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

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