Integrating fuzzy logic and machine learning to improve traffic signal system

Since their invention in 1868, traffic signal systems have significantly influenced how contemporary cities manage their traffic. Enhanced traffic management not only promotes the ease of daily commutes but also has implications for environmental sustainability. Although their primary objective is to mitigate accidents and govern the movement of pedestrians and vehicles, conventional traffic signal systems frequently encounter difficulties in adjusting to the ever-changing characteristics of urban expansion. Throughout history, fixed traffic signal systems have served to regulate and establish order in the realm of urban traffic. Nevertheless, these systems fail to adequately address the current traffic requirements. Emerging technologies like sensors and detectors are tackling this issue by dynamically altering the timing of traffic signals based on real-time traffic conditions. Fuzzy logic present viable opportunities for improving traffic signal control within this particular framework. Due to its capacity to process imprecise or dubious data, fuzzy logic enables traffic control to make more nuanced decisions. The incorporation of fuzzy logic algorithms into traffic signal systems facilitates advanced decision-making by leveraging expert knowledge and real-time data. This empowers the implementation of traffic management strategies that are more responsive and adaptable. Our research centers on the examination of both fuzzy logic and machine learning methodologies into traffic signal systems to develop intelligent solutions for traffic management. Our objective is to successfully simulate traffic flow at individual intersections by utilizing the Simulation of Urban Mobility (SUMO) setup. Our objective is to create adaptive traffic control systems that feature machine-learning algorithms and fuzzy logic. These systems should have the ability to dynamically modify signal timing in response to fluctuations in traffic conditions. The potential benefits of fuzzy logic and also machine learning in urban environments include the optimization of traffic flow, the reduction of congestion, and the minimization of fuel consumption. Our objective is to make a positive contribution to the enhancement of safety, efficiency, and sustainability in contemporary urban transportation systems through the utilization of these technologies. Keyword: Adaptive Traffic control, Fuzzy Logic, Sumo, Machine Learning