Dynamic heat risk management to reduce the costs of propagating hot weather delays on the railway network.

Urban areas contain not only significant concentrations of both railway infrastructure, but also elevated temperatures due to the urban heat island effect. During the summer months, this can often mean that track and lineside assets in cities breach critical temperature thresholds resulting in localised failures on the network. Whilst the rest of the track remains fully serviceable, the interdependent nature of the system means that asset failure at critical nodes (i.e. in urban areas) propagate throughout the rail network causing extensive delays, passenger dissatisfaction, and a disproportionate additional cost than the original fault itself. Examples of such impacts were clearly highlighted during the recent 2013 heatwave which caused the much publicised track-buckle at London Waterloo and track-circuit malfunction at Edinburgh Waverley. In order to manage the heat risk, blanket speed restrictions are often imposed above pre-defined temperature thresholds to ensure passenger safety. The delays caused due to direct failures or, more commonly, speed restrictions are not only disruptive to passengers, but they also cost Network Rail significant money in the form of Schedule 8 payments (i.e. fines) to the train operating companies that use the infrastructure.

This proposal explores whether the existing temperature thresholds used for heat risk management are appropriate. In particular, it focuses on determining the feasibility of dynamic thresholds which increment over the course of the summer season. The rationale for this is that failures are 'harvested' during hot spells and hence the first heatwave of the year highlights network vulnerability, with subsequent heatwaves (unless significantly hotter) being less problematic. This approach represent a radical and innovative solution to reduce the number of blanket speed restrictions presently used in the industry, the significance of which will become even more apparent when the consequences of climate change on the network are factored into the analysis.