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By: Yamini N. Deshvena
Abstract
Urban traffic congestion is a persistent challenge, resulting in economic setbacks, extended travel
durations, excessive fuel consumption, and environmental harm. The rapid urbanization and increasing
number of vehicles have intensified this issue, highlighting the need for smarter and more efficient
traffic management solutions. With advancements in artificial intelligence (AI), innovative traffic
control strategies can be implemented to optimize urban mobility and alleviate congestion. This paper
explores AI-driven traffic management solutions, focusing on machine learning-based adaptive traffic
signal control, predictive congestion analysis, and vehicle-to-infrastructure (V2I) communication.
These advanced technologies enable real-time data processing, dynamic traffic flow adjustments, and
predictive analytics to anticipate and mitigate congestion before it occurs. By leveraging AI, traffic
systems can adapt to varying conditions, enhance road network efficiency, and minimize delays. A case
study is conducted in a densely populated metropolitan city to evaluate the impact of AI-powered traffic
management solutions. Key performance indicators, such as traffic efficiency, vehicle delays, fuel
consumption, and overall road network performance are analyzed. The results demonstrate that AI
driven traffic control significantly enhances urban mobility by reducing congestion, improving travel
time reliability, and lowering emissions. Additionally, these solutions contribute to the development of
sustainable and smart city infrastructures, fostering a more efficient and eco-friendly transportation
system This study underscores the potential of AI in revolutionizing urban traffic management and
highlights its role in shaping the future of smart cities. The findings suggest that the integration of AI-
based traffic control systems can serve as a viable long-term solution for addressing the growing
challenges of urbanization, ultimately enhancing the quality of life for city residents.
Keywords: Traffic congestion, artificial intelligence, smart cities, machine learning, adaptive traffic
control, predictive congestion analysis, vehicle-to-infrastructure (V2I) communication, urban mobility,
traffic optimization, sustainable transportation
Citation:
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