Due to deteriorating signal control systems and escalating traffic congestion, urban transportation is facing more and more difficulties. In order to improve urban mobility, this research presents a density-based traffic light management system that dynamically modifies traffic signals in response to real-time traffic density data. By monitoring vehicle flow at junctions with the use of sophisticated sensors and data analytics, the suggested system allows for adaptive signal timing, which improves traffic flow and lessens congestion. The system measures traffic density and examines traffic patterns in real time by integrating vehicle identification technology like cameras and inductive loop sensors. The traffic lights use density-based algorithms to modify their timing in response to changing traffic circumstances. This allows them to prioritize green signals in areas with high traffic volumes and reduce delays for all users of the road. In comparison to conventional fixed-timing traffic signals, preliminary findings show that the density-based control system greatly improves traffic flow and decreases wait times at crossings. Because of the adaptive nature of the system, peak and off-peak traffic may be better managed, resulting in less congestion and more seamless transitions between lanes. By minimizing stop-and-go traffic, this strategy not only improves overall urban mobility but also helps to reduce car emissions and improve air quality. The research shows how density-based traffic signal control systems may revolutionize urban traffic management by offering contemporary cities a scalable and efficient alternative. In order to provide complete traffic control, future research will concentrate on improving the system's algorithms, extending its deployment, and combining it with other smart city technologies.

Due to deteriorating signal control systems and escalating traffic congestion, urban transportation is facing more and more difficulties.