We in GB rail tend to err on the side of caution—and with good reason. How else would we remain one of the global frontrunners in railway safety?
However, excessive caution in certain circumstances can result in economic and operational costs that unnecessarily hinder our industry’s growth.
Consider gauging, which is the process of checking whether a train can safely travel a route without hitting nearby infrastructure or rolling stock.
Such clearance assessments are crucial. A case in Spain a few years back, where a new fleet of trains was found to be too big for tunnels, tells us as much.
Traditional absolute gauging certainly keeps people, assets, and infrastructure safe. It has done so for decades.
But applying this method today, with no supplementary assessment, can actually hold us back because it works on the worst-case scenario.
It assumes that every tolerance is at its extreme, all at the same time. The result is a clearance that’s much larger than trains actually need.
This excess leads to costly infrastructure upgrades, delays in introducing new rolling stock, reduced operating speeds, rerouted services, and the imposition of avoidable restrictions.
If our goal is to achieve growth without compromising safety, this approach just won’t do.
Recalculating risks
In some situations, we’d do well to use a supplementary assessment called probabilistic gauging when determining clearance between trains and nearby structures or other trains.
The method works by simulating how different variables behave in the real world because, in reality, a train may be narrower or shorter than the dimensions assumed. And in these instances, clearances wouldn’t be a problem.
Yes, traditional absolute gauging is to remain foundational, forming the bedrock of railway gauging—after all, we wouldn’t want a repeat of the Spanish fiasco on our shores.
But probabilistic gauging should be applied at pinch points on the network where it may be assumed, for example, that a new train can’t operate or infrastructure may need to be altered.
A new rail industry standard, which was published last month, sets out requirements and guidance for the application of a Monte Carlo statistical method to undertake probabilistic gauging calculations.
Supporting the wider adoption and more efficient use of probabilistic gauging is critical for both cost-effective and safe growth within our industry. We expect that this standard—by clearly defining the method’s scope, procedures, required inputs, and expected outputs—will provide this support.