Regulator talks intelligence-based enforcement

During a recent National Bulk Tanker Association (NBTA) webinar, the National Heavy Vehicle Regulator (NHVR) outlined its modern approach to compliance and enforcement within the trucking industry.

NHVR Chief Operations Officer, Paul Salvati, began by describing the direction in which the NHVR is heading in order to provide a streamlined and targeted approach to this key area of its operations.

“The NHVR has been talking for a while about our desire to become an intelligence-led and risk-based modern regulator,” said Salvati.

“We’re not there yet, but we’re working towards creating a national regulatory model to ensure law-abiding operators are not tarred with the same brush as those who flout the laws.”

Salvati said a compelling reason for this change is that the NHVR only sees and interacts with a very small proportion (under 10 per cent) of the national truck fleet.

“It’s difficult to identify the ‘cowboys’ when we’re only dealing with such a small percentage of the fleet,” he said.

The goal with the Regulatory Model, Salvati said, is to indirectly reward those who are doing the right thing by not intercepting them and focus resources on changing the behaviour of those doing the wrong thing.

He showed an illustration of the NHVR National Regulatory Model which detailed Data and Intelligence including risk models and risk profiles in one column and the Compliance and Enforcement levers the NHVR can use to change behaviour in the other column.

“We draw our data and intelligence from a number of sources including registration data bases, information from insurers, on-road intercepts, crash data, vehicle data, among others,” said Salvati.

“All this data goes into what we call the Safety and Compliance Regulatory Platform which enables us to make associations and match up data,” he said.

“What we also do with this data is define what creates a risk and the best way to do this is to investigate a crash and then work backwards, identifying all the elements that pertain to the participants in this incident.”

Salvati said when this is done many times over, patterns start to develop and it can be seen, for example, that with fatigue related crashes trends start to emerge which are common across many of the entities involved.

This led to the creation of generic risk models for each of the factors such as fatigue, mass management or mechanical into which new data is constantly fed.

“From this we develop risk profiles around individual fleets or vehicles and determine which entities have a higher safety risk,” he said.

Salvati said these risk profiles are fluid – continually updated to accurately reflect the current status of each entity and the more data that is added the higher the accuracy of the risk profile.

The upshot of this is that the NHVR is aiming to garner a much better understanding of the behaviour of the national truck fleet as a whole, thus enabling the organisation to specifically target problem operators and not bother those who have a proven track record of doing the right thing.

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