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?
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