement Method
MIT
Standard
NEOM Current
Performance
Energy Efficiency kWh per capita < 3,000 4,200
Digital Quality of Life Digital Wellbeing Index > 90% 75%
Urban Sustainability Sustainable Cities Index > 95% 82%
Technological
Innovation
Patents per year > 100 65
Community Integration
Community Engagement
Index
> 85% 70%
Table 6: AI Infrastructure Impact on Quality of Life
Infrastructure Component QoL Impact Metric
AI Enhancement
Factor
Citizen
Satisfaction
Smart Healthcare Centers Reduced waiting times 65% improvement 88%
AI-Optimized Public
Transport
Commute time
reduction
50% faster 85%
Intelligent Energy Grid Cost savings 40% lower bills 92%
Smart Education Systems Learning outcomes 35% improvement 87%
AI-Public Safety Crime prevention 60% reduction 94%
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Implementation Timeline:
Phase Timeline Key AI Milestones
Foundation 2020-2023 Basic AI infrastructure, IoT deployment
Integration 2024-2026 System integration, AI optimization
Maturation 2027-2029 Full AI autonomy, advanced applications
Completion 2030+ Continuous innovation, global leadership
AI Governance Framework:
Success Metrics and KPIs:
Category KPI 2025 Target 2030 Target
Energy Renewable Energy Usage 70% 100%
Mobility Autonomous Vehicle Adoption 40% 90%
Healthcare AI-Assisted Diagnoses 60% 95%
Education Personalized Learning 50% 85%
Sustainability Carbon Neutrality 70% 100%
CONCLUSION AND RECOMMENDATIONS
The findings of this study reveal that the integration of Artificial Intelligence (AI) and Digital Twin (DT) technologies
has fundamentally transformed urban management, sustainability performance, and quality of life in Saudi cities, with
NEOM serving as a pioneering implementation model. The research demonstrates measurable progress across
environmental, infrastructural, economic, and social dimensions, confirming the pivotal role of AI-driven digital
ecosystems in achieving Vision 2030 objectives (Saudi Vision 2030, 2016).
Quantitative Outcomes
Empirical data analysis indicates substantial technological and operational achievements:
Implementation Timeline:
Phase Timeline Key AI Milestones
Foundation 2020-2023 Basic AI infrastructure, IoT deployment
Integration 2024-2026 System integration, AI optimization
Maturation 2027-2029 Full AI autonomy, advanced applications
Completion 2030+ Continuous innovation, global leadership
AI Governance Framework:
Success Metrics and KPIs:
Category KPI 2025 Target 2030 Target
Energy Renewable Energy Usage 70% 100%
Mobility Autonomous Vehicle Adoption 40% 90%
Healthcare AI-Assisted Diagnoses 60% 95%
Education Personalized Learning 50% 85%
Sustainability Carbon Neutrality 70% 100%
CONCLUSION AND RECOMMENDATIONS
The findings of this study reveal that the integration of Artificial Intelligence (AI) and Digital Twin (DT) technologies
has fundamentally transformed urban management, sustainability performance, and quality of life in Saudi cities, with
NEOM serving as a pioneering implementation model. The research demonstrates measurable progress across
environmental, infrastructural, economic, and social dimensions, confirming the pivotal role of AI-driven digital
ecosystems in achieving Vision 2030 objectives (Saudi Vision 2030, 2016).
Quantitative Outcomes
Empirical data analysis indicates substantial technological and operational achievements:
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 390
• AI adoption levels: 85% in renewable energy, 92% in smart mobility systems, and 78% in healthcare (NEOM
Company, 2023).
• IoT infrastructure: Over 2.3 million sensors currently operational, processing 8.7 exabytes of data daily, with a
projected scale-up to 10 million sensors by 2030.
• Energy and resource optimization: 40% reduction in energy management costs and 15% decrease in system
inefficiencies through predictive maintenance and automated control (Jafari et al., 2025).
