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.

翻译：面向智能交通系统的数字孪生技术当前备受关注，其中在仿真环境中生成真实、多样且类人的交通流是一项艰巨挑战。现有方法多依赖预定义驾驶员模型、目标优化或预录驾驶数据集，导致其在可扩展性、通用性和适应性方面存在局限。本文提出TrafficMCTS创新框架，通过融合基于群体的蒙特卡洛树搜索（MCTS）与社会价值取向（SVO），生成兼具多样驾驶风格与合作倾向的复合交通流。该框架以闭环架构为核心，使车辆能够实时动态适应环境，并确保可行的无碰撞轨迹。通过与前沿方法的全面对比，我们从计算效率、规划成功率、意图完成时间及多样性指标等方面凸显本方法的优势。此外，通过对高速公路与环岛场景的仿真验证，展示了所提框架的有效性及其在交通流中激发多样化社会行为的能力。最后，通过在大规模路网中同步调度海量车辆，验证了TrafficMCTS在再现人类行为复杂性的交通流场景中的可扩展性。