Traffic flow simulation within the domain of intelligent transportation systems is garnering significant attention, and generating realistic, diverse, and human-like traffic patterns presents critical challenges that must be addressed. Current approaches often hinge on predefined driver models, objective optimization, or reliance on pre-recorded driving datasets, imposing limitations on their scalability, versatility, and adaptability. In this paper, we introduce TrafficMCTS, an innovative framework that harnesses the synergy of group-based Monte Carlo tree search (MCTS) and Social Value Orientation (SVO) to engender a multifaceted traffic flow with varying driving styles and cooperative tendencies. Anchored by a closed-loop architecture, our framework enables vehicles to dynamically adapt to their environment in real time, and ensure feasible collision-free trajectories. Through comprehensive comparisons with state-of-the-art methods, we illuminate the advantages of our approach in terms of computational efficiency, planning success rate, intention completion time, and diversity metrics. Besides, we simulate multiple scenarios to illustrate the effectiveness of the proposed framework and highlight its ability to induce diverse social behaviors within the traffic flow. Finally, we validate the scalability of TrafficMCTS by demonstrating its capability to efficiently simulate diverse traffic scenarios involving numerous interacting vehicles within a complex road network, capturing the intricate dynamics of human-like driving behaviors.