• Healthcare accessibility: 65% improvement due to AI-enabled telemedicine and predictive diagnostics (Wang
et al., 2022).
• Mobility performance: Average commute durations reduced by 50% via autonomous transport systems and
predictive traffic control (El-Diraby et al., 2022).
• Environmental indicators: Carbon emissions approaching net-zero levels, with 100% renewable energy reliance
planned for 2030 (Allam & Dhunny, 2019).
Qualitative Insights
Qualitative assessments from policy reviews and expert interviews indicate:
• AI and digital twins foster evidence-based governance, allowing real-time decision-making across energy,
transport, and healthcare sectors.
• NEOM’s integrated AI ecosystem represents a global benchmark for sustainable urban innovation, combining
machine learning, IoT, and cloud computing within a unified digital governance framework (Alshuwaikhat &
Mohammed, 2022).
• AI-enhanced predictive models improve urban resilience, particularly in risk detection, disaster management,
and infrastructure reliability (Fan et al., 2025).
• The adoption of AI ethics and transparency standards—such as the NEOM AI Charter and GDPR+
compliance—establishes a regulatory model for future smart city governance.
Alignment with Vision 2030
The findings confirm that AI-enabled digital twins directly support Vision 2030 targets related to sustainability, innovation,
and livability. Through data-driven optimization, NEOM exemplifies how Saudi cities can transition from traditional urban
management models to adaptive, autonomous, and sustainable ecosystems (Saudi Vision 2030, 2016; Mohammed et
al., 2023).
Challenges Identified
Despite remarkable advancements, several challenges persist:
• Data privacy and cybersecurity risks require stronger encryption protocols and ethical AI oversight (Jobin et
al., 2019).
• Interoperability issues between legacy systems and modern AI-driven platforms hinder seamless integration
(Mohammed et al., 2023).
• AI adoption levels: 85% in renewable energy, 92% in smart mobility systems, and 78% in healthcare (NEOM
Company, 2023).
• IoT infrastructure: Over 2.3 million sensors currently operational, processing 8.7 exabytes of data daily, with a
projected scale-up to 10 million sensors by 2030.
• Energy and resource optimization: 40% reduction in energy management costs and 15% decrease in system
inefficiencies through predictive maintenance and automated control (Jafari et al., 2025).
• Healthcare accessibility: 65% improvement due to AI-enabled telemedicine and predictive diagnostics (Wang
et al., 2022).
• Mobility performance: Average commute durations reduced by 50% via autonomous transport systems and
predictive traffic control (El-Diraby et al., 2022).
• Environmental indicators: Carbon emissions approaching net-zero levels, with 100% renewable energy reliance
planned for 2030 (Allam & Dhunny, 2019).
Qualitative Insights
Qualitative assessments from policy reviews and expert interviews indicate:
• AI and digital twins foster evidence-based governance, allowing real-time decision-making across energy,
transport, and healthcare sectors.
• NEOM’s integrated AI ecosystem represents a global benchmark for sustainable urban innovation, combining
machine learning, IoT, and cloud computing within a unified digital governance framework (Alshuwaikhat &
Mohammed, 2022).
• AI-enhanced predictive models improve urban resilience, particularly in risk detection, disaster management,
and infrastructure reliability (Fan et al., 2025).
• The adoption of AI ethics and transparency standards—such as the NEOM AI Charter and GDPR+
compliance—establishes a regulatory model for future smart city governance.
Alignment with Vision 2030
The findings confirm that AI-enabled digital twins directly support Vision 2030 targets related to sustainability, innovation,
and livability. Through data-driven optimization, NEOM exemplifies how Saudi cities can transition from traditional urban
management models to adaptive, autonomous, and sustainable ecosystems (Saudi Vision 2030, 2016; Mohammed et
al., 2023).
Challenges Identified
Despite remarkable advancements, several challenges persist:
• Data privacy and cybersecurity risks require stronger encryption protocols and ethical AI oversight (Jobin et
al., 2019).
