AI-Powered Smart City Resource Planning
Keywords:
Smart Cities, Urban Data Ecosystems, Big Data Analytics, Artificial Intelligence (AI), AI-Driven Urban Systems, Data Lifecycle Management, Intelligent Public Services, Urban Sustainability and Resilience, Predictive and Prescriptive Analytics, Urban Decision-Making Systems, Data Integration and Processing, Smart Infrastructure Management, Transportation and Mobility Analytics, Energy and Utilities Optimization, Waste Management Systems, PublicAbstract
Rapid urbanization is a global challenge acclaimed by development agencies and governments alike. Accelerated growth magnifies the demand for essential resources, such as energy, water, transportation, education, and healthcare. Therefore, cities need to address residents' needs while maintaining a high quality of life, innovation, sustainability, and resilience. A smart city implements a citywide data ecosystem, comprising multiple entities that generate, share, and use data effectively. Smart cities are thus capable of delivering intelligent public services and offer harmonious urban living experience for its citizens. Big data and artificial intelligence (AI) play an important role in achieving smart city objectives. Big data technologies and processes ensure reliable data access for different stakeholders, while AI methods provide predictive, prescriptive, and optimization capabilities that are designed for decision-making. AI-driven big data systems are therefore capable of modeling a dynamic system, predicting future trends, and enabling effective planning. AI methods have been successfully applied in many urban segments, including transportation and mobility, energy and utilities, waste management, public health, public safety, and security.
Although several AI applications have shown promising results, AI-based systems for managing the entire data lifecycle in smart cities are still evolving. These systems enable end-to-end big data processes, from data collection through quality control, integration, and decision-making. Cities worldwide are adopting AI-driven big data solutions in these planning segments. Dedicated processes are thus set up to manage the data lifecycle. City domain experts identify, set up, and execute data pipelines that support planning objectives. Decision-making can be accomplished or automated by pattern extraction or predictive modeling. Finally, AI-based algorithms provide effective solutions by minimizing designated target functions, such as cost, journey time, or power utilization.
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