A data model for prediction of weather-related rail incidents: a case-study example of wind-related incidents on the rail network in Great Britain

The impacts of extreme weather events on railway operations are complex, and in the most severe cases can cause significant disruption to service, leading to delays for passengers and financial penalties to the industry. This paper presents a prototype data model with logistic regression analysis, which enables exploration of the underlying causal factors impacting on weather-related incidents on the rail network. The methodology is demonstrated using wind related delay data gathered from the Anglia Route of Great Britain’s rail network between financial year 2006/07 and 2014/15. The work presented draws on a diverse group of data resources, including climatic, geographical, and vegetation data sets, in order to include a wide range of potential contributing factors in the initial analysis. It investigates ways in which these data may be used to predict when and where wind-related disruptions will occur, thus enabling us to gain a deeper understanding of the conditions that prevail in sites at risk of disruption events, and pointing to possible mitigation in the design of the infrastructure, and its relationship to the local environment.