Congestion in a transportation network is usually the result of either an increase in traffic demand, i.e. the desire of drivers to use the transportation network, or a decrease in traffic supply, i.e. the traffic capacity, which is affected by weather conditions, incidents, etc. In either case, congestion reduces the efficiency of the transportation network and increases the travel time of vehicles in the network. In this paper, we leverage the benefits that Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications provide in Intelligent Transportation Systems (ITS), to optimize traffic assignment in transportation networks. In particular, we formulate a convex optimization problem for a transportation network and minimize network criticality, a new graph metric that measures centrality. The robustness of this solution is studied and compared to that of the System Optimal Equilibrium (SOE) Optimization. The results show that using network criticality provides robustness (lack of sensitivity) to both increases in traffic demand and decreases in traffic supply, thus reducing traffic congestion.