This thesis project investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves traffic microscopic simulations for a mixed traffic environment of conventional and automated vehicles (AVs) both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is accessed for its potential to improve traffic efficiency in a full market penetration of connected AVs with absolute compliance. The project also aims to determine the possible outcome resulting from enhancing the AVs capabilities such as implementing short time headways between vehicles in the future.  The best traffic performance results achieved by operating GLOSA goes for connected AVs with the lowest simulated time headway (0.3 sec). The waiting time reduction reaches 95% and trip delay lessens to 88 %.