用强化学习模拟智能货运走廊，让货车自动组队充电，提升通行效率。
Simulating Cognitive Smart Freight Corridors with Agent-Based Models and Reinforcement Learning
- 构建三层次框架：基础设施+车联网+强化学习决策
- 智能场景下通行量提升，拥堵降低，每公里能耗减少
- 多智能体强化学习比规则分配更高效利用充电桩
智能货运走廊为货运领域自动驾驶车辆的部署提供了可行路径，但实体实验成本高昂，且现有方法依赖预设控制策略，难以捕捉自适应行为。本文提出一种融合物理基础设施层、车联网（V2X）层与集成强化学习（RL）和多智能体强化学习（MARL）的决策层的基于代理的建模（ABM）框架，用于编队形成与充电协调。通过吞吐量、拥堵、能耗、排放及鲁棒性等指标评估三种情景（基线、辅助、认知）。初步结果显示，认知场景在通行量和拥堵控制上优于基线，而辅助场景通过编队实现每公里显著的能耗节省。敏感性分析表明，在高需求条件下，智能走廊的通行优势进一步扩大；且MARL协调方式相比规则分配，能更高效利用固定充电资源。
原文摘要 · Abstract (English)
Smart freight corridors offer a practical pathway for connected and automated vehicle (CAV) deployment in freight transportation, but physical experimentation is expensive and existing approaches rely on predefined control policies that cannot capture adaptive behaviors. This paper presents an agent-based modeling (ABM) framework coupling a physical infrastructure layer, a connectivity layer (V2X), and a decision layer integrating reinforcement learning (RL) and multi-agent reinforcement learning (MARL) for platoon formation and charging coordination. We evaluate three scenarios (Baseline, Assisted, and Cognitive) using throughput, congestion, energy, emissions, and robustness metrics. Preliminary results indicate that the Cognitive scenario achieves higher throughput and lower congestion than the baseline, while the Assisted scenario delivers meaningful energy savings per kilometer through platooning. Sensitivity analysis shows that the throughput advantage of the smart corridor widens under conditions with high demand and that MARL coordination extracts greater utilization from fixed charging capacity than rule-based assignment.
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