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Switches and crossings are prone to failure thus making them drivers of unreliability in the operation of many railway systems. Unreliability is directly linked to punctuality which continues to limit, among other factors, the uptake of railways as a sustainable mode of transport. To fix this issue, there is a need for a better understanding of the contribution of railway sub-systems to train delays. Therefore, this paper seeks to quantify the impact of switch faults (switch failures and switch inspection warnings) on the delay of a train, using historical train operation and maintenance data. We have investigated the increased chance of a train being delayed in the event of a switch fault and the average size of delay that can be attributed to a switch fault. We find that 1) in the event of a switch failure trains have a 1% increased chance of experiencing a dwell delay, a 0.7% increased chance of experiencing an arrival delay, and a 0.1% increased chance of experiencing a departure delay for non-stopping trains; 2) the impact of switch inspection warnings on train delays is negligible; and 3) on average, a train is delayed by six extra minutes in the event of a switch failure. This study serves as a step towards quantifying primary delays and their origin in the railway system. Understanding the sources serves the purpose of informing decision-makers on the areas for prioritization during operations planning. Understanding the source and magnitude of primary delays is also important for simulation.

Understanding the impact of various factors on train arrival delays is a prerequisite for effective railway traffic operating control and management. Existing studies model the train delay prediction problems using a single, generic equation, restricting their capability in accounting for heterogeneous impacts of spatiotemporal factors on arrival delays as the train travels along its route. The paper proposes a set of equations conditional on the train location for predicting train arrival delays at stations. We develop a seemingly unrelated regression equation (SURE) model to estimate the coefficients simultaneously while considering potential correlations between prediction residuals caused by shared unobserved variables (e.g., driver characteristics). The operational data for high-speed trains on Sweden's Southern Mainline from 2016 to 2020 is used to validate the proposed model and explore the effects of operation-related factors on train arrival delays. The results confirm the necessity of developing a set of station-specific delay prediction models to understand the heterogeneous impact of explanatory variables, and SURE provides more efficient parameter estimations than the traditional ordinary least squares regression (OLS). The important factors impacting train arrival delays include the scheduled and actual running time, scheduled dwell time, and train arrival delays at preceding stations. However, the impact of these factors could vary depending on where the station is, and different types of operating management strategies should be targeted.