Traffic Signaling Optimization for Intelligent and Green Transportation in Smart Cities

In today's world, population growth causes more vehicle traffic and also this increase in vehicle traffic causes more traffic congestion, air pollution, fuel consumption, health problems and so on. In order to solve these problems in this study, we applied traffic signaling optimization by using ant colony algorithm on an urban isolated intersection structure. Vehicles movement are set according to VANET architecture and traffic data are transmitted from road to center via VAN ET. It was aimed to show that decreasing in average waiting times of vehicles at intersections reduces the CO2 emissions, fuel consumption and noise ratios in the study. For this purpose, simulations run using traffic scenarios with different vehicle density and results obtained were also compared with Webster's equations and fixed time systems in current traffic signaling techniques. Thus, it was realized that the use of ACO (Ant Colony Optimization) in traffic signaling systems gives better results in dense and variable traffic conditions.