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：应对扰动的交通管理优化是当前铁路研究的主要挑战之一。现有文献绝大多数提出集中式决策方法以最小化延误传播。本文针对同一目标提出新范式：设计并实现了一种模块化流程，使列车能够实现自组织。该流程包含列车识别邻近列车、制定交通管理假设、检验假设兼容性，并通过共识机制筛选最优方案。最终将这些假设整合为可直接执行的交通计划。通过在意大利铁路网某区段进行的深入实验分析，我们将自组织方法与先进集中式方法的结果进行对比。特别地，我们借助包含微观铁路仿真器的闭环框架模拟了实际部署场景。结果表明，自组织方法较集中式算法表现更优，这主要得益于所提方法对问题实例进行分解定义与利用的特性。