The real-time Railway Traffic Management Problem (rtRTMP) is a challenging optimisation problem in railway transportation. It involves the efficient management of train movements while minimising delay propagation caused by unforeseen perturbations due to, e.g, temporary speed limitations or signal failures. This paper re-frames the rtRTMP as a multi-agent coordination problem and formalises it as a Distributed Constraint Optimisation Problem (DCOP) to explore its potential for decentralised solutions. We propose a novel coordination algorithm that extends the widely known Distributed Stochastic Algorithm (DSA), allowing trains to self-organise and resolve scheduling conflicts. The performance of our algorithm is compared to a classical DSA through extensive simulations on a synthetic dataset reproducing diverse problem configurations. Results show that our approach achieves significant improvements in solution quality and convergence speed, demonstrating its effectiveness and scalability in managing large-scale railway networks. Beyond the railway domain, this framework can have broader applicability in autonomous systems, such as self-driving vehicles or inter-satellite coordination.

翻译：实时铁路交通管理问题（rtRTMP）是铁路运输领域一个具有挑战性的优化问题。其核心在于高效管理列车运行，同时最小化由临时限速或信号故障等突发扰动引起的延误传播。本文将rtRTMP重新构建为一个多智能体协调问题，并将其形式化为分布式约束优化问题（DCOP），以探索其分散式解决方案的潜力。我们提出了一种新颖的协调算法，该算法扩展了广为人知的分布式随机算法（DSA），使列车能够自组织地解决调度冲突。通过在再现多种问题配置的合成数据集上进行大量仿真，我们将所提算法的性能与经典DSA进行了比较。结果表明，我们的方法在解的质量和收敛速度方面均取得了显著提升，证明了其在管理大规模铁路网络时的有效性和可扩展性。除铁路领域外，该框架在自动驾驶车辆或卫星间协调等自主系统中也具有更广泛的适用性。