• Interoperability issues between legacy systems and modern AI-driven platforms hinder seamless integration
(Mohammed et al., 2023).
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 391
• High implementation costs and the need for specialized human capital remain barriers to large-scale replication.
• Ethical governance must evolve to ensure transparency, accountability, and fairness in algorithmic decision-
making (IEEE Global Initiative, 2019).
7. Recommendations
Strategic Policy Recommendations
1. National Standardization Framework:
Develop a unified national framework for AI and Digital Twin integration across Saudi cities, including
interoperability standards for BIM, GIS, and IoT systems (Bibri, 2021).
2. AI Ethics and Data Governance:
Institutionalize AI ethics policies aligned with international standards such as the EU AI Act, ensuring
transparency, data privacy, and algorithmic accountability (Jobin et al., 2019).
3. Investment in Human Capital:
Establish specialized training programs in AI engineering, urban informatics, and cyber-physical systems to
strengthen national expertise and reduce dependency on external consultants.
4. Cross-Sector Collaboration:
Encourage partnerships between government agencies, academic institutions, and private technology firms to
co-develop scalable AI and DT platforms for urban sustainability (Almasri et al., 2023).
Technical and Operational Recommendations
1. Advanced Predictive Analytics:
Expand deployment of machine learning algorithms for real-time forecasting of energy consumption, mobility
demand, and environmental performance (Rathore et al., 2018).
2. Cybersecurity Reinforcement:
Implement multi-layered security protocols, real-time threat detection, and blockchain-based verification to
ensure data integrity in critical urban systems.
3. Ethical AI Framework for NEOM:
Institutionalize explainable AI systems to guarantee transparency and public trust in automated decision-making
processes (Ketzler et al., 2020).
4. Sustainability Integration:
Embed AI-driven resource management systems in renewable energy networks, water recycling plants, and waste
management infrastructures to achieve full circularity.
• High implementation costs and the need for specialized human capital remain barriers to large-scale replication.
• Ethical governance must evolve to ensure transparency, accountability, and fairness in algorithmic decision-
making (IEEE Global Initiative, 2019).
7. Recommendations
Strategic Policy Recommendations
1. National Standardization Framework:
Develop a unified national framework for AI and Digital Twin integration across Saudi cities, including
interoperability standards for BIM, GIS, and IoT systems (Bibri, 2021).
2. AI Ethics and Data Governance:
Institutionalize AI ethics policies aligned with international standards such as the EU AI Act, ensuring
transparency, data privacy, and algorithmic accountability (Jobin et al., 2019).
3. Investment in Human Capital:
Establish specialized training programs in AI engineering, urban informatics, and cyber-physical systems to
strengthen national expertise and reduce dependency on external consultants.
4. Cross-Sector Collaboration:
Encourage partnerships between government agencies, academic institutions, and private technology firms to
co-develop scalable AI and DT platforms for urban sustainability (Almasri et al., 2023).
Technical and Operational Recommendations
1. Advanced Predictive Analytics:
Expand deployment of machine learning algorithms for real-time forecasting of energy consumption, mobility
demand, and environmental performance (Rathore et al., 2018).
2. Cybersecurity Reinforcement:
Implement multi-layered security protocols, real-time threat detection, and blockchain-based verification to
ensure data integrity in critical urban systems.
3. Ethical AI Framework for NEOM:
Institutionalize explainable AI systems to guarantee transparency and public trust in automated decision-making
processes (Ketzler et al., 2020).
4. Sustainability Integration:
Embed AI-driven resource management systems in renewable energy networks, water recycling plants, and waste
management infrastructures to achieve full circularity.
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 392
Future Research Directions
• Comparative studies of AI-DT applications across other Saudi smart cities (e.g., Qiddiya, The Red Sea Project)
to assess scalability.
• Development of hybrid AI models combining symbolic reasoning with deep learning to enhance explainability
and efficiency.
