关键词: 多尺度, 群体蒙特卡洛树搜索协同策略, 微观轨迹规划, 时变OD生成机制, 多智能体

Abstract: To address the challenges of multi-scale collaborative modeling and insufficient interaction continuity in existing traffic simulation systems, this paper proposes a unified multi-scale traffic simulation framework integrating micro-, meso-, and macro-level modeling. The framework follows a core workflow of data-driven processing, scale classification, strategy execution, trajectory control, and simulation feedback, and establishes a modular architecture for multi-source information fusion and behavior-strategy decoupling.The framework establishes a mesoscale bridging layer that enables bidirectional state propagation between individual vehicle dynamics and network-level flow evolution, thereby mitigating scale discontinuities. A social-value–driven group Monte Carlo Tree Search (MCTS) mechanism is further incorporated to support cooperative multi-agent decision-making under heterogeneous interaction patterns. At the macroscopic level, a dynamic, time-activated Origin–Destination (OD) generation module is constructed to capture temporal variations in travel demand and to regulate system-wide flow input adaptively.Comprehensive experiments demonstrate that the proposed framework achieves substantial improvements in both behavioral plausibility and scenario adaptiveness while maintaining computational scalability. Compared with state-of-the-art baselines, the method reduces conflict occurrence by 19.8% and increases network throughput by 34.6%, highlighting its effectiveness as a robust modeling paradigm for intelligent traffic coordination and large-scale dynamic simulation. 

Key words: multi-scale, collective Monte Carlo Tree Search (MCTS) -based cooperative strategy, microscopic trajectory planning, time-varying Origin-Destination (OD) generation mechanism, multi-agent