Traffic Light Green-Time Prediction Using Machine Learning: A Review Of Adaptive Signal Control From Fixed Timing To Learning-Based Intersections

Traffic congestion is a major urban problem, worsened by conventional traffic lights that operate on fixed time intervals without considering how many vehicles are waiting on each approach. This paper reviews approaches to adaptive traffic signal control and focuses on using machine learning to predict suitable green-light durations for each lane of an intersection based on traffic demand. It traces the field from classical signal timing formulas through adaptive systems that adjust timings from detector data, to data-driven prediction of traffic flow and reinforcement learning controllers that learn signal policies through simulation. Input parameters such as lane-wise vehicle counts from cameras, traffic density, queue length, waiting time and time of day are discussed, along with traffic simulators used for safe training and evaluation. A five-layer prototype architecture is proposed in which vehicle detection feeds feature construction, a regression model predicts green times, a safety and constraint layer enforces minimum and maximum phases and pedestrian crossing times, and a controller updates the signal. Research gaps, objectives, a comparison of control approaches and challenges such as sensor reliability, mixed Indian traffic and coordination across intersections are discussed.