Practical predictive model for evaluating performance and delay propagation in multi-track railways during disruptions

Numerous studies have explored modelling approaches for managing disruption in rail networks, yet the examples of practical applications remain somewhat limited. This paper introduces a real-time, practical predictive modelling approach to simulate rail network operations during disruptions. In the United Kingdom, the rail network is highly congested, with the train services closely interconnected. As a result, disruptions can easily propagate the effects of delays across the entire network. This approach aims to simulate the propagation of delay, analyse network interdependencies, and predict rail network performance during disruptions. The results indicate a good match between the in-field data and the results of the simulation, illustrating the potential of the model to produce accurate estimations. This model has the potential to serve as a real-time advisory tool for managing disruptions in a highly congested rail network, swiftly producing reliable estimates of the outcomes for different service alterations before introduction.