Digital re-engineering of railway safety systems

Railway companies operate safety management systems to ensure the safety of trains, staff and passengers. Safety management hinges on capturing and storing structured and unstructured data relating to the operation of the railway and the performance of their safety controls. This data is used by railway safety practitioners to better understand railway system safety risk, leading to the development of strategies to improve the safety of the railways, thereby reducing accidents, injuries and fatalities.

Professor Coen van Gulijk

Modernising reporting systems

Historically, railway safety management systems have used digital data analysis techniques sparsely, due to a lack of knowledge on how to appropriately apply these to rail datasets. Research carried out by Professor Coen van Gulijk, Peter Hughes, Dr Miguel Figueres, Dr Rawia El Rashidy and Julian Stow provided blueprints, methods and algorithms for the modernisation of reporting systems in the UK and France with Spain, Switzerland and Denmark following in their tracks. The work inspired the European Railway Agency to consider novel reporting techniques in their mandate to modernise European reporting systems as well. Their research focused on three key areas:

Natural Language Processing

Automated text analysis, referred to as Natural Language Processing (NLP), is a useful tool for the analysis of safety reports. Because standard NLP techniques do not perform well with railway jargon and poor spelling (commonplace in safety reports) they developed novel NLP approaches that dramatically improved the outputs from NLP on unstructured data. This approach was adopted by The Railway Safety and Standards Board (RSSB) (UK), Network Rail (UK), RENFE (Spain) and TNO (Netherlands).

Telemetry data analysis techniques

Railway systems, such as signalling, planning and operations, and on train data recorders (OTDR) generate immense amounts of data that are not designed to inform operational safety management systems. The research focussed on developing new data analysis techniques for use on data from trains and signalling systems. The results provide new safety key performance indicators and insights for planners including the development of the fundamental logic and algorithms for a Red Aspect Approaches to Signals (RAATS) software analysis tool, which is used to better understand the likelihood and occurrence of Signals Passed at Danger (SPAD) events, which have potentially catastrophic consequences.

Signalling data analysis techniques

On a national level the research outputs have been used by the Rail Safety and Standards Board (RSSB) to develop and publish a rail risk toolkit for the Red Aspect Approaches to Signals (RAATS) tool. The tool uses signalling data to predict those signals that have high-risk red-aspect approaches. Railway operators are able to use the tool to greatly reduce risk and improve the overall flow of traffic on the network and consequently the punctuality of the service.

Digital safety system

The visualisation of results is of key importance if safety information is to be interpreted correctly. The research connected safety indicators to commercial safety software: BowTies. To some extent the work has inspired GB railway partners to work with BowTies as efficient safety management interfaces, including RSSB, Network Rail, London and North Eastern Railway (LNER). Overseas railways have followed their example, including SNCF, Renfe, Swiss FOT, ProRail, Danish Railways and even the European Railway Agency adopted elements of BowTies.

This research has helped significantly to contribute to railway safety across Europe. The findings supported the European Railway Agency in developing digital incident registration systems. The research had an impact on the development and launch of a digital safety system for the French railways SNCF.

For more information on the research in this article email: c.vangulijk@hud.ac.uk