In this study, a virtual traffic system has been developed by establishing a brand-new Cellular Automata (CA) traffic model to figure out adequate strategies that can be implemented in actual traffic systems to optimize road capability. We investigate the flow efficiency and social dilemma that emerged due to the defector drivers in a traffic flow system, who are highly aggressive in driving and impose threatening/pushing effects on their preceding while they are tailgating. The evolutionary game theory, which is one of the most efficient tools in the decision-making process, has been utilized to identify the Social Efficiency Deficit (SED), which means the dilemma strength of those games. We introduced a new lane-changing protocol for the preceding vehicle, considering the threatening effects given by the aggressive follower. This investigation explored several case studies defining various strategies for cofactors and defectors. We conducted a series of multi-agent simulations on this traffic flow system and experienced the Prisoner’s Dilemma (PD) and the Quasi-Prisoner’s Dilemma game with diverse dilemma strengths for four different strategies for cooperators and defectors.