THE INTERNET OF TRAFFIC LIGHTS (IOTL). AN EVALUATION OF SELF-ADJUSTING FUZZY LOGIC HYBRID VEHICULAR TRAFFIC CONTROL BY RNN IN COLONIAL CITIES

Traffic congestion poses significant challenges in historic cities striving to balance modern mobility needs and heritage preservation. This paper proposes a self-adaptive fuzzy logic control system for traffic signals optimized by a recurrent neural network (RNN) for vehicular density prediction. The fuzzy controller dynamically adjusts signal timing based on real-time traffic density data at intersections in the colonial cities. The RNN component forecasts traffic density to tune the fuzzy membership functions, enabling adaptive signal control. Simulation experiments demonstrate noticeable reductions in queue length using the proposed neuro-fuzzy method compared to uncontrolled and fuzzy logic only techniques. Improvements are positively correlated to street length, although less significant in very short streets. The system demonstrates promising capabilities to reduce congestion and emissions through adaptive optimization in complex urban environments.