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Digital twins for intelligent transportation systems are currently attractinggreat interests, in which generating realistic, diverse, and human-like trafficflow in simulations is a formidable challenge. Current approaches often hingeon predefined driver models, objective optimization, or reliance onpre-recorded driving datasets, imposing limitations on their scalability,versatility, and adaptability. In this paper, we introduce TrafficMCTS, aninnovative framework that harnesses the synergy of groupbased Monte Carlo treesearch (MCTS) and Social Value Orientation (SVO) to engender a multifacetedtraffic flow replete with varying driving styles and cooperative tendencies.Anchored by a closed-loop architecture, our framework enables vehicles todynamically adapt to their environment in real time, and ensure feasiblecollision-free trajectories. Through comprehensive comparisons withstate-of-the-art methods, we illuminate the advantages of our approach in termsof computational efficiency, planning success rate, intent completion time, anddiversity metrics. Besides, we simulate highway and roundabout scenarios toillustrate the effectiveness of the proposed framework and highlight itsability to induce diverse social behaviors within the traffic flow. Finally, wevalidate the scalability of TrafficMCTS by showcasing its prowess insimultaneously mass vehicles within a sprawling road network, cultivating alandscape of traffic flow that mirrors the intricacies of human behavior.

TrafficMCTS: A Closed-Loop Traffic Flow Generation Framework with Group-Based Monte Carlo Tree Search