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：数字孪生技术在智能交通系统中日益受到关注，其中在模拟中生成真实、多样且类人化的交通流是一项艰巨挑战。现有方法通常依赖于预定义驾驶员模型、目标优化或预录驾驶数据集，限制了其可扩展性、通用性和适应性。本文提出TrafficMCTS这一创新框架，它利用群体蒙特卡洛树搜索（MCTS）与社会价值取向（SVO）的协同作用，生成蕴含多样化驾驶风格与合作倾向的多层面交通流。该框架以闭环架构为支撑，使车辆能够实时动态适应环境，并确保无碰撞可行轨迹的可行性。通过与前沿方法的全面对比，我们在计算效率、规划成功率、意图完成时间及多样性指标方面凸显了本方法的优势。此外，我们模拟了高速公路与环岛场景以验证所提框架的有效性，并强调其在交通流中诱导多样化社会行为的能力。最后，我们通过展示TrafficMCTS在大规模道路网络中同时操控大量车辆的能力，验证了其可扩展性，从而塑造出能够反映人类行为复杂性的交通流场景。