Traffic Signaling Optimization for Intelligent and Green Transportation in Smart Cities

In today's world, population growth causes more traffic congestion, air pollution, fuel consumption, infrastructural inadequacy, health problems and so on. In order to solve these problems, some structures that have centralized, coordinated and cost efficiency plans with using limited resources through information technology, are necessary. So some smart city applications in energy, environment, transportation, infrastructure etc. have being developed to solve these problems of big cities. One of the most important smart city applications is intelligent transportation systems that allow people to drive more securely and comfortably by controlling the traffic density in urban-city. Intelligent transport systems are designed to keep the traffic under control and manage the traffic dynamically according to changing traffic conditions. The most important issue in intelligent transportation systems which consist of many sub-modules such as signaling systems, electronic monitoring systems, parking management systems, is the signalization of traffic lights. Traffic signalization has been studied in the literature on many times with different perspectives. On the other hand, data to be used in traffic signaling optimization are obtained by measurement devices such as detectors, cameras etc. in traditional systems. Since these systems can be easily affected by environmental factors, it has been seen that VANET architecture is preferred for data gathering from field to center in recent academic studies. In this study, we applied traffic signaling optimization with 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 VANET. It was aimed to show that decreasing in average waiting times of vehicles at intersections reduces the CO
₂
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 usage of computational technique in traffic signaling systems gives better results in dense and variable traffic conditions.