Speed trajectories considerably influence vehicular fuel consumption, particularly on signalized roads. To minimize fuel consumption, sharp acceleration/deceleration maneuvers and idling events at signalized intersections should be prevented. By taking advantage of the technological developments in infrastructure-to-vehicle communication, the possibility of receiving traffic signal phase and timing information in advance is enabled. Although a vast amount of research has been dedicated to optimal speed trajectory planning, existing methods may not be adequate in identifying the optimal solution for vehicles driving on signalized roads. Most studies do not involve queue estimation in the algorithm, which makes it challenging to deploy these methods in practice. Moreover, research efforts focus on undersaturated traffic conditions where queues can completely dissolve in a single cycle. Once the network is oversaturated, residual queues are formed generating traffic fluctuations and complete stops, significantly reducing the effectiveness of the application. In this thesis, an optimal control problem is formulated to obtain the optimal speed trajectory, where traffic induced constraints are taken into account and queue estimation is explicitly integrated into the control framework. Based on kinematic wave theory, an efficient and accurate procedure to formulate the queue constraints in various traffic conditions is developed. To facilitate real-time control actions, the constrained optimization problem is solved using model predictive control. The simulation case studies show the proposed algorithm achieves vehicular fuel consumption savings as high as 29.15% compared to an existing approach in the literature. However, the fuel consumption savings are at the expense of an increase in travel time up to 1.65% compared to the literature approach. The results also indicate the benefits grow with increasing market penetration rates (MPRs) of controlled vehicles until it levels off at about 80% MPR. Furthermore, the results demonstrate the proposed algorithm can deal with stochasticity in traffic behavior. Finally, the thesis highlights the need for future research to further improve the proposed algorithm.