Safety
at the base we have the mountain of data collected from all those millions of normal journeys where very little of interest seems to be happening. Or so it might seem. The aviation sector was faced with exactly the same problem in the 1970’s. In the dispassionate terminology of the risk field, an accident represents a ‘lagging indicator’ and all we can do is learn from it. What we really want, though, are ‘leading indicators’, things that tell us before an accident happens so that we have time to do something about it. The UK aviation sector innovated an approach called Flight Data Monitoring, which uses black box data to do exactly this. Here the opportunity was recognised to make proactive use of all the data being collected from those countless ‘normal’ journeys. The bigger the data, the more subtle the trends that could be detected which, if they were allowed to continue, might eventually lead to something more serious. So you have a cycle. A continuous living database of black box data, the detection of trends within it, counter-measures, and monitoring to ensure the countermeasures aren’t causing more problems than they solve. What sort of countermeasures exactly? Perhaps a very minor procedural change might be all it takes to arrest a trend? Maybe an adjustment to the
infrastructure or equipment? Maybe greater emphasis to a particular subtlety in training? In any case it isn’t really about big traumatic step-changes and it’s certainly not about punitive measures directed at drivers. Data monitoring is about constantly helping the industry evolve towards its desired safety and performance goals.
The rail industry is now at a point where this experience and insight can be brought to bear to inspire a similar form of ‘Rail data monitoring’. This is what we have been examining here at Heriot-Watt University.
Predicting where risks rise
Believe it or not there is still a fly in the ointment. Even despite all this, human factors incidents still percolate to the top to surprise us, even with mature flight data monitoring techniques. That is what our research has been specifically targeted towards. Here in the academic sector we have scientific methods that can accept OTDR data as an input and tell us, as an output, where human factors risks might be increasing. In other words, what we have been developing are various types of ‘human factors leading indicators’ which we can derive from all that routine data being collected from OTDR devices. A simple example is the so-called vigilance
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We know that after about 15 minutes of not having to do much, reaction times start to slow down
decrement. We know that after about 15 minutes of not having to do much, reaction times start to slow down. We can examine black box data for instances where long periods of monotony are broken by periods of sudden high activity. Psychological research tells us what sorts of errors might become more likely if we put people in this situation: large-scale black box data tells us exactly where such situations might be occurring in real-life. The UK is currently a world leader in flight data monitoring and there is a major opportunity for it to become a world leader in rail data monitoring too. By doing so, we can go beyond the headlines and get to the root of how something like this could happen and
stop it before it does. • Dr Guy Walker is a lecturer at Heriot-Watt University’s Institute for Infrastructure and Environment. Email:
G.H.Walker@hw.ac.uk
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