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English(EN) Effects of Social Interactions in Self-Organising Railway Traffic Management

短时间范围可改善人工智能驱动的铁路交通管理

一篇新论文探讨了“预测性邻域范围”如何影响自组织铁路交通管理系统。该参数决定了哪些列车之间会协商交通计划。研究发现,与人们直觉认为更长范围会更好相反,更短的范围足以实现最优的全局调度一致性。在这些安全关键环境中,更短的范围还能提高局部可处理性和计算响应能力。 AI

排序理由 该集群包含一篇详细介绍人工智能驱动交通管理研究结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

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  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Vito Trianni ·

    Effects of Social Interactions in Self-Organising Railway Traffic Management

    Recent research is exploring self-organised traffic management as a solution for scaling to complex real-world networks. In such a system, trains predict their neighbourhood, produce traffic plan hypotheses, and agree via consensus with neighbours on a future traffic plan to be i…