Smart City Simulation and Design using AI

Abstract

The idea of smart cities is a viable answer to the problems that urbanization is posing to cities worldwide, and artificial intelligence (AI) is a key component of this change. With an emphasis on its six primary domains—smart mobility, smart environment, smart governance, smart living, smart economics, and smart people—this study reviews the literature on AI solutions used in smart cities. The Scoop-available publications from 2021 to 2024 are included in the analysis. This study looks at how AI is being used in each field and notes its challenges, developments, and potential paths forward. The analysis\'s objectives were as follows: (1) to find applications and solutions that use AI in smart cities; (2) to find the obstacles that prevent AI from being implemented in smart cities; and (3) to investigate potential future directions for AI use in smart cities.

Introduction

I. Overview

Smart cities are a response to the growing challenges of urbanization, aiming to improve sustainability, efficiency, and quality of life. Artificial Intelligence (AI) is a central enabler of this transformation, impacting areas such as:

• Resource management

• Governance

• Economic development

• Transportation systems

• Public services

Smart cities integrate IoT devices, data analytics, and digital infrastructure to deliver smart solutions across sectors like energy, healthcare, education, mobility, and government.

II. Literature Review

A growing body of research highlights AI's wide-ranging applications in smart cities. Key themes include:

• Citizen Participation: Feher (2021) emphasized the role of public engagement and fostering a "smart mentality" for successful AI adoption.

• Environmental Sustainability:

  • AI tools like Greencoin promote eco-conscious behavior.

  • AI is used in air quality modeling, load forecasting, and energy optimization.

  • Low-cost sensing systems and predictive models assist under-resourced areas.

• Cybersecurity & Governance:

  • AI supports secure e-governance and cyberattack detection in IoT systems.

• Social Well-being: AI frameworks address issues like cyberbullying in urban digital environments.

• Urban Energy Management: Neural networks are used for forecasting energy usage and improving grid efficiency.

Despite these advances, common challenges include:

• Data governance

• Algorithmic bias

• Integration complexity

• Ethical concerns

III. Methodology

The study uses a literature review approach based on Scopus-indexed papers from 2021 to January 2024. Key findings include:

• Over 773 papers combine the keywords "artificial intelligence" and "smart city".

• Selection aimed at focusing on recent advancements in the AI–smart city intersection.

IV. Key Domain Focus – Smart Environment

AI in smart environments emphasizes:

• Energy conservation

• Smart metering

• Load forecasting using recurrent neural networks

• Smart grid optimization

• Energy theft detection

V. Future Directions

The paper outlines areas for further research, including:

• Multi-agent and multi-scale simulations

• AI-powered urban planning

• Integration with digital twins

• Ethical and policy-driven AI design

• Human behavior modeling in urban systems

Conclusion

The swift development of artificial intelligence offers smart cities enormous potential to solve urban problems and enhance citizens\' quality of life. This study has determined the key uses, implementation challenges, and future paths of AI solutions in the six primary domains of smart cities—smart mobility, smart environment, smart governance, smart living, smart economy, and smart people—by analyzing the literature from 2021 to January 2024. This study\'s reliance on peer-reviewed sources and possible linguistic bias favoring English publications are among its research weaknesses. Furthermore, the methodology of this study, which centers on a literature review approach, can restrict the breadth of analysis and the applicability of the results to all smart cities worldwide. The selection of keywords is also linked to a research constraint. Only the fundamental terms \"artificial intelligence\" and \"smart city\" have been selected. It is possible to use more specific keywords, for example, “urban AI”, “smart governance AI”, “public safety AI”, etc., in future, more detailed research.

References

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Copyright

Copyright © 2025 Gupta Ramkalesh Raghav Prasad, Faizal Hussain, Gopal Ji, Harsh Anand , Dr. Saumya Chaturvedi, Mr. Harendra Malik. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.