How AI can help reduce deadhead miles

AI can help reduce deadhead miles by comparing the next loads before a truck finishes its current trip. It needs current load options, truck records, and rules set by your team. Your dispatcher checks the shortlist and decides what to book.

AI cannot guarantee that you will eliminate empty miles. Freight may not be there when you need it. A nearby load may not fit the truck, driver hours, or pickup time. The goal is to find better work with less empty travel, not to fill every mile at any cost.

What counts as deadhead miles?

Deadhead miles are miles driven with an empty trailer. DAT uses this definition in its guide to deadhead trucking. That differs from bobtailing, when the tractor has no trailer attached.

An empty trip still uses fuel and adds wear. It also takes time that your team must plan for. Whether someone pays for those miles depends on the job and your terms. Do not treat every empty mile as unpaid or assume that every loaded mile makes a profit.

Give the system the facts it needs

A chatbot does not know which loads are open or where your trucks will be next. It needs approved access to the right records, or current files your team supplies.

For a first test, use:

• The truck's planned drop point and likely empty time.
• Load pickup and delivery places, dates, and time windows.
• Driver hours and planned rest.
• Trailer type, size, weight limits, and other load needs.
• Rates, known fees, and the status of each load offer.
• Your cost rules and minimum profit target.
• Home time, shop visits, and work already promised.

Keep the source and the time checked beside each fact. A shortlist based on an old load offer can waste time. Missing costs can make a poor load look good. Ask for a check when a key fact is missing instead of letting AI fill the gap.

Compare the full trip, not just the next pickup

The nearest load is not always the best choice. A short empty leg can lead to a long wait, a weak rate, or a place with few good next loads.

Have the system show these checks for each option:

1. Empty miles from the planned drop to the pickup.
2. A truck-safe route and estimated travel time.
3. Time to unload, rest, reach pickup, and meet the next delivery.
4. Fit with the driver, truck, and trailer.
5. Expected revenue and costs under your own cost model.
6. What the next drop means for later work and home time.

Use a route tool built for trucks, not a guessed distance from a text model. Use tested code for cost math and rules. AI can help read varied load notes and explain why an option fits. It should not invent a route, rate, or legal driving window.

This type of planning is not just a theory. Trimble describes an AI-powered carrier system that recommends matches using truck location, driver hours, and equipment type. It says dispatchers can approve or override those matches.

Trimble also describes dispatch planning that checks time windows and later orders. These are the vendor's descriptions of its tools. They do not prove a set saving for your fleet or mean every AI tool can do the same job.

Put profit beside empty miles

Set the cost model with the people who know your fleet. Include fuel, driver pay, tolls, and wear. Decide how to count wait time and share fixed costs. Keep the same basis when you compare loads. Do not count the same cost twice.

Show estimated profit and the facts behind it, not just a score. Flag costs you do not yet know. A minimum profit rule is a filter for review, not proof that the final job will make that profit.

For example, imagine two load offers after a delivery. One is close but has a long wait and a low rate. The other is farther away but fits the driver's time and leaves more profit after the full trip costs.

The second may be the better choice even with more empty miles. This is a made-up example, not a client result. Your dispatcher would still check rates, terms, and availability before booking.

Keep driver hours and safety as hard limits

A higher-ranked load must not override hours-of-service rules. These rules limit driving and duty time. The FMCSA summary of hours-of-service rules covers driving limits, breaks, rest, and specific exceptions in the US.

Use the rules that apply to your operation and the driver's current record. A remaining-hours field alone is not a full legal check. Pickup delays, required breaks, and earlier duty time can change what is possible. Your safety team should review the checks before a live test.

Do not let AI change a driver's logs, promise an unsafe arrival time, or tell a driver to rush. A short route is not a safe route just because the software ranks it first.

Start with a shortlist, not automatic booking

A useful first system can gather the facts, run checks, and prepare a few options. Let your dispatcher see why each one passed and what still needs a check.

Keep booking rights separate. Before a person books, they should confirm that the load is still open and check the broker or shipper, rate, terms, and pickup details. A draft plan is not a booked load.

You may not need an AI agent for every step. A fixed workflow follows set steps. An agent can choose tools and next steps as it works toward a goal. Anthropic makes this distinction in its guide to building effective agents and advises teams to start with the simplest solution that works.

Use fixed rules for clear checks. Add AI where it helps with notes or explanations. Limit tool access, keep a log, and stop when required facts or checks fail. Do not send private driver or customer data to a model unless your data rules allow it.

Measure the whole result

Test on past trips first, using only facts that were known at the time. A test that uses future load data can make the system look better than it is.

Then try a small live test with human review. Compare similar lanes, truck types, and work periods. Record:

• Empty miles and total miles, using the same definitions each time.
• Profit estimates and actual trip costs once known.
• Wait time, missed appointments, and service failures.
• Time spent finding, checking, and fixing load options.
• Tool fees, setup work, and ongoing upkeep.
• Loads the team rejected and the reason why.

Fewer empty miles do not count as a win if profit falls or service gets worse. Expand only when the full results support it.

Start with one lane or truck group and a clear manual fallback. If you are still choosing a first test, use our guide to choosing the first task to automate.

This guide describes a planning approach, not a claim that Core Agentic has deployed this system for a trucking client. Any build needs the carrier's real data, approved tool access, tested rules, and a named person in charge.