Improving traffic management in case of perturbation is one of the main challenges in today's railway research. The great majority of the existing literature proposes approaches to make centralized decisions to minimize delay propagation. In this paper, we propose a new paradigm to the same aim: we design and implement a modular process to allow trains to self-organize. This process consists in having trains identifying their neighbors, formulating traffic management hypotheses, checking their compatibility and selecting the best ones through a consensus mechanism. Finally, these hypotheses are merged into a directly applicable traffic plan. In a thorough experimental analysis on a portion of the Italian network, we compare the results of self-organization with those of a state-of-the-art centralized approach. In particular, we make this comparison mimicking a realistic deployment thanks to a closed-loop framework including a microscopic railway simulator. The results indicate that self-organization achieves better results than the centralized algorithm, specifically thanks to the definition and exploitation of the instance decomposition allowed by the proposed approach.

翻译：在扰动情况下改进交通管理是当今铁路研究的主要挑战之一。现有文献绝大多数提出了通过集中决策来最小化延误传播的方法。本文针对同一目标提出了一种新范式：我们设计并实现了一种模块化流程，使列车能够自组织。该流程包括让列车识别其相邻列车、制定交通管理假设、检查这些假设的兼容性，并通过共识机制选择最优假设。最终，这些假设被合并为可直接应用的交通计划。通过对意大利铁路网某段进行的详尽实验分析，我们将自组织方法与最先进的集中式方法的结果进行了比较。特别地，我们借助包含微观铁路模拟器的闭环框架模拟实际部署场景进行对比。结果表明，自组织方法取得了优于集中式算法的效果，这主要得益于所提方法允许的实例分解定义与利用。