Traffic signal control, which is an integral part of Intelligent Transportation System (ITS), plays an important role in regulating vehicular flow at road intersections. With the increase of vehicular traffic, there has been a significant degradation in the functional efficiency of signal systems. Traditional systems are not capable of adjusting the timing pattern in accordance with vehicular demand. This results in excessive delays for road users. Hence it is necessary to develop dynamic systems that can adjust the timing patterns according to traffic demand. Of the various available techniques, Vehicular Adhoc Networks (VANETs) are attracting considerable attention from the research community and the automotive industry to implement dynamic systems. In this context, exchanging data among vehicles is one of the key technological enablers through which the density of vehicles approaching the intersection can be predicted. This requires extensive collaboration between vehicles. Inherent properties and limitations of VANETs, distributing information among the vehicles is a very challenging task. In this thesis, an adaptive traffic signal control system based on car-to-car communication (VANETs) is proposed. To achieve this, a data dissemination technique titled, Clustering in DiRectIon in Vehicular Environment (C-DRIVE) is implemented. In C-DRIVE, the formation of clusters is based on the direction metric. Precisely this metric defines the direction a vehicle will travel after crossing the intersection. To attain stability within the clusters and to have accurate estimation of the density of vehicles, two policies are adapted. In the first policy, a clusterhead switching mechanism is defined. In the second method, termed as Modified C-DRIVE (MC-DRIVE), the clusterhead election policy is modified. In this modification the election policy is based on the stable cluster length. Once the clusters are formed, the elected cluster head will compute the density in its clusters and transmits the information to the traffic signal controller (TSC). With the density information of different lanes approaching the intersection, at the TSC an optimal cycle length is computed using the modified Webster's model and based on the demand, required green splits are allotted to the various phase. The efficiency of this method is advocated through simulation results which show that the waiting time for vehicles and queue length at intersections are considerably reduced. It is also shown that the proposed solution is collision free at intersections. The proposed system is compared with a classic pre-timed system and an adaptive fuzzy logic system. The simulations also show that the data convergence time and the communication delay between vehicles and traffic signals do not compromise the efficiency of the system.