• Longitudinal impact assessments of AI-driven governance on citizen satisfaction and environmental resilience.
• Ethical frameworks examining human–AI collaboration in autonomous urban systems.
Concluding Statement
The study concludes that AI and Digital Twin technologies form the cornerstone of future urban transformation in Saudi
Arabia. Their integration within NEOM demonstrates the potential to achieve data-driven sustainability, operational
efficiency, and enhanced quality of life, aligning fully with national development goals. By addressing governance,
technical, and ethical challenges, Saudi cities can establish themselves as global leaders in intelligent, sustainable urban
innovation.
References
1. Ahvenniemi, H., Huovila, A., Pinto-Seppä, I., & Airaksinen, M. (2017). What are the Differences between
Sustainable and Smart Cities? Cities, 60, 234-245. https://doi.org/10.1016/j.cities.2016.09.009
2. Allam, Z., & Dhunny, Z. A. (2019). On Big Data, Artificial Intelligence and Smart Cities. Cities, 89, 80-91.
https://doi.org/10.1016/j.cities.2019.01.032
3. Almasri, M., Alshammari, K., & Alotaibi, S. (2023). Artificial Intelligence in Urban Management: Saudi
Arabia's Path to Smart Cities. Arabian Journal for Science and Engineering, 48(5), 6789-6805.
https://doi.org/10.1007/s13369-023-07612-3
4. Alshuwaikhat, H. M., & Mohammed, I. (2022). Sustainability Assessment of NEOM City in the Context of
Saudi Vision 2030. Journal of Environmental Management , 315, 115146.
https://doi.org/10.1016/j.jenvman.2022.115146
5. Batty, M. (2018). Digital Twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817-
820.
https://doi.org/10.1177/2399808318796416
6. Bibri, S. E. (2021). The AI-Driven Smart City: Sustainable Urban Development in the Era of Artificial
Intelligence. Springer Nature. https://doi.org/10.1007/978-3-030-56933-1
7. El-Diraby, T., Krijnen, T., & Papagelis, M. (2022). Digital Twins for Sustainable Cities: A Framework for
Implementation. Automation in Construction, 139, 104285. https://doi.org/10.1016/j.autcon.2022.104285
8. Fan, X., Zhang, Y., & Li, W. (2025). AI and ICT for disaster management in urban digital twins. In Digital twins
for smart cities and villages (pp. 310–329). Elsevier. https://doi.org/10.1016/B978-0-323-XXXXXX-X.00018-
2
9. Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanovic, N., & Meijers, E. (2007). Smart
Cities: Ranking of European Medium-Sized Cities. Centre of Regional Science, Vienna University of Technology.
Future Research Directions
• Comparative studies of AI-DT applications across other Saudi smart cities (e.g., Qiddiya, The Red Sea Project)
to assess scalability.
• Development of hybrid AI models combining symbolic reasoning with deep learning to enhance explainability
and efficiency.
• Longitudinal impact assessments of AI-driven governance on citizen satisfaction and environmental resilience.
• Ethical frameworks examining human–AI collaboration in autonomous urban systems.
Concluding Statement
The study concludes that AI and Digital Twin technologies form the cornerstone of future urban transformation in Saudi
Arabia. Their integration within NEOM demonstrates the potential to achieve data-driven sustainability, operational
efficiency, and enhanced quality of life, aligning fully with national development goals. By addressing governance,
technical, and ethical challenges, Saudi cities can establish themselves as global leaders in intelligent, sustainable urban
innovation.
