数据驱动的高速铁路列车运行调整理论

本书分析了高速列车运行数据的获取、筛选方法，研究基于高速铁路列车运行实绩，综合运用现代统计方法及模型、机器学习和深度学习方法、强化学习理论，在解析晚点分布规律的基础上重点研究数据驱动的列车晚点传播与恢复理论与方法，提出基于冗余时间运用效率及晚点恢复优选化的冗余时间优化布局方法，建立数据驱动的高速铁路列车运行调整理论。本书的主要内容包括：高速列车晚点的宏观分布规律；晚点横向、纵向传播的宏观规律和微观机理；数据驱动的高速列车晚点恢复模型；基于晚点恢复的冗余时间布局方法；本书提炼了团队基于列车运行实绩开展数据驱动高速铁路列车运行调整理论研究的近期新成果，介绍了数据科学在铁路运输组织优化领域的结合与应用，能够丰富我国铁路列车调度指挥理论与技术体系，具有较高的理论价值。本书可以作为现场调度员的参考学习用书，强化基于历史数据制定调度指挥策略、列车运行调整方法的理论和实践知识，将为调度员制定列车运行调整方案、行车组织预案及实施应急组织等提供一定指导。另外，实现高速列车晚点传播及恢复的预测，将能够为铁路部门资源运用、信息发布提供实时决策支持，为旅客出行提供更为精准的实时服务信息。

Chapter 1 Train dispatching management with data-driven approaches:A comprehensive review and appraisal
1.1 Introduction
1.2 Data-driven train dispatching
1.3Data-driven models in traindispatching:Literature
1.4Review results and further discussions
1.5Conclusions
Chapter 2 Data—driven delay distributions of HSR trains
2.1 Statistical Investigation on Train Primary Delay based on Real Records:Evidence from Wuhan-Guangzhou HSR
2.2 Statistical Delay Distribution Analysis on High-Speed Railway Trains
2.3 Temporal and SpatialDistributions of Primary Delays in a High-Speed RailSystem
Chapter 3 Data—driven delay propagation mechanism on horizontal
3.1 Cause-specific Investigation of Primary Delays of Wuhan-Guangzhou HSR
3.2 Modellingof Effects of Primary Delays Using High-speed Train Operation Records
3.3 Modelling the influence of disturbances for real-time train dispatching
Chapter 4 Data—driven delay propagation mechanism on vertical
4.1 A hybrid model to improve the train running time prediction ability during high-speed railway disruptions
4.2 A Spatiotemporal Deep Learning Framework for Train Delay Prediction in High-speed Railway Systems
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