The world’s largest coal export operation is located in New South Wales, Australia. The state has more than 87% of the coal transportation done through railways, and one of the strategies to increase throughput is the use of sophisticated computational techniques for rail traffic optimisation. The current state of the art shows a lack of practical applications, thus making scalability, decentralisation and real-world commitment three key research directions. Towards that, this research presents a simulation-based machine learning approach for the railway traffic management problem, in the context of the Hunter Valley Coal Chain (HVCC). We modelled trains, load points and terminals as autonomous intelligent agents that interact, learn and act independently—thus constituting a multi-agent system (MAS). The MAS is implemented on top of a rail network simulation model currently in use at the HVCC. The model is adapted as a decentralised partially-observed Markov decision process environment that allows multi-agent learning via a genetic algorithm. We present experiments with scenarios based on the actual rail network data, which show that the MAS outperforms the heuristic approach embedded in the HVCC simulation tool by up to 81% (in terms of the schedule’s total dwell time). Further to those experiments, a comparison analysis evaluates the relevance of specific state features (e.g. track length, train conflicts, etc.). Finally, an important outcome was that the agents have learned to overcome very complex traffic situations that appear in train scheduling operations and that sometimes result in unnecessarily long dwell times. This type of high level learning represents a significant step forward in the use of complex computational techniques for rail transportation problems.

世界上最大的煤炭出口业务位于澳大利亚新南威尔士州。该州 87% 以上的煤炭运输通过铁路完成，提高吞吐量的策略之一是使用复杂的计算技术来优化铁路交通。目前的技术水平表明缺乏实际应用，因此使可扩展性、去中心化和现实世界承诺成为三个关键研究方向。为此，本研究在猎人谷煤炭链 (HVCC) 的背景下，针对铁路交通管理问题提出了一种基于模拟的机器学习方法。我们将火车、装载点和终端建模为独立交互、学习和行动的自主智能代理——从而构成一个多代理系统 (MAS)。MAS 是在 HVCC 当前使用的铁路网络仿真模型之上实施的。该模型适用于分散的部分观察马尔可夫决策过程环境，允许通过遗传算法进行多代理学习。我们展示了基于实际铁路网络数据的场景实验，结果表明 MAS 优于嵌入在 HVCC 模拟工具中的启发式方法高达 81%（就计划的总停留时间而言）。除了这些实验之外，比较分析评估了特定状态特征（例如轨道长度、列车冲突等）的相关性。最后，一个重要的结果是代理已经学会了克服列车调度操作中出现的非常复杂的交通情况，有时会导致不必要的长时间停留。