发表机构
University of Tennessee, Knoxville; Concordia University; Amazon.com, Inc.（田纳西大学诺克斯维尔分校; 康考迪亚大学; 亚马逊公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出耦合多技术层的ABM框架，结合RL与MARL模拟认知智能货运走廊，实验表明其在吞吐量、拥堵、节能等方面优于基准场景，高需求下优势更显著。
AI 中文摘要
智能货运走廊为货运交通领域的网联自动驾驶车辆（CAV）部署提供了可行路径，但实体实验成本高昂，且现有方法依赖预定义控制策略，无法捕捉自适应行为。本文提出一种智能体建模（ABM）框架，耦合物理基础设施层、车万物联网（V2X）连接层，以及集成强化学习（RL）与多智能体强化学习（MARL）的决策层，用于车队编组与充电协调。我们基于吞吐量、拥堵、能源、排放和鲁棒性指标评估三种场景：基准场景、辅助场景和认知场景。初步结果表明，认知场景较基准场景实现更高吞吐量与更低拥堵，辅助场景通过车队编组实现每公里显著节能。敏感性分析显示，高需求条件下智能走廊的吞吐量优势会扩大，且MARL协调相比基于规则的分配能从固定充电容量中提取更高利用率。
英文摘要
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.
Comments11 pages, 5 figures
