Driving Innovation Intelligence in Cities: Digital Twins, Generative AI, and Cyber-Physical Systems
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Item A Digital Twin Approach to Advancing River Emergency Response Systems in Smart Cities(2025-01-07) Zhu, Yueming; Pan, Xiyu; Mohammadi, Neda; Taylor, JohnDrowning incidents and floods in river cities have become a significant public safety concern worldwide. These incidents result in numerous deaths and injuries, highlighting the urgent need for effective monitoring and rescue systems. Traditional safety detection systems for bodies of water are primarily designed for controlled indoor environments, such as indoor pools, where conditions are stable and predictable; relying on sensors and wearable devices, which are not practical for the varied and challenging conditions of outdoor environments (e.g., distance, wider monitoring areas, and environmental factors such as waves). In response to this challenge, we propose a river emergency response system based on a digital twin model, supported by a human detection model, a water level prediction model, and related algorithms. This piloted system employs a single overhead camera as the primary hardware sensor for continuous real-time safety monitoring. We focus on the Chattahoochee River in the Columbus-Phoenix City area, where drowning incidents have surged in recent years. This system aims to improve rescue response time by generating multi-level of danger alerts based on varying real-time conditions.Item Introduction to the Minitrack on Driving Innovation Intelligence in Cities: Digital Twins, Generative AI, and Cyber-Physical Systems(2025-01-07) Mohammadi, Neda; Taylor, JohnItem Leveraging Meta AI, Spatial AI, and Character AI Model for Generative Smart Cities(2025-01-07) Kent, Lee; Karayel, Tolga; Miyake, Youichiro; Villman, TeroCities are complex, dynamic environments, requiring huge numbers of services and systems to facilitate and better the lives of the citizens within them. Keeping up with the demands of modern life has led to the creation of Smart City Digital Twins (SCDT), which are complete and bidirectional Cyber-Physical Systems (CPS) acting as observation and control mechanisms. Current SCDTs are typically bespoke implementations, catering to the city's unique needs and footprint. Generative AI will enable the generation of broader possible visions of the city, but the current data created by SCDTs is insufficient to train generative AI. This is a common problem for AI, and synthetic data is utilised to augment the training set. This paper proposes a novel concept for the creation of synthetic data; the use of the Meta, Character, Spatial Artificial Intelligence (MCS-AI) Model to emulate and therefore build the vast amounts of synthetic data required for a City Generative AI.