In modern cities, growing traffic volumes and limited infrastructure capacity lead to frequent congestion, increased emissions, and reduced quality of life. Traditional traffic management systems, based on fixed signal timings, often fail to adapt to real-time traffic dynamics. This paper presents how artificial intelligence (AI) can significantly enhance the efficiency and sustainability of urban traffic systems. By integrating data from sensors, cameras, and mobile devices with learning and forecasting algorithms, an intelligent system is developed to adjust traffic signals in real time. Simulation results show reduced waiting times, lower greenhouse gas emissions, and improved safety for all road users, including pedestrians and public transport. Special focus is placed on fairness and inclusive mobility, ensuring that technological advancement also addresses social equity. The proposed approach can be implemented across various urban environments without requiring extensive infrastructure changes
In modern cities, growing traffic volumes and limited infrastructure capacity lead to frequent congestion, increased emissions, and reduced quality of life. Traditional traffic management systems, based on fixed signal timings, often fail to adapt to real-time traffic dynamics. This paper presents how artificial intelligence (AI) can significantly enhance the efficiency and sustainability of urban traffic systems. By integrating data from sensors, cameras, and mobile devices with learning and forecasting algorithms, an intelligent system is developed to adjust traffic signals in real time. Simulation results show reduced waiting times, lower greenhouse gas emissions, and improved safety for all road users, including pedestrians and public transport. Special focus is placed on fairness and inclusive mobility, ensuring that technological advancement also addresses social equity. The proposed approach can be implemented across various urban environments without requiring extensive infrastructure changes
In modern cities, growing traffic volumes and limited infrastructure capacity lead to frequent congestion, increased emissions, and reduced quality of life. Traditional traffic management systems, based on fixed signal timings, often fail to adapt to real-time traffic dynamics. This paper presents how artificial intelligence (AI) can significantly enhance the efficiency and sustainability of urban traffic systems. By integrating data from sensors, cameras, and mobile devices with learning and forecasting algorithms, an intelligent system is developed to adjust traffic signals in real time. Simulation results show reduced waiting times, lower greenhouse gas emissions, and improved safety for all road users, including pedestrians and public transport. Special focus is placed on fairness and inclusive mobility, ensuring that technological advancement also addresses social equity. The proposed approach can be implemented across various urban environments without requiring extensive infrastructure changes
