Digital twins—virtual models that simulate real-world city dynamics—are transforming urban transportation and mobility planning. These intelligent systems integrate real-time data, machine learning (ML) algorithms, and transportation research to optimize citywide solutions. By simulating traffic patterns, travel demand, and road dynamics, cities can make informed, equitable decisions that benefit all residents. Yet, critical questions arise as urban areas embrace these artificial intelligence (AI)-driven tools. Addressing these issues requires a thoughtful and ethical approach, where ML and transportation research converge to shape the future of smart cities.
Experts like Dr. Anu Kuncheria are aligning cities’ needs with technology to achieve more sustainable urban futures. Dr. Kuncheria is an ML engineer at Uber Technologies and a former researcher at the University of California, Berkeley, where she earned her Ph.D. in engineering, specializing in transportation, geospatial analytics, and machine learning. She also conducted research at Lawrence Berkeley National Laboratory, where she developed big data solutions for mobility and transportation. Her work leverages high-performance simulations and traffic models to analyze and characterize regional and citywide transportation dynamics. With expertise in the public and private sectors, she has researched large-scale traffic simulations in the United States and internationally. In this Q&A, Dr. Kuncheria explains the significance of digital twins and the future of transportation in urban planning.
Q: What advances in technology have made digital twins scalable for urban transportation planning?
Kuncheria: Advancements in high-performance computing, big data, Internet of Things (IoT), and AI allow digital twins to be applied at scale today. In the past, modeling an entire city was difficult due to computational limitations, which made it challenging to perform multiple iterations and limited data-driven decision-making. Today, optimization techniques and parallel computing drastically reduce simulation times. For example, Mobiliti, a high-performance traffic simulator developed by UC Berkeley and Lawrence Berkeley National Laboratory, can model the entire Bay Area of more than 100 cities in under 30 minutes. This represents a major advancement in the scalability and utility of digital twins for urban planning.
ML is essential in facilitating real-time responsiveness. By combining ML models with real time data from sensors, it’s now possible to forecast traffic, predict congestion, and coordinate immediate responses. For instance, during the 2019 Richmond Bridge closure in San Francisco, traffic chaos could have been mitigated with a digital twin powered by live data. As cities face growing complexity, digital twins offer a critical advantage, allowing planners to simulate and optimize responses to large-scale incidents in minutes, not hours.
Data access is also crucial. Open-source datasets like CalTrans PeMS, GTFS transit data, and Uber Movement offer valuable inputs for model calibration and validation. Yet, many high-resolution datasets collected by private companies, such as navigation app providers and transportation network organizations, are restricted or locked behind paywalls. Expanding access to high-quality private data will improve simulation accuracy and drive broader adoption.
Q: How are digital twins being used in real-world city applications?
Kuncheria: Internationally, cities are implementing digital twin technology with promising results. Singapore is a notable early adopter and innovator of full-scale digital-twin modeling in urban planning. Its national-scale project now informs a wide range of projects for resilient systems, such as collaborations with ETH-Zurich, utilizing Singapore’s digital twins to test and map strategies for citywide cooling and targeted decarbonization.
Sejong City in South Korea used digital-twin modeling research to optimize waste management and garbage collection routes. Many U.S. cities are also exploring digital twins. For example, Las Vegas and Orlando have developed small-scale digital twins focused on their downtown areas. Meanwhile, Eindhoven and several Mediterranean cities are actively exploring digital twin research to improve mobility and sustainability.
Q: What benefits do digital twins provide for urban planning, especially regarding transportation, sustainability, and community involvement?
Kuncheria: Digital twins enable the testing of multiple what-if scenarios, helping city planners evaluate and choose the best resource and management strategies. This is invaluable for managing congestion, reducing pollution, and improving overall quality of life. To effectively address these challenges, decision-makers must consider a growing range of metrics and factors and analyze the tradeoffs when planning for smarter, more sustainable cities. Digital twins can architect the integration of these diverse metrics and data types, providing a more holistic understanding of how planning decisions can optimize for positive community impacts.
Additionally, digital twins open the door for greater community engagement and collaboration. Citizens and researchers can model how different choices affect communities and neighborhoods, making planning processes more informed and data-driven. These collaborative stakeholder approaches help cities tailor policies to specific needs and support smarter, more sustainable planning by aligning technology with human-centered objectives.
Further, digital twins are powerful tools for identifying and learning city typologies based on real-world mobility patterns and behaviors, helping foster inter-city collaborations.
Q: What roles do different stakeholders play in the effectiveness of digital twins?
Kuncheria: Effective digital twin deployment requires close collaboration among city governments, researchers, private companies, and the public. Governance is essential. Models must be calibrated and validated with current data since an outdated model won’t reflect real-world conditions. Responsibility for these technologies extends beyond the developers. Users must ensure transparency, accuracy, and relevance. Community input also plays an important role in setting priorities and evaluating trade-offs. By simulating conditions in a virtual environment, digital twins can reduce implementation risks and ensure that new solutions are aligned with long-term mobility goals.
Q: What challenges limit the widespread adoption of digital twins, and what is the future of mobility and city planning?
Kuncheria: Digital twins face several key challenges, including data integration, real-time processing, high implementation costs, and ensuring data privacy. Many cities struggle to gather and harmonize diverse data sources, such as traffic sensors, public transit feeds, weather data, and user behavior, which are crucial for accurate, dynamic simulations. Maintaining a real-time digital twin requires significant computational power and robust infrastructure, often beyond the reach of smaller municipalities. As a result, only a few cities are ready or willing to invest in digital twin technologies. With governance playing a significant role in the implementation and efficacy of digital twins, adoption is a barrier to development. Another challenge includes efforts and technical know-how in model parameterization and validation that can undermine model accuracy and impact community outcomes. To overcome these barriers, cities and researchers will need to invest in rigorous testing frameworks and prioritize data management to ensure simulations truly reflect real-world conditions.
While digital twins and AI offer immense potential, they should not serve as final decision-makers. This is where human insight and the power of community become essential. Ideally, digital twins can analyze complex scenarios, and AI can provide recommendations, but final decisions should rest with stakeholders. It’s important to recognize that urban planning requires tailored solutions—no one-size-fits-all approach exists. With strategic integration, digital twins will create smarter, more inclusive, and more resilient communities.
The Future of Cities
Digital twins represent a profound achievement in coordination, research, and innovation, spanning industry, academia, and governance efforts. The integration and collaboration bring city-scale simulation to actionable reality. Digital-twin technologies require careful calibration but offer extraordinary promise to improve outcomes across many planning domains and may lead to better outcomes for energy consumption, environmental conditions, and long-term disaster mitigation. The simulated city offers a civic platform for collaboration among diverse stakeholders to tackle pressing problems and holistically improve quality and resilience throughout urban life. To achieve this, researchers such as Dr. Kuncheria strive to promote these tools, inform decision-makers, and share their insights to create better cities of the future.
About the Author
Michael Martin is a freelance writer covering technology, science, humanities, and law. He seeks to promote cross-cultural understanding and learning through written discourse on critical and emerging technologies. Michael holds a bachelor’s degree in IT management from Trident University and a master’s degree in technical communication and localization from the University of Strasbourg. Connect with him on LinkedIn.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.