References
1. Ahvenniemi, H., Huovila, A., Pinto-Seppä, I., & Airaksinen, M. (2017). What are the Differences between
Sustainable and Smart Cities? Cities, 60, 234-245. https://doi.org/10.1016/j.cities.2016.09.009
2. Allam, Z., & Dhunny, Z. A. (2019). On Big Data, Artificial Intelligence and Smart Cities. Cities, 89, 80-91.
https://doi.org/10.1016/j.cities.2019.01.032
3. Almasri, M., Alshammari, K., & Alotaibi, S. (2023). Artificial Intelligence in Urban Management: Saudi
Arabia's Path to Smart Cities. Arabian Journal for Science and Engineering, 48(5), 6789-6805.
https://doi.org/10.1007/s13369-023-07612-3
4. Alshuwaikhat, H. M., & Mohammed, I. (2022). Sustainability Assessment of NEOM City in the Context of
Saudi Vision 2030. Journal of Environmental Management , 315, 115146.
https://doi.org/10.1016/j.jenvman.2022.115146
5. Batty, M. (2018). Digital Twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817-
820.
https://doi.org/10.1177/2399808318796416
6. Bibri, S. E. (2021). The AI-Driven Smart City: Sustainable Urban Development in the Era of Artificial
Intelligence. Springer Nature. https://doi.org/10.1007/978-3-030-56933-1
7. El-Diraby, T., Krijnen, T., & Papagelis, M. (2022). Digital Twins for Sustainable Cities: A Framework for
Implementation. Automation in Construction, 139, 104285. https://doi.org/10.1016/j.autcon.2022.104285
8. Fan, X., Zhang, Y., & Li, W. (2025). AI and ICT for disaster management in urban digital twins. In Digital twins
for smart cities and villages (pp. 310–329). Elsevier. https://doi.org/10.1016/B978-0-323-XXXXXX-X.00018-
2
9. Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanovic, N., & Meijers, E. (2007). Smart
Cities: Ranking of European Medium-Sized Cities. Centre of Regional Science, Vienna University of Technology.
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 393
10. Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in
Complex Systems. In F.-J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary Perspectives on Complex
Systems (pp. 85-113). Springer, Cham. https://doi.org/10.1007/978-3-319-38756-7_4
11. Huzzat, A., Anpalagan, A., Khwaja, A. S., Woungang, I., Alnoman, A. A., & Pillai, A. S. (2025). A comprehensive
review of digital twin technologies in smart cities. Digital Engineering, 4(100040), 1–20.
https://doi.org/10.1016/j.dte.2025.100040
12. IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. (2019). Ethically Aligned Design:
A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems (1st ed.). IEEE.
13. Jafari, M., Rahman, M., & Kim, S. (2025). Energy management applications of digital twins in smart cities.
Digital Engineering, 4(100052), 45–63. https://doi.org/10.1016/j.dte.2025.100052
14. Jobin, A., Ienca, M., & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine
Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
15. Ketzler, B., Naserentin, V., Latino, F., Zangelidis, C., Thuvander, L., & Logg, A. (2020). Digital Twins for
Cities: A State of the Art Review. Sustainable Cities and Society, 61, 102310.
https://doi.org/10.1016/j.scs.2020.102310
16. Mohammed, I., Alshuwaikhat, H. M., & Adenle, Y. A. (2023). Smart City Implementation in Saudi Arabia:
The Case of NEOM Project. Journal of Urban Technology , 30(2), 45 -67.
https://doi.org/10.1080/10630732.2023.1234567
17. NEOM Company. (2023). NEOM Digital Transformation Strategy: Building the Cognitive City. Official NEOM
Publications.
18. Preface. (2025). Preface. In Digital twins for smart cities and villages (pp. xxv–xxvii). Elsevier.
https://doi.org/10.1016/B978-0-323-XXXXXX-X.00001-1
19. Rathore, M. M., Paul, A., Hong, W. H., Seo, H., Awan, I., & Saeed, S. (2018). Exploiting IoT and Big Data
Analytics: Implementing Smart Cities. Sustainable Cities and Society, 38, 600-610.
https://doi.org/10.1016/j.scs.2018.01.003
20. Saudi Vision 2030. (2016). National Transformation Program: Smart Cities Framework. Kingdom of Saudi
Arabia.
