By Luis Lopez, AI transportation consultant, CEO of Go Hub.io Holdings Corp and subsidiaries, and host of the Freight Guru Podcast
Every carrier knows the feeling of a truck rolling with nothing in the trailer. Empty miles burn fuel, driver hours, tires, and time without producing a dollar of revenue. Some empty movement is unavoidable, because freight does not flow evenly between markets. But a lot of it comes from decisions made too late, with too little information. That is where AI earns a place in the dispatch office.
As an AI transportation consultant, the first thing I look at is not the software. It is the data a carrier already has: where its trucks delivered, where they picked up next, how far they drove empty in between, and how long they waited. Most of the ten uses below depend on that history being reasonably clean.
If you need a refresher on the basics, start with what deadhead means in trucking, then come back here.
1. Reload prediction before the truck delivers
The best time to find the next load is before the current one delivers. Predictive models can estimate, for a given delivery city and day of week, how likely it is that a reload will appear nearby and how long it usually takes. Dispatchers then know which deliveries need early attention and which markets usually take care of themselves.
The value is in timing. A reload search that starts the morning of delivery has fewer options than one that starts a day or two earlier.
2. Backhaul matching against known outbound lanes
A carrier with steady headhaul freight from one region can use AI to look for return freight that fits the same equipment, timing, and driver home-time needs. Instead of a dispatcher scanning lists manually, the tool ranks backhaul options by how much they reduce empty distance and how well they line up with the next committed pickup.
This works best when the carrier understands its own headhaul and backhaul balance, so it knows which return lanes are worth discounting and which are not.
3. Network balance across the whole fleet
Looking at one truck at a time hides the bigger pattern. Network-level models look at where the fleet is heading over the next several days and flag imbalances: too many trucks converging on a market with weak outbound freight, or too few trucks near a customer that ships every afternoon.
With that view, a planner can steer freight choices earlier, accepting a slightly lower-paying load that moves a truck toward a stronger market rather than a better-paying load that strands it.
4. Triangular and multi-leg routes
Sometimes there is no good direct backhaul. A three-leg route, where a truck goes from market A to B, then B to C, then C back toward A, can cover more loaded miles than a simple out-and-back. Optimization tools are good at searching these combinations, which are tedious to work out by hand.
The output still needs a human check. A route that looks efficient on a map may not fit driver hours of service, customer appointment windows, or a driver who needs to be home by the weekend.
5. Smarter load board filtering
Load boards show a lot of freight that is technically available but practically wrong for a given truck. AI filters can learn from a carrier’s past choices and screen out loads with poor pickup timing, problematic facilities, or weights and equipment that do not fit. That leaves dispatchers with a shorter, better list.
The risk is overfitting. If the filter only learns from past habits, it can hide new lanes that would actually reduce empty miles. Review the filter rules periodically.
6. Lane history and reload time
A carrier’s own lane history is one of its most valuable assets. Analysis tools can show, by lane, how far trucks typically deadhead after delivery and how long they sit before the next pickup. Lanes that look profitable on the rate alone can turn out to be weak once the empty miles and waiting time after them are counted.
That changes how a carrier prices and accepts freight. A lane into a strong reload market can justify a lower rate than a lane that ends in a dead zone.
7. Dynamic dispatch decisions
When a load cancels or a delivery runs late, the plan for the rest of the day changes. AI-assisted dispatch tools can re-evaluate assignments quickly and suggest swaps between trucks that cut total empty distance. For more on how that matching works, see AI dispatch and load matching explained.
8. Relay and slip-seat planning
For fleets that run relays or slip-seat operations, AI can help plan where trailers and tractors meet so that equipment keeps moving loaded while drivers get home. Getting the meeting points right reduces the empty repositioning that often happens around driver changes.
9. Customer and facility patterns
Some shippers release freight at predictable times. Some receivers routinely delay unloading. Pattern detection on appointment and dwell data can show which facilities are reliable reload points and which tend to burn hours that push the next pickup out of reach. That knowledge feeds back into which loads a dispatcher chooses.
10. Measuring empty miles honestly
You cannot reduce what you do not measure. AI tools can reconcile telematics data with load records to calculate empty miles by truck, driver, lane, and customer. This is less exciting than optimization, but it is often the step that makes everything else possible. Many carriers discover their empty-mile picture is different from what they assumed.
The limits
AI does not create freight. If a market has weak outbound demand, no model will make reloads appear. What the tools do is help carriers see the problem earlier and choose better among the options that exist.
- Data quality matters. Missing timestamps, wrong addresses, and manual workarounds produce bad recommendations.
- Constraints are real. Hours of service, equipment type, driver preferences, and customer commitments must be part of the model, or the suggestions will be ignored.
- Dispatcher judgment stays. Relationships with shippers, knowledge of a specific facility, and a sense of which driver can handle which run are not captured well in data.
- Small fleets have less history. Models trained on a handful of trucks can be noisy, so start with simple measurement and lane analysis before trying full optimization.
Where to start
A practical sequence looks like this:
- Measure empty miles by lane and customer using data you already have.
- Identify the deliveries that most often lead to long deadhead runs.
- Start reload searches earlier for those deliveries.
- Test backhaul matching and multi-leg routing on a few trucks before rolling it out.
- Review results monthly and adjust the rules.
Reducing empty miles is not one decision. It is many small decisions made a little earlier and with better information. That is exactly the kind of work AI handles well when people stay in charge of the final call.
For more conversations on freight and AI, subscribe to the Freight Guru Podcast.
About the author: Luis Lopez is a Miami-based AI transportation consultant and logistics entrepreneur, the CEO of Go Hub.io Holdings Corp and subsidiaries, and host of the Freight Guru Podcast.
