Abstract:

To address the difficulty in multi-scale collaborative modeling and the insufficient interaction continuity in traffic simulation systems， a multi-scale micro-meso-macro integrated traffic simulation framework was proposed. In the framework， with a core logic of data-driven processing， scale classification， policy execution， trajectory control， simulation feedback， a modular modeling architecture was constructed for multi-source information fusion and behavior policy decoupling； by establishing a mesoscale bridging layer， the bidirectional information propagation between micro behaviors and macro trends was achieved， and a Social Value Orientation （SVO） swarm Monte Carlo Tree Search （MCTS） cooperative decision-making mechanism was incorporated. At the same time， at the macroscopic level， a dynamic OD （Origin-Destination） matrix generation module was constructed to empower the simulation system with adaptive control capabilities to the temporal evolution of traffic flow. Experimental results demonstrate that the proposed method achieves substantial improvements in both behavioral rationality and scenario adaptability of the system while maintaining simulation efficiency. Compared with the traditional single-scale models and single-vehicle decision models without collaborative strategies， the method reduces the conflict rate by 19.8% on average， and increases the Throughput Ratio （TR） by 34.6% comprehensively， providing an effective technical path for intelligent coordination and resource optimization in complex traffic scenarios.

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

摘要：

针对交通仿真系统中多尺度协同建模困难与交互连续性不足的问题，设计一种融合微观、介观与宏观的多尺度交通仿真方法。该方法以数据驱动?尺度分类?策略执行?轨迹控制?仿真反馈为核心逻辑，构建模块化建模体系，实现多源信息融合与行为策略解耦。通过建立介观桥梁层，打通微观行为与宏观趋势之间的双向信息通路，并融入基于社会价值导向（SVO）的群体蒙特卡洛树搜索（MCTS）协同决策机制；同时，在宏观层构建动态OD（Origin-Destination）矩阵生成系统，赋予仿真系统对交通流时序演变的自适应调控能力。实验结果表明，本文方法在保证仿真效率的同时，显著提升系统行为合理性与场景适应力，与传统的单一尺度模型和无协同策略的单车决策模型相比，冲突率平均降低了19.8%、通行率（TR）综合提升了34.6%，为复杂交通场景下的智能协同与资源优化提供了有效的技术路径。

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