## 2 Methodology
### Overview of Data  
The first dataset comprises the trackwork records from the track utilisation plan, detailing 225,507 instances of scheduled trackwork. Each record provides specific information about the scheduled time, location, and the restrictions imposed on train traffic due to maintenance. Our study focused on basic maintenance trackwork, which is characterised by the absence of full track closures and a duration of less than 24 hours. In the track utilisation plan, locations of trackwork are identified by unique signal numbers situated along the track segments that span between two designated stations, marked as Ss and Se in Figure 1. Out of the 225,507 trackwork activities listed for 2017, we identified 3218 distinct track segments, which may include up to nine intermediary stations. Within these segments, the plan records a set of smaller trackwork that is performed at the same time in the same area. To streamline our dataset, we merged overlapping activities into single records, thereby eliminating duplication and simplifying the dataset for analysis. As a result, adjacent trackwork events, such as those depicted in Figure 1 as Ss.1-Sn.1 and Sn.2-Se.2, were combined into consolidated entries, labelled as trackwork 1-2 in the figure. The second dataset comprises the train punctuality data, extracted from the train plan 2017. This dataset provides information about the scheduled departure/arrival time and actual departure/arrival time to each station on the assigned train path, with a time precision of one minute. It includes specific details for each train route, such as a unique identification number, the type of train, and the type of track (whether single, double, or quadruple). In total, this dataset captures 32,591,482 train observations (Figure 2). Each recorded train passage is captured as a sequence of stations along its route, providing a precise geographical profile compared to the trackwork dataset (Figure 1). To integrate the datasets, we matched each unique journey in the punctuality records with corresponding  
Figure 1: Railway track segment where trackwork happens between stations S1 and Sn. track segments between the start (Ss) and end (Se) stations on the route. Given that trains traversed numerous segments or bypassed them entirely on their routes, 32.6 million recorded journeys throughout 3218 designated segments comprised roughly 27.2 million distinct train passages (Figure 2). Following this, we prepared the datasets for analysis with two regression models: multiple logistic regression and negative binomial regression. For the logistic regression, we defined two additional variables to capture both the presence and absence of train running time (runtime) delay increases, without altering the overall number of observations (Figure 2). In contrast, for the negative binomial regression, we aggregated the data based on a unique mix of train type, track type, trackwork, train entry status, daytime, and location. We then grouped the dataset with three new variables to quantify the counts of train running time delay increases, decreases, and instances where delays remained constant. Table 1 shows a summary statistic of trackwork duration and train delay size. On average, the trackwork lasted for 181 min but had a large range and a standard deviation of 207 min. The running time delay was calculated as a difference between the scheduled and actual train running times between analysed stations. The measurements were conducted with a precision of up to 1 min. The mean value of the observed train running time delays is -0.15 min, and a standard deviation of 5. The range of delay times spans substantially, with the earliest arrivals recorded at minus 444 min, and the maximum value 1447 min. Figure 2: Data processing workflow for train punctuality and trackwork analysis.