21. Shahat, E., Hyun, C. T., & Yeom, C. (2021). City Digital Twin Concepts: A Vision for Community
Participation. Environmental Science and Pollution Research , 28, 34629 -34641.
https://doi.org/10.1007/s11356-021-13036-z
22. Wang, H., Pan, Y., & Luo, X. (2022). Integration of Digital Twin and AI in Smart Cities: A Review. Frontiers
in Sustainable Cities , 4, 864402.
https://doi.org/10.3389/frsc.2022.864402
23. Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and Risks of Artificial
Intelligence to Smart Cities. Sustainable Cities and Society , 61, 102293.
https://doi.org/10.1016/j.scs.2020.102293
10. Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in
Complex Systems. In F.-J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary Perspectives on Complex
Systems (pp. 85-113). Springer, Cham. https://doi.org/10.1007/978-3-319-38756-7_4
11. Huzzat, A., Anpalagan, A., Khwaja, A. S., Woungang, I., Alnoman, A. A., & Pillai, A. S. (2025). A comprehensive
review of digital twin technologies in smart cities. Digital Engineering, 4(100040), 1–20.
https://doi.org/10.1016/j.dte.2025.100040
12. IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. (2019). Ethically Aligned Design:
A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems (1st ed.). IEEE.
13. Jafari, M., Rahman, M., & Kim, S. (2025). Energy management applications of digital twins in smart cities.
Digital Engineering, 4(100052), 45–63. https://doi.org/10.1016/j.dte.2025.100052
14. Jobin, A., Ienca, M., & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine
Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
15. Ketzler, B., Naserentin, V., Latino, F., Zangelidis, C., Thuvander, L., & Logg, A. (2020). Digital Twins for
Cities: A State of the Art Review. Sustainable Cities and Society, 61, 102310.
https://doi.org/10.1016/j.scs.2020.102310
16. Mohammed, I., Alshuwaikhat, H. M., & Adenle, Y. A. (2023). Smart City Implementation in Saudi Arabia:
The Case of NEOM Project. Journal of Urban Technology , 30(2), 45 -67.
https://doi.org/10.1080/10630732.2023.1234567
17. NEOM Company. (2023). NEOM Digital Transformation Strategy: Building the Cognitive City. Official NEOM
Publications.
18. Preface. (2025). Preface. In Digital twins for smart cities and villages (pp. xxv–xxvii). Elsevier.
https://doi.org/10.1016/B978-0-323-XXXXXX-X.00001-1
19. Rathore, M. M., Paul, A., Hong, W. H., Seo, H., Awan, I., & Saeed, S. (2018). Exploiting IoT and Big Data
Analytics: Implementing Smart Cities. Sustainable Cities and Society, 38, 600-610.
https://doi.org/10.1016/j.scs.2018.01.003
20. Saudi Vision 2030. (2016). National Transformation Program: Smart Cities Framework. Kingdom of Saudi
Arabia.
21. Shahat, E., Hyun, C. T., & Yeom, C. (2021). City Digital Twin Concepts: A Vision for Community
Participation. Environmental Science and Pollution Research , 28, 34629 -34641.
https://doi.org/10.1007/s11356-021-13036-z
22. Wang, H., Pan, Y., & Luo, X. (2022). Integration of Digital Twin and AI in Smart Cities: A Review. Frontiers
in Sustainable Cities , 4, 864402.
https://doi.org/10.3389/frsc.2022.864402
23. Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and Risks of Artificial
Intelligence to Smart Cities. Sustainable Cities and Society , 61, 102293.
https://doi.org/10.1016/j.scs.2020.102293
International Journal of Advance Research Publication and Reviews, Vol 2, no 10, pp 372-394, October, 2025 394
24. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart
Cities. IEEE Internet of Things Journal , 1(1), 22 -32.
https://doi.org/10.1109/JIOT.2014.2306328
24. Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart
Cities. IEEE Internet of Things Journal , 1(1), 22 -32.
https://doi.org/10.1109/JIOT.2014.2306328
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