This research presents a novel navigation system harnessing the power of machine learning to address two critical challenges in smart cities: traffic congestion and air pollution. The system seamlessly integrates real-time traffic data and air quality monitoring, enabling dynamic route optimization and proactive pollution mitigation. By leveraging machine learning algorithms and deep learning algorithms such as neural networks, the system predicts traffic congestion patterns and reroutes vehicles for optimal flow, reducing travel time and fuel consumption. Simultaneously, information on cars like the rate of fuel consumption, and engine size provides continuous data, allowing the system to reroute vehicles away from high-pollution areas, thereby minimizing public exposure. This comprehensive approach is anticipated to yield significant benefits, including improved air quality, reduced traffic congestion, and increased public health. Additionally, the system's predictive capabilities enable proactive interventions, fostering the development of more resilient and sustainable urban environments. This research represents a significant step towards intelligent and environmentally conscious urban management, paving the way for smart cities’ healthier and more efficient future. This paper extensively analyses the areas in which pollution emissions can be intelligently and effectively calculated through data mining and deep learning techniques.
A Machine Learning-Powered Navigation System for Smart Cities: Optimizing Traffic Flow and Air Quality
Springer Tracts in hum.- Centered Computing
Doctoral Symposium on Human Centered Computing ; 2024 ; Kolkata, India March 30, 2024 - March 30, 2024
2025-05-01
14 pages
Article/Chapter (Book)
Electronic Resource
English
Revolutionizing Traffic Management with AI-Powered Machine Vision: A Step Toward Smart Cities
ArXiv | 2025
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