Empty miles, or empty running, refers to the distance travelled by a truck running without cargo. In practice, it’s mileage that isn’t directly carrying revenue-generating cargo – which means fuel, driver time and vehicle wear without a load on board.
As much as transport operators try to avoid their fleet from getting into this position, it can be difficult to prevent it from happening.
Often not because of any decisions made by the planning team or driver, but because there’s simply not the right flow of data. The information they’d need to make better decisions either doesn’t exist, arrives too late, or lives in a system no one else can see. It’s a data problem that gets disguised as a human one.
In this blog, we show how empty miles are not usually a human efficiency problem, but rather, a data visibility problem.
How empty miles actually happen
DfT’s Domestic Road Freight Statistics show that empty running rose to 31% in 2025 (5,897m of 18,975m total HGV kilometres run empty) in the UK.
The UK also compares unfavourably to the rest of Europe: one in three UK lorry kilometres is empty versus roughly one in four (25.9%) across the EU average.
As you can see, it’s a significant issue across the UK and EU. However, in most cases, empty miles are not caused by negligence.
A breakdown across various data points is often the more likely culprit.
Here’s a common sequence of events that creates an empty leg:
- Shipper confirms a load late
- Carrier can’t find a backhaul in time
- Driver departs empty rather than waiting and returning late
It’s really easy to look at this series of events and think “well, why couldn’t the driver just find a backhaul.” But, it’s often a lot harder than it sounds.
Some transport operators may look at load boards to see if there’s anything relevant that the driver could haul back. Load boards can help, but they only show what has been posted to the market – not the full picture of future demand, timing, compatibility or network fit. And because a large share of freight moves under contract, many loads never appear as spot opportunities in the first place.
Finally, a lack of real-time visibility means that carriers don’t know where the trucks are in relation to available loads, making it difficult to confirm jobs before a competitor swoops in.
An incomplete flow of data is the bottleneck across all these scenarios, not the driver.
Where the data breaks down
Data across the transport cycle is often disjointed and hidden. Here are some of the key areas where the flow of data breaks down:
- Fragmented systems: shipper TMS, carrier TMS, and broker platforms rarely talk to each other
- Late confirmations: loads confirmed close to pickup can leave very little time to plan a return leg
- No forward visibility: shippers don’t share upcoming demand; carriers can’t pre-position
- Static lane thinking: historical lane data drives planning, not live demand signals
- Manual handoffs: each step in the chain – booking, dispatch, POD – involves a human re-entering data that already exists somewhere
The instinct can be to frame empty miles as a planning failure on the carrier side, or worse, an accountability problem at the driver level. Largely, because they are the final and most visible part of the chain.
But as we can see above, that framing misses the root cause entirely – and doesn’t help you solve the problem.
How to solve the empty mile problem
At Qargo, we see the solution as a three step process.
- Make empty miles visible
- Plan to reduce them
- Eliminate empty miles through network collaboration
Let’s break these down step by step.
Make empty miles visible
You can’t reduce what you haven’t measured. So you need to get clinical about capturing this data.
Qargo’s dashboards help make empty mileage visible by breaking distance down into loaded, empty and deadhead mileage across resources such as drivers, vehicles and subcontractors. This gives operators a clearer view of where empty running is happening most often, and which routes, vehicles or operating patterns are creating the biggest losses.
Combined with the right FMS or telematics integrations, actual driven distance can be brought closer to the planning view, reducing the gap between what was planned and what happened on the road.
Plan to reduce them
Now you can see where the empty miles are creeping in most frequently, you can plan in a more targeted manner.
The planning board within Qargo shows cargo utilisation at each stop, so planners can immediately spot where additional loads could fit on a part-empty trip. Route optimisation features and integrations mean that planners can stop making gut calls and start making data driven ones.
Better data quality also improves planning decisions: when order details are complete and consistent, optimisation has the context it needs to produce more reliable routes.
Eliminate empty miles through network collaboration
Finally, attack the structural cause through network collaboration. Pallet networks are one of the most effective structural answers to empty miles – they balance outbound and inbound loads across members by design.
With Qargo’s native integrations across the UK’s major pallet networks (Palletline, Palletforce, Palletways, PallEx, and others), core data exchange – order imports, status syncs, ePODs – is automated, removing the manual handoffs that cause planning delays and create the gaps empty miles fill. Integration depth varies by network, so some data points (like live ETAs) may sync faster on certain networks than others.
None of this requires drivers to do anything differently. It requires the right data, in the right place, early enough to act on.
It’s worth noting that even with the best data, you can’t solve trade and lane imbalances. Sometimes the flow of goods will simply not work in your favour for all legs of a trip. However, that makes it more important to quickly spot the legs you are able to optimise on.
Conclusion
Empty miles are often treated as an unavoidable cost of doing business — but the data tells a different story. The high percentage of empty running is not because the problem is unsolvable, but because the information needed to solve it is usually late, missing, or scattered across systems that don’t talk to each other. When planners can’t see where capacity is going empty until after the fact, there’s no way to plan around it.
The fix isn’t complicated: make empty miles visible before they happen, and plan around them. That means having your orders, capacity, and route data in one system rather than pieced together after the fact.
If you’d like to hear how Qargo can help your business reduce empty running, get in touch today.
