For the majority of operations, fleet management systems record truck metrics including the truck response to the road it runs on. In truck haulage, cycle times can vary as a result of many factors, not the least of which is the road condition itself. Whilst we may see, over time, an increase in cycles times, it’s often harder to explain the source of that increase – especially if and when it is related to road deterioration as opposed to simply the geometrics of the haul itself.
One critical measure of truck performance is based the impact of increased rolling resistance on cycle times and unit costs. But can a measure of this effect be extracted directly from existing ‘big data’ and used to inform road management strategies?
Ultimately, it may not be just as simple as ‘today’s rolling resistance is 3%’, but rather interrogating the data to reveal an incremental change indicator which would flag a more pro-active response to road maintenance, as opposed to the reactive responses more typical of current operational strategies.