AI-driven traffic prediction models for sustainable and resilient urban mobility in IoT-enabled cities

Authors

https://doi.org/10.22105/sci.v2i4.48

Abstract

Rapid urbanization in India has led to severe traffic congestion, negatively affecting economic productivity and the quality of life in cities. This paper examines the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) to develop advanced traffic prediction models that enhance urban mobility. The paper discusses the significance of AI and IoT in urban mobility, presents case studies from major Indian cities, and addresses the challenges and future trends of these technologies. This paper investigates the integration of AI and IoT to develop advanced traffic prediction models tailored for Indian cities. These models enhance traffic management, reduce congestion, and improve public transportation efficiency by leveraging real-time data collected from various IoT devices. This research provides an overview of traditional traffic prediction models, highlights their limitations, and showcases AI- and IoT-driven solutions, with case studies from cities such as Delhi, Bengaluru, Mumbai, Pune, and Ahmedabad. Challenges such as data privacy, regulatory frameworks, and infrastructure limitations are discussed, along with future trends that promise to further enhance urban mobility.

Keywords:

Artificial intelligence, Internet of things, Traffic prediction, Urban mobility, Smart cities, Machine learning, Autonomous vehicles

References

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Published

2025-12-17

Issue

Vol. 2 No. 4 (2025): Smart City Insights

Section

Articles

Categories

How to Cite

Saheli, H., & Nozick, V. (2025). AI-driven traffic prediction models for sustainable and resilient urban mobility in IoT-enabled cities. Smart City Insights, 2(4), 187-196. https://doi.org/10.22105/sci.v2i4.48

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