发表机构
Univ Gustave Eiffel; ESTIN（古斯塔夫·埃菲尔大学; ESTIN 高等技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对部分网联车辆环境下匝道信号控制问题，提出结合宏观与微观观测的混合表示及决斗双深度Q网络方法，在SUMO中相比ALINEA显著降低出行时间和溢流持续时间。
AI 中文摘要
高速公路入口匝道汇入是拥堵的主要来源，造成巨大的经济和环境成本。虽然深度强化学习（DRL）为匝道信号控制提供了有前景的解决方案，但现有方法主要依赖聚合的宏观数据。网联车辆（CVs）提供车辆级观测，可补充聚合交通测量，但其有限的渗透率导致微观信息不完整。本文提出一种混合观测表示，结合宏观交通测量与双通道网格编码观测到的网联车辆存在性和速度。采用决斗双深度Q网络处理这些输入以选择匝道绿灯时长。控制器在不同交通需求和网联车辆渗透率下训练，并在SUMO中与ALINEA及仅宏观DRL变体进行评估。在50个匹配的评估场景中，部分网联车辆可见性下的混合控制器相比ALINEA，报告的总出行时间减少11.4%，平均溢流持续时间减少84.9%。在完全网联车辆可见性下评估同一训练策略，进一步减少约1.6%的出行时间。跨渗透率分析表明，随着微观观测更完整，性能差距减小。这些结果支持在基于学习的匝道信号控制中结合互补的宏观和稀疏微观观测。模型源代码实现可在以下网址获取：此https URL
英文摘要
Freeway on-ramp merges are major sources of congestion, causing significant economic and environmental costs. While Deep Reinforcement Learning (DRL) offers a promising solution for ramp metering, existing approaches rely primarily on aggregated macroscopic data. Connected vehicles (CVs) provide vehicle-level observations that can complement aggregate traffic measurements, but their limited penetration produces incomplete microscopic information. This paper proposes a hybrid observation representation combining macroscopic traffic measurements with a two-channel grid encoding observed CV presence and speed. A Dueling Double Deep Q-Network processes these inputs to select ramp-metering green durations. The controller is trained under varying traffic demands and CV penetration rates and evaluated against ALINEA and macroscopic-only DRL variants in SUMO. Across 50 matched evaluation scenarios, the hybrid controller under partial CV visibility reduces the reported total travel time by 11.4 % and mean spillback duration by 84.9 % relative to ALINEA. Evaluating the same trained policy with full CV visibility yields a further travel-time reduction of approximately 1.6 %. Analysis across penetration rates suggests that the performance gap decreases as microscopic observations become more complete. These results support the use of complementary macroscopic and sparse microscopic observations for learning-based ramp metering. The source code implementation of the model is available at: https://github.com/youcefMehamlia/Multimodal-DRL-RMC
