By Dr Umi Rafiah Shukri (umi@might.org.my) and Siti Halimah Ismail (sitihalimah@might.org.my)
Urbanisation is the process by which an increasing number of people choose to live and work in cities rather than in rural areas. Globally, North America, Latin America and the Caribbean lead this trend, with over 80% of their populations residing in urban areas, compared to the global average of 58% (Figure 1). Asia is rapidly joining this trend, contributing more than half of the world’s population. In 2020, approximately 2.6 billion people across Asia lived in urban areas. This number is projected to grow steadily, reaching an estimated 3.48 billion by 2050 (Figure 2). This shift highlights the urgent need for sustainable urban planning, particularly in Asia, where infrastructure and services must keep pace with population growth. The trend presents both challenges and opportunities for shaping liveable, inclusive and resilient cities.
Urban planning today faces complex challenges due to rapid urban growth, climate change, outdated infrastructure and evolving technologies. Urban planners must address climate resilience, upgrade aging systems, and integrate smart technologies while balancing sustainability, economic growth and liveability. Ensuring equitable access to urban resources and coordinating among diverse stakeholders adds complexity to urban planning efforts. In the era of smart cities, two technologies are emerging as game-changers in urban planning, namely digital twins and artificial intelligence (AI). Individually powerful, their combined application is revolutionising how cities are envisioned, managed, and constantly improved. Importantly, it is a process that involves and benefits all stakeholders, making us all part of the smart city development process.
A digital twin is a system continuously updated with real-time data from sensors and connected devices. It allows planners to simulate scenarios, monitor infrastructure, and test decisions before implementing them in practical situations. AI, on the contrary, brings intelligence to these simulations. By analysing vast datasets, identifying patterns and making predictions, AI enables cities to optimise traffic flows, anticipate energy demands, and respond proactively to environmental or social changes. Together, digital twins and AI empower urban planners to move from reactive to predictive and adaptive planning, resulting in creating cities that are not only smarter, but also more sustainable, resilient and inclusive.
Digital Twins: The Concept and Applications
A digital twin is essentially a virtual copy of a physical system, such as a building, a transportation network, or an entire city. The concept of digital twins originated in the early 2000s, introduced by Dr. Michael Grieves during his work on product lifecycle management (PLM) at the University of Michigan. Over the past two decades, the digital twin concept has evolved from manufacturing and aerospace to broader applications, including smart cities, healthcare, energy systems, and infrastructure management.
Today, digital twins are a foundation of smart urban planning, enabling cities to become more responsive, efficient and sustainable. These virtual models operate through a series of steps that allow urban planners to test ideas virtually, anticipate problems, and optimise solutions before making changes in the real world (Figure 3). Sensors, IoT devices and connected systems constantly collect real-world data such as traffic patterns, energy use, weather and infrastructure performance. This data feeds into a digital twin, a virtual replica built with 3D models, Geographic Information System (GIS) data, and system maps that mirror anything from a single building to an entire city. Real-time updates keep the digital twin aligned with the physical world. Urban planners use it to simulate scenarios such as traffic changes, emergencies, or energy surges. AI analyses these simulations to predict outcomes and suggest the best actions. This creates a feedback loop where the system learns, adapts and improves decision-making over time.
The Role of AI In Urban Planning
AI refers to the ability of computer systems to perform tasks that typically require human intelligence, such as learning, problem-solving and decision-making. In urban planning, AI’s proactive nature is evident in its ability to analyse vast and complex datasets from sources like sensors, satellite imagery, public records, and social media. By leveraging machine learning models, AI can forecast future urban conditions using both historical and real-time data. It can also recommend the most efficient solutions to urban challenges, enabling cities to shift from reactive to proactive planning, and reassure us of the potential for smarter, faster and more sustainable decisionmaking (Figure 4).
Real-World Applications and Case Studies
Across top-performing smart cities worldwide, the integration of digital twins and AI is transforming the way urban systems are planned, monitored and managed. These real-world applications can be grouped into six key urban elements, each demonstrating how this technological convergence enhances decision-making, efficiency and resilience. The key urban elements have been tabulated in Table 1.
The Future of Smart Cities: Challenges, Lessons and What’s Next
Digital twins and AI have the potential to make cities smarter, safer and more efficient. However, their adoption comes with challenges, including technical limitations, data privacy issues, inadequate infrastructure, and the need for transparent and equitable governance frameworks. These obstacles often stem not only from technical limitations but also from socio-political and institutional barriers. Figure 5 shows the key challenges in digital twins and AI for smart urban planning.
