Traffic signal control, adaptive control, historical data analysis, discrete control
Abstract
Traffic jams have become a worldwide presence where urban centers are concerned, and it is clearly seen that traditional fixed-time control systems are not as flexible. The systems fail to have some ability to react to real-time traffic conditions, situations that bring a lot of inefficiencies, more fuel consumption, and more emissions. This paper forwards a new data-driven traffic signal control optimization technique, which deploys historical data analysis and real-time adaptive control. Supplying historical traffic pattern data, the presented system estimates traffic volume and makes use of intelligent signal control plans that advocate for movable vehicles and clean lungs. Dynamic control allows the system to adapt by managing the signal via real-time live traffic conditions.
