Hybrid AI and Big Data Frameworks for Smart City Infrastructure Planning
DOI:
https://doi.org/10.15662/IJEETR.2024.0606016Keywords:
Smart City Resilience, Hybrid AI and Big Data Frameworks, Urban Crisis Modeling, Real-Time Urban Data Analytics, Data-Driven Urban Planning, Mixed-AI Intelligent Agents, Urban Systems Integration, Real-Time Traffic Prediction, Infrastructure Adaptation Strategies, Urban Energy and Transport Coordination, Crisis Response Optimization, Multidisciplinary Urban Systems Engineering, Predictive Flow Modeling at Intersections, Data-Science-Driven Urban Governance, Simulation-Based Emergency ManagementAbstract
Smart cities constitute poles for the economic, cultural, and social interactions that shape the evolution of areas. The resilience of cities is key to facing environmental, technological, or health crises. Resilience is related to the capacity for quickly taking optimal decisions in response to crises, and hence to the ability to timely absorb and analyse large volumes of data. Data are generated continuously by urban systems and reside in different databases held by different stakeholders. Hybrid AI and Big Data frameworks enable real-time responses to crises, implement data-driven and simulative models, and produce data-science, model-driven and Mixed-AI Intelligent Agents for the modelling of urban crises. However, applying such frameworks in planning urban systems are highly demanding.
The urban systems and services enabling the operations of cities and urban networks are complex, multidisciplinary, and partitioned within traditional areas of expertise, both in engineering and in information technologies. External funding is mostly project based, and concentrated on individual sectors, and not on an integrated planning approach. Most importantly, real-time responses to crises require data-driven models able to reproduce an emergency. For the integrated planning of transport, energy, and communication systems, real-time traffic prediction, demand signalling, and infrastructure adaptation are mandatory. For intersections of two roads, real-time data analytics require prediction of the expected intensity of flows.
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