In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies, such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication and automated driving capability. As the world of transportation gets more and more connected through these technologies, the need to implement algorithms with V2I capability is amplified. In this paper, an algorithm called Pass at Green, utilizing V2I and vehicle longitudinal automation to modify the speed profile of a mid-size generic vehicle to decrease fuel consumption has been studied. Pass at Green (PaG) uses Signal Phase and Timing (SPaT) information acquired from upcoming traffic lights, which are the current phase of the upcoming traffic light and remaining time that the phase stays active. Then, PaG modifies the speed of the vehicle by accelerating, keeping its speed constant or decelerating to decrease fuel consumption, minimize idling time and reduce the likelihood of catching a red light in an intersection. As presented in this paper, the fuel economy benefit achieved by the PaG algorithm was studied through Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) simulations in CarSim, as well as traffic simulations in a commercial microscopic simulator called Vissim. Traffic simulations in Vissim to further test the performance of the PaG model had low, medium, high and no traffic scenarios. Fuel economy performance of the PaG algorithm was then compared to a modified Intelligent Driver Model (IDM) and a fuel optimal speed profile obtained by using Dynamic Programming.