用网络拓扑特征预测荷兰铁路延误，探索缓解连锁故障的新方法。
Predicting Delayed Trajectories Using Network Features: A Study on the Dutch Railway Network
- 基于节点中心性等网络特征，用XGBoost预测列车延误路径。
- 非同步测试下模型表现有限，说明需更适配铁路场景的改进。
- 为交通网络延迟预测提供新思路，适合铁路运维与规划研究者。
荷兰铁路网络是全球最繁忙的之一，延误问题对主要运营商NS构成重大挑战。现有研究多聚焦短期预测，忽视影响连锁效应的全局网络模式。本文采用XGBoost分类器，结合节点中心性等拓扑特征，将原本用于预测美国航空网络动态变化的方法改进并应用于荷兰铁路系统，以预测列车延误轨迹。通过对比RandomForest、DecisionTree、GradientBoosting、AdaBoost及LogisticRegression等多种分类器，验证模型性能。结果显示，在非同步测试条件下，模型表现有限，表明当前方法需进一步适配铁路网络特性。尽管如此，本研究推动了对交通网络评估的理解，并为构建更鲁棒的延误预测模型指明了未来方向。
原文摘要 · Abstract (English)
The Dutch railway network is one of the busiest in the world, with delays being a prominent concern for the principal passenger railway operator NS. This research addresses a gap in delay prediction studies within the Dutch railway network by employing an XGBoost Classifier with a focus on topological features. Current research predominantly emphasizes short-term predictions and neglects the broader network-wide patterns essential for mitigating ripple effects. This research implements and improves an existing methodology, originally designed to forecast the evolution of the fast-changing US air network, to predict delays in the Dutch Railways. By integrating Node Centrality Measures and comparing multiple classifiers like RandomForest, DecisionTree, GradientBoosting, AdaBoost, and LogisticRegression, the goal is to predict delayed trajectories. However, the results reveal limited performance, especially in non-simultaneous testing scenarios, suggesting the necessity for more context-specific adaptations. Regardless, this research contributes to the understanding of transportation network evaluation and proposes future directions for developing more robust predictive models for delays.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。