AI- BASED SMART CITY SIMULATION AND OPTIMIZATION FRAMEWORK

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

The rapid urbanization and increasing complexity of modern cities present significant challenges in managing resources, infrastructure, and services efficiently. To address these challenges, this paper introduces an AI-based Smart City Simulation and Optimization Framework designed to enhance decision-making processes and optimize urban planning. The framework leverages advanced artificial intelligence techniques, including machine learning, deep learning, and reinforcement learning, to model, simulate, and optimize various aspects of smart city operations. The proposed framework integrates diverse data sources such as traffic flow, energy consumption, environmental conditions, and social dynamics to create a comprehensive virtual representation of the city. Through this simulation, stakeholders can analyze the impacts of different policies, infrastructure developments, and technological interventions in a controlled environment. The AI algorithms within the framework enable predictive analytics, allowing for the anticipation of future trends and potential issues, thereby facilitating proactive management. A key feature of the framework is its optimization module, which employs multi-objective optimization algorithms to balance conflicting goals, such as reducing traffic congestion while minimizing environmental impact. The framework also supports real-time data integration and adaptive learning, ensuring that the system remains responsive to dynamic urban conditions and evolving citizen needs. Case studies demonstrating the application of the framework in various urban scenarios, such as smart transportation, energy management, and emergency response, are presented. These studies highlight the framework's capability to improve efficiency, sustainability, and quality of life in smart cities. In conclusion, the AI-based Smart City Simulation and Optimization Framework offers a powerful tool for urban planners, policymakers, and city administrators, providing a scalable and adaptable solution to the complexities of modern urban management. Future work will focus on enhancing the framework's scalability, expanding its data integration capabilities, and refining its AI models to accommodate a broader range of urban challenges.

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Keywords

Smart City Simulation, AI Optimization, Urban Planning Integration, Real-Time Analytics.