Key Performance Indicators, such as station overruns and delay minutes, are used to assess the performance and punctuality of the GB railway. They can be used to quantify the effects of low adhesion, but the majority of previous analysis has been constrained to the autumn season. A Python script has been created in this work to extract 11 years of detailed passenger and freight station overrun data, throughout the entire year. The information gathered includes time and date, location, direction, vehicle type, railhead conditions and subsequent delay minutes caused by the incident. Although the majority of low adhesion related overruns occur in the autumn season due to leaf fall, this work has highlighted the number of low adhesion related issues that occur throughout the year where there are no visible signs of contamination. This work gives an overview of this new dataset and looks at some key trends in the data but the granular detail available means that future case studies could be carried out in specific locations, linked to geographic and meteorological data, to assess when and why low adhesion is occurring. From an operational perspective, the dataset could then be used as a daily updated assessment of the effectiveness of low adhesion mitigation methods.

诸如车站超限和延误分钟等关键绩效指标用于评估 GB 铁路的绩效和准时性。它们可用于量化低附着力的影响，但之前的大多数分析都局限于秋季。在这项工作中创建了一个 Python 脚本，以提取 11 年的详细客运和货运站超限数据，贯穿全年。收集的信息包括时间和日期、位置、方向、车辆类型、铁路头状况以及事故造成的后续延误时间。尽管由于落叶而导致的大多数低附着力相关溢出发生在秋季，但这项工作突出了全年发生的低附着力相关问题的数量，这些问题没有明显的污染迹象。这项工作概述了这个新数据集并着眼于数据中的一些关键趋势，但可用的详细信息意味着未来的案例研究可以在特定地点进行，与地理和气象数据相关联，以评估何时以及为何低粘附性正在发生。从操作的角度来看，该数据集可以用作对低附着力缓解方法有效性的每日更新评估。