Horizon explores: Risk and Safety Intelligence
Navigating the complexities of risk and safety
Find out how decision-making has evolved in risk and safety, and the tools RSSB offers to support it.
Anna PlodowskiSenior Content Editorial Manager, RSSB
To quote one of the industry’s best-known phrases, reducing the risk of rail accidents and incidents to ‘as low as reasonably practicable’ (ALARP) has been an overarching legal requirement since the 1974 Health and Safety at Work Act. Our approach ensures our decisions are confidently rational and demonstrably compliant with, if not exceeding, this requirement.
However, in the past, major safety improvements typically followed major accidents and addressed a single cause. Although this led to important improvements, this reactive management approach could also result in disproportionate, knee-jerk responses and a narrow focus that failed to address more significant, though less headline-grabbing, sources of safety risk.
The industry’s Safety Management Intelligence System (SMIS), which we manage, is a hugely valuable source of safety event information. Our independent position at the centre of the industry allows the sector to trust us with their data. Pooling data from different, even competing, operators covering a wide range of accidents and incidents paints a richer picture than using disparate data silos.
We’re constantly developing ever-more complex ways of understanding risk and safety to meet industry needs, including what risk is, and the data we need to measure it.
We’re using data to map the likeliness of an event, its potential consequences, and understand how representative the past is of the present and future. We also include rare but potentially severe consequence events, of which little or no direct experience exists. This requires combining hard data with operational experience to develop models of the railway, how accidents occur, and their outcomes.
Complex trade-offs can arise between different aspects of safety, or between safety, cost, and performance. In these instances, our Safety Risk Model (SRM) can be particularly useful, structuring and quantifying the risk from operating and maintaining the mainline GB railway. Version 9, the latest version, uses new modelling techniques and new data sources to produce local risk estimates.
Combining measures of multiple risks or harms can be useful too. We’re all familiar with today’s Fatalities and Weighted Injuries (FWI) metric, but this evolved out of earlier pioneering work. Combining outcomes of different severity for safety decisions was introduced by the Safety Panel established in the late 1980s. This recognised the need for a more rigorous evidence-led approach, a robust understanding of the benefits of safety decisions, and a holistic approach to decision-making. This enabled the prioritisation of safety investments where they were most needed, sometimes—as in the case of safe walking routes for staff—including solutions that were previously neglected.
It isn’t enough to provide tools or metrics that can be used in more complex risk and safety decisions. Industry also needs to understand the principles that underpin good risk management. Our industry guidance document Taking Safe Decisions (TSD) lays out the essential stages of risk management and implementation, and good practice in decision making. It emerged from concern in the mid-2000s that excessive risk aversion was driving unnecessary cost into rail, and a lack of clarity over how to interpret the ALARP requirement. TSD helps prioritise safety improvement, identify where to invest or save, demonstrate compliance with legal obligations, and keeps up with current legislation and good practice. Our Safety and Intelligence teams use it themselves in addressing industry-wide challenges and support our members using it for safety-related decisions.
An increasingly digital railway provides new opportunities to understand and reduce risk. 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 to help our members better understand and manage risk from Signals Passed at Danger (SPADs). Currently covering around one-third of the network, we are extending its range, improving the user interface, and adding new functionality.
However 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. 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.
The rail industry’s understanding of risk and safety is evolving all the time. By harnessing the power of collaboration and data sharing, we have been able to create tools and frameworks that will support robust and demonstrably compliant risk and safety decision-making today, and in the future.
Want to know more? Find out about the tools covered in this article on our website.
SMIS
SRM
TSD
Red Aspect Approach to Signals
PRIMA