  Digital twins for intelligent transportation systems are currently attracting
great interests, in which generating realistic, diverse, and human-like traffic
flow in simulations is a formidable challenge. 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 groupbased Monte Carlo tree
search (MCTS) and Social Value Orientation (SVO) to engender a multifaceted
traffic flow replete 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, intent completion time, and
diversity metrics. Besides, we simulate highway and roundabout 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 showcasing its prowess in
simultaneously mass vehicles within a sprawling road network, cultivating a
landscape of traffic flow that mirrors the intricacies of human behavior.
