Picture of Open Access badges

Discover Open Access research at Strathprints

It's International Open Access Week, 24-30 October 2016. This year's theme is "Open in Action" and is all about taking meaningful steps towards opening up research and scholarship. The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. Explore recent world leading Open Access research content by University of Strathclyde researchers and see how Strathclyde researchers are committing to putting "Open in Action".


Image: h_pampel, CC-BY

Modelling GB rail timetable risk : analysis of SPADs due to human error on the Scotland network between 2004-10

Montanana, Mark and Griffin, Dave and Revie, Matthew and Walls, Lesley (2011) Modelling GB rail timetable risk : analysis of SPADs due to human error on the Scotland network between 2004-10. In: Annual Reliability and Maintainability Symposium 2011, 2011-05-03 - 2011-05-05.

Full text not available in this repository. (Request a copy from the Strathclyde author)


As the timetable dictates the arrival times of trains at junctions and stations, it seems reasonable to assume that different timetables may result in different levels of opportunity for conflict and collision. Although a well designed timetable should have few (if any) conflicts, trains do not always run on time, and consequently the robustness of the timetable to service perturbations is an area of interest to RSSB, the not for profit organisation that collect and analyse safety data on the GB rail network. Our aim is to investigate selected operational data for the GB rail network to examine how variables characterising timetable risk affect the probability of occurrence of Signals Passed at Danger (SPAD) where SPADs represent a precursor for railway accidents. Interviews with RSSB analysts and engineers have surfaced factors such as complexity of the timetable, frequency and density trains, amongst others, that can be used to characterise timetable risk. Data have been extracted from multiple databases and prepared to capture the relationships between, for example, the network junctions and the times at which trains pass the junction. A combination of exploratory statistical analysis and regression modelling has been used to investigate the data and build preliminary models for explaining and predicting the probability of a SPAD as a function of accessible variables representing timetable risk.