Empty kilometers in trucking — a solution that cuts them
Every empty kilometer is fuel, driver time and wear with no revenue — and on a typical fleet, empty running quietly drains a six-figure sum from the P&L every year. It isn't one big leak; it's three concrete, fixable problems that add up. We show how to build a solution that attacks all three — it hands the dispatcher ranked, concrete steps and learns from their decisions. Not reports, but decisions you can see in fuel burned and capacity freed.
Three real problems this solves — and who has them
Across the EU, 21.6% of all road-freight vehicle-kilometers ran empty in 2024 — and 25.8% on national transport (Eurostat). Driver wages and fuel are roughly two-thirds of a carrier's cost base, so every empty kilometer burns the biggest cost line for zero revenue. Here is where that actually happens.
1. The empty return leg nobody had time to fill
Who has it: a 55-truck regional carrier hauling for automotive and construction customers across the SK–CZ–AT–HU corridor — strong outbound contracts, weak structured backhaul.
The dispatcher has 30–60 minutes after a delivery to find a return load before the driver's hours and the next dispatch force the truck home empty. A 300 km empty return burns €150–420 with no one paying for it. One avoidable empty return per truck per week on a 50-truck fleet is well over a quarter of a million euros a year in variable cost alone.
How we solve it: every morning the dispatcher gets a ranked queue of concrete return-load and repositioning options that fit the truck's hours, equipment and home base — each with the empty km saved, the net contribution and a plain-language "why".
2. Freight that never reaches the optimizer because the data is a mess
Who has it: a 25-person freight forwarder whose orders land all day as PDFs, email bodies and spreadsheet attachments from dozens of small shippers.
Hand-keying a single transport order takes 10–15 minutes (up to an hour for a complex one), and the same load detail gets retyped four or five times downstream. By the time a load is in the system, the window to combine it with something else has closed — so the matchable freight that could have killed an empty leg was never even visible.
How we solve it: the system reads the messy inbound orders directly — parses the PDF, the email, the ERP record — and drops normalized, matchable loads into the pool automatically, with a human confirming anything ambiguous. You can't optimize freight the system can't see.
3. Each depot optimizes its own trucks, so the network leaks between them
Who has it: a 120-truck carrier with six regional depots, where each branch is measured on its own numbers.
Depot A sends a truck empty toward a region on the same morning Depot B has a load there going begging — but the two dispatchers never see each other's boards in time, and no load board will ever tell you to reassign your own committed truck. The empty repositioning piles up at the network level, where nobody owns it.
How we solve it: the system optimizes across the whole network, not one board, and proposes concrete inter-depot swaps ("reassign load #2231 to a Žilina truck already heading that way — saves one empty reposition, +€260 net") that a branch manager can see is a fair trade.
Illustrative figures grounded in industry benchmarks (Eurostat empty-running rates, IRU cost-per-km ranges). The real impact depends on your lanes and data — which is why we validate it on your own trips.
How the solution works
All three problems share one root: at the moment of dispatch, the freight that could fill an empty leg is invisible, unmatched, or stuck in another depot. One loop closes that gap.
The system runs over real trips and every morning prepares the dispatcher a queue of concrete suggestions — ranked by confidence and savings. The dispatcher accepts or rejects them with one click, and the solution thereby learns what works in practice.
Where exactly AI helps
Let's be precise — the kilometer savings themselves aren't produced by a language model, but by optimization: matching return trips, swapping dispatches, repositioning. That's operations math.
AI adds value where math alone isn't enough:
- reads data from all your systems — it turns orders from emails, PDFs, spreadsheets, ERP and dispatcher notes into unified data the optimization can work with,
- explains "why" for each suggestion in plain language — so the dispatcher trusts it and actually uses it,
- handles incomplete and messy data without the whole process grinding to a halt.
In other words: AI doesn't raise the ceiling of savings, but it decides whether anyone actually uses the system and whether it covers real, chaotic operational data.
What the morning suggestion queue looks like
Each suggestion is concrete — vehicle, route, reason and a quantified saving. A sample from a pilot simulation over real trips:
| Suggestion | Confidence | Empty km | Saving |
|---|---|---|---|
| Combine a return with a waiting order on the way back | 92% | −1,830 km | ~€847 |
| Reposition a vehicle for tomorrow's pickup + an intermediate load on the way | 87% | −1,428 km | ~€605 |
| Swap the assignment with a vehicle from another depot (dispatch swap) | 84% | −1,404 km | ~€539 |
The system does nothing behind the dispatcher's back. It suggests, quantifies and explains "why" — the decision stays with the human.
Impact during the pilot
As accepted suggestions accumulated, the share of empty kilometers in a simulation over real trips (January–May 2026) gradually dropped from 22% to 17% — roughly a fifth of empty runs.
Fewer empty kilometers means direct savings on fuel and driver time — and more capacity for paid trips without a single new vehicle.
What it means in euros
On a model fleet of 200 trucks (120,000 km/year per vehicle, avoidable cost ~€0.55 per empty kilometer) the annual saving works out as follows:
The realistic scenario means ~€660,000 a year (~€3,300 per vehicle) — without a single new truck, just by better using the ones already on the road.
Model estimate. The numbers are based on the stated assumptions and serve to give a sense of the order of magnitude. The actual result depends on the type of transport, region and data quality — which is why we validate it on your own data, not on promises.
Why it works better than classic reporting
Classic reporting tells you there are a lot of empty kilometers. It doesn't tell you what to do about it today. The difference is in three things:
- A suggestion instead of a number — the dispatcher gets an action, not a chart.
- Confidence and saving at every step — they know what to prioritize.
- Learning from decisions — the more suggestions go through, the more accurate the next ones.
Who it makes sense for
We see the biggest impact with carriers that own their fleet, where many trips are planned daily and return trips often stay empty. Fix the three problems above and the business case is simple: fewer empty legs, more paid trips per driver, and a lower cost per kilometer — without buying a single new truck, and with the driver-hours you already struggle to hire spent on revenue instead of deadhead.
A dispatcher that cuts empty kilometers is one concrete AI agent — the wider pattern is in AI agents for business: what they actually do and where to start, and our AI solutions overview shows where it fits.
If that's you too, get a free diagnostic on your own data — we map it and show you exactly what we'd build, the impact and the cost. No obligation.