The railway has always collected data about accidents and their consequences and has a long history of data-informed decision making. We’ve learnt the value of sharing data across organisational and functional boundaries to provide a holistic view of safety that helps us prioritise investment and take decisions from a whole system perspective. Now, the digital revolution is enabling us to get more value from more data and bringing about a step change in safety management.

The industry’s Safety Management Intelligence System (SMIS), which we manage, is a hugely valuable source of the safety event information that the industry has agreed to share. Pooling data from different operators and covering a wide range of accidents and incidents, we provide a richer picture of railway safety than would be possible if this intelligence was held in disparate silos. Our independent position at the centre of the industry has enabled everyone to trust us with their data. The SMIS system has developed hand-in-hand with GB rail’s culture of safety collaboration and information sharing. 

Over the years we’ve developed more sophisticated ways of using data to understand risk. To understand risk, we want to know both how likely something is to happen and the consequences that might arise if it did. Incident data – what has happened in the past – plays an important role and can also ensure analysis is grounded by operational reality. But we need to consider how representative the past is of the present as well as the future. We also need to ensure that we’re properly considering low frequency but potentially very high consequence events of which there may be no or very little direct experience. These can still represent a significant risk because of the magnitude of the potential outcome. This requires combining hard data with operational experience to develop models of the railway, how accidents occur, and their outcomes.

Our Safety Risk Model (SRM) structures and quantifies the risk from operating and maintaining the mainline GB railway. It effectively covers everything that can harm anyone anywhere on the railway network, and disaggregates this by the accident scenario, the people affected, and the likely profile of injuries that would result. The SRM is the starting point for most quantified risk analysis on the railway. It can be especially useful when making difficult decisions that involve complex trade-offs between different aspects of safety, or between safety and cost and performance. Version 9, the latest version of the model, makes use of new modelling techniques and new data sources to produce local risk estimates so, for example, the results are available by route and by operator.

An increasingly digital railway is providing new opportunities to understand and reduce risk. The proliferation of data about railway assets and operations, along with the tools and techniques to derive insights from it, are already proving to be a game-changer for safety management. But there’s much more potential still to be unlocked. 

For example, our Red Aspect Approach to Signals Tool ingests around 5 million data points daily from Network Rail signalling and train describer feeds. It turns them into intelligence that can help our members better understand and manage risk from Signals Passed at Danger (SPADs). The current tool only covers around one-third of the network, which constrains its use. We have started a project to redevelop the tool to extend its coverage, improve the user interface, and add new functionality. This will allow analysis of red aspect approaches and delays by train service, which promises to deliver both safety and performance benefits.

The digital revolution can bring about a step-change in safety management. But data alone rarely provides the answers. It also needs to be understood in context and incorporated into sense-making models and decision support tools. This is especially true when managing major accident risk, for which direct data will very limited or absent. We are at the forefront of developing and embedding data-driven approaches to safety improvement, with a mature capability in risk analysis and risk modelling combining with a growing capability in big data analytics. 

The Whole System Risk Model and the associated PRIMA decision support tool we are developing with Network Rail provide good examples of how these capabilities can complement each other. We used big data techniques to understand the relationship between extreme rainfall and failures of soil cuttings and embankments. We then combined this with our risk modelling know-how to evaluate the impact of different speed restriction options on both direct risk from train derailments and knock-on risk associated with delays and service perturbation. The resulting PRIMA decision support tool will help decision-makers prepare proportionate operational responses to adverse weather. 

Whatever your need for risk and safety intelligence, data is crucial. As the digital revolution increases the availability of data, we look forward to increased sharing and collaboration with industry so that we can deliver increased value from our risk and safety intelligence.

Risk and safety intelligence tools to help you: