Regional risk monitoring with the Precursor Indicator Model
With rail traffic increasing, here’s how the Precursor Indicator Model generates precise accident risk data.
Jasmin CollierEditorial Content Manager, RSSB
According to RSSB research, train travel is estimated to be more than 20 times safer than travelling by car. These are welcome safety statistics—so much so that train accidents on our network are relatively rare. Given this, we can only fully understand incident risk by examining the potential underlying causes.
Enter the Precursor Indicator Model (PIM): industry’s incident precursor monitoring tool. Developed and maintained by RSSB, the PIM analyses a range of possible accident precursors to generate a weighted risk calculation, and this informs rail management of areas that might require further attention.
Train accident risk comes down to a handful of core events, including collisions, derailments, and fires. To provide a more granular view of what can cause these events, the PIM breaks them down into several ‘precursor groups’, including objects on the line, SPADs and adhesion difficulties, and issues with structures.
The tool then takes past national occurrences of such events, scaling them to account for increased exposure due to increasing volumes of rail traffic, and provides an overall view of train accident risk. More specifically, it tells us the degree to which a given precursor is affecting train accident risk across the country and in particular regions.
PIM data is presented in a dedicated, easy-to-use dashboard. The ability to filter the results by region was added earlier this year, and it’s proving particularly useful for regional managers. Notably, this capability can help them interrogate the data as they seek to understand and bring down train accident risk in their area.
You can use the Power BI dashboard to bring out the data and really understand and create graphs on the fly.
But it’s not just a static display; the PIM can also be used to follow short- and long-term trends in accident precursor prominence. The tool tracks the frequency of each precursor actually leading to a train accident, which enables it to accurately capture changes in risk over time. For example, we know right now that a large proportion of train accident risk is not to employees or passengers but to members of the public—particularly at level crossings.
It’s widely known that individual incidents occurring at level crossings can lead to serious harm, both on the train and off, but how this translates to the broader risk profile is less well-known. The PIM weighs the outcome of each known incident based on its severity, such as by assigning one fatality the same statistical score as 10 major injuries, and this allows meaningful comparisons to be made across various incident outcomes. This is also what makes the PIM’s data precise and robust.
The PIM is charting the way towards an even safer rail network, so it may be worth embedding its data in your company’s risk management efforts.
To learn more about the PIM and how to integrate it into your company’s processes, head to our website.
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