Forecasting Railway Network Resilience under Propagating Uncertainty

The resilience of railway networks in the face of weather-related disruptions is a critical concern, particularly with the increasing frequency of extreme weather events. Local failures not only cause direct impacts but also propagate across the network, amplifying uncertainty and increasing risks to journey connectivity, as well as the flow of passengers and goods. This study addresses the challenge of forecasting network resilience under the propagating uncertainty caused by weather conditions and asset failures. <br/><br/>To tackle this, we developed a computational model that quantifies the resilience of the railway network in the UK by calculating incident probabilities and simulating the propagation of uncertainties arising from weather events and asset vulnerabilities. The model utilises historical weather data and disruption records to estimate the failure probability of key railway assets such as rail tracks, overhead lines and lineside vegetation. It combines regression analysis with a Bayesian inference framework to conduct probabilistic assessments and generate a risk map that visualises how local failures impact broader network performance. This map, alongside a software tool, will be deployed on the UK’s DAFNI (Data &amp; Analytics Facility for National Infrastructure) platform to provide railway operators with better-informed decision-support tools for routing, scheduling and alternative service provision during disruptions. <br/><br/>The study provides short-term predictive capabilities, offering insights into potential disruptions within a specific timeframe (e.g. hours, days or up to two weeks) in advance. By leveraging data sharing and integration, adhering to FAIR (Findability, Accessibility, Interoperability and Reusability) principles, and employing advanced data analytics, the modelling tool and comprehensive risk map will enhance the understanding of operational resilience and support strategic planning for a more reliable and adaptable railway network. Ultimately, the study contributes to safeguarding transport networks against the growing challenges posed by climate change, improving the reliability and accessibility of railway services across the UK.<br/>