Heterogeneous Machine Learning Ensembles for Predicting Train Delays
Heterogeneous Machine Learning Ensembles for Predicting Train Delays
复制标题
DOI：
10.1109/tits.2023.3337858
复制
发表时间：
2024-06
影响因子：
8.5
通讯作者：
Mostafa Al Ghamdi;Gerard Parr;Wenjia Wang
中科院分区：
文献类型：
--
作者：
Mostafa Al Ghamdi;Gerard Parr;Wenjia Wang
火车延误一直是英国和许多其他国家持续存在的严重问题。由于需求不断增加，铁路网络已接近满负荷运行。因此，最初的延误可能会对其他列车造成许多连锁延误，这是铁路网络性能整体恶化的主要原因。因此，有一种基于人工智能的方法可以准确可靠地预测延误，帮助列车控制员在发生延误时及时制定和应用替代计划，以减少或防止进一步的延误，这是非常有用的。然而，现有的机器学习模型不仅不准确，更重要的是不可靠。在这项研究中，我们提出了一种构建异构集成的新方法，采用两种基于准确性和多样性的新颖模型选择方法。我们使用现实世界的数据测试了我们的异构集成，结果表明它们比单一模型和最先进的同质集成更准确和稳健。随机森林和 XGBoost。然后，我们使用来自不同列车运营公司的独立数据集验证了他们的表现，发现他们取得了一致且准确的结果。
Train delays have been a serious persisting problem in the UK and also many other countries. Due to increasing demand, rail networks are running close to their full capacity. As a consequence, an initial delay can cause many knock-on delays to other trains, and this is the main reason for the overall deterioration in the performance of the rail networks. Therefore, it is really useful to have an AI-based method that can predict delays accurately and reliably, to help train controllers to make and apply alternative plans in time to reduce or prevent further delays, when a delay occurs. However, existing machine learning models are not only inaccurate but more importantly unreliable. In this study, we have proposed a new approach to build heterogeneous ensembles with two novel model selection methods based on accuracy and diversity. We tested our heterogeneous ensembles using the real-world data and the results indicated that they are more accurate and robust than single models and state-of-the-art homogeneous ensembles, e.g. Random Forest and XGBoost. We then verified their performances with an independent dataset from a different train operating company and found that they achieved the consistent and accurate results.