No abstract

Understanding the impact of various factors on train arrival delays is a prerequisite for effective railway traffic operating control and management. Existing studies analyze the train delay factors using a single, generic regression 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 analyzing train arrival delay factors at stations. We develop a seemingly unrelated regression equation (SURE) model to estimate the coefficients simultaneously while considering potential correlations between regression residuals caused by shared unobserved variables among equations. The railway data from 2017 to 2020 in Sweden are used to validate the proposed model and explore the effects of various factors on train arrival delays. The results confirm the necessity of developing a set of station-specific train arrival delay models to understand the heterogeneous impact of explanatory variables. The results show that the significant factors impacting train arrival delays are primarily train operations, including dwell times, running times, and operation delays from previous trains and upstream stations. The factors of the calendar, weather, and maintenance are also significant in impacting delays. Importantly, different train operating management strategies should be targeted at different stations since the impacts of these factors could vary depending on where the station is.

Railway traffic is growing, resulting in a highly interconnected train network. Due to the interdependence between trains’ activities, a better understanding of train passes and their effects can ensure dispatching decisions made have minimum risk of delays. The impacts of train pass on dwell time delays were investigated using historical Swedish railway operation data. Three scenarios were considered by combining the scheduled and actual operations: passes that happened as scheduled, unscheduled passes that happened in operation, and scheduled passes that were cancelled. A logistic regression model was used to explore the effects of these passes on delays. The findings show that train passes rarely occurred as scheduled, more frequently they are cancelled or unscheduled. This implies that some adjustments are required to assure the timetable’s feasibility. This study also found that the odds of delays for the cancelled pass was about 9.80 times lower than scheduled pass but 2.6 times more often for an unscheduled pass than a scheduled pass. The different types of train passes were quantified using an odds ratio to make comparisons easier for dispatching decision-making. The approach used in this study can be extended to other types of train movements, such as the meeting of trains, as well as other delay-influencing factors.

Real-time train arrival time prediction is crucial for providing passenger information and timely decision support. The paper develops methods to simultaneously predict train arrival times at downstream stations, including direct multiple output liner regression (DMOLR) and seemingly unrelated regression (SUR) models. To capture correlations of prediction equations, two bias correction terms are tested: (1) one-step prior prediction error and (2) upstream prediction errors. The models are validated on highspeed trains operation data along the Swedish Southern Mainline from 2016 to 2020. The results show that the DMOLR model slightly outperforms the SUR. The DMOLR's prediction performance improves up to 0.32% and 24.03% in term of RMSE and R 2 respectively when upstream prediction errors are considered.