Empty Miles in Trucking: How AI Reduces Deadhead Costs
Empty miles in trucking create a major challenge for freight companies. Every empty trip consumes fuel, driver time, and equipment capacity. However, these miles generate no direct freight revenue.
For carriers and freight brokers, this problem can reduce profit margins. In addition, unnecessary travel increases operating costs and limits available capacity.
Fortunately, artificial intelligence (AI) offers new ways to improve freight planning. AI-powered freight matching helps companies find suitable loads, reduce empty trips, and make better dispatch decisions.
According to the American Transportation Research Institute (ATRI), average truck operating costs reached $2.336 per mile in 2025. Therefore, improving truck utilization remains an important goal for logistics companies.
In this guide, we explain how empty miles affect trucking operations. You’ll also learn seven practical ways to reduce deadhead costs using AI and smarter freight planning.
What Are Empty Miles in Trucking?
Empty miles in trucking refer to the distance a truck travels without carrying paying freight. These trips are also known as deadhead miles.
For example, a truck delivers a shipment from Chicago to Indianapolis. After unloading, the driver travels 100 miles to collect another load. Those 100 miles count as empty miles.
Although the trailer is empty, the carrier still pays operating expenses. These costs can include fuel, maintenance, tires, and driver compensation.
As a result, excessive deadhead miles can reduce the profit earned from completed shipments.
Why Are Empty Miles a Problem?
Empty miles create several operational challenges.
First, they increase the distance trucks must travel to generate revenue. As a result, carriers spend more money supporting the same volume of paid freight.
Second, empty travel reduces available driving time. This leaves fewer hours for profitable shipments.
Furthermore, unnecessary miles increase equipment wear. They can also raise fuel consumption and emissions.
For these reasons, reducing empty miles is an important part of freight cost optimization.
Deadhead Miles vs. Backhaul: What’s the Difference?
Deadhead miles and backhaul loads are closely related. However, they describe different transportation activities.
Deadhead miles occur when a truck travels without paying freight.
A backhaul is a load carried during a return or repositioning trip.
For example, a carrier transports freight from Dallas to Houston. Instead of returning empty, the dispatcher finds another shipment going toward Dallas.
Consequently, the truck generates revenue during its return journey.
This approach can improve equipment utilization. In addition, it may reduce the number of miles traveled without freight.
However, not every backhaul is profitable. Dispatchers must also consider freight rates, pickup distance, delivery schedules, and operating costs.
Therefore, successful backhaul planning requires more than finding an available shipment.
How Much Do Empty Miles Cost Trucking Companies?
Empty miles create expenses without directly generating freight revenue. However, their financial impact varies across carriers.
According to ATRI’s 2026 report, the average cost of operating a truck reached $2.336 per mile in 2025.
This figure provides useful industry context. However, it does not represent the exact savings from eliminating one empty mile.
Actual savings depend on fuel costs, driver compensation, equipment expenses, and other operating conditions.
Example: Calculating Empty Mile Costs
Consider a trucking fleet with 20 trucks.
Each truck travels 100,000 miles annually. Additionally, assume that 15% of those miles are empty.
The calculation looks like this:
| Metric | Annual Value |
|---|---|
| Number of trucks | 20 |
| Miles per truck | 100,000 |
| Total fleet miles | 2,000,000 |
| Empty mile percentage | 15% |
| Total empty miles | 300,000 |
| Industry-average cost per mile | $2.336 |
| Allocated operating cost | $700,800 |
The calculation is:
300,000 × $2.336 = $700,800
This example shows the operating costs associated with empty mileage under the stated assumptions.
However, the full amount is not necessarily recoverable. Some expenses remain even when a truck travels fewer miles.
Therefore, carriers should calculate potential savings using their own avoidable costs.
What Happens When Empty Miles Decrease?
Suppose the same fleet reduces its empty-mile percentage from 15% to 10%.
If total annual mileage remains unchanged, empty miles fall from 300,000 to 200,000.
That represents a reduction of 100,000 empty miles.
At the illustrative rate of $2.336 per mile, the difference equals $233,600 in allocated operating costs.
Actual savings will depend on how the fleet changes its operations. Nevertheless, the example shows why better freight planning can create meaningful financial opportunities.
How to Calculate Empty Mile Percentage
Measuring empty miles helps logistics companies understand fleet performance.
Fortunately, the formula is simple.
Empty Mile Percentage = (Empty Miles ÷ Total Miles) × 100
For example, imagine a truck completes 10,000 miles during one month.
Of those miles, 8,500 are loaded. The remaining 1,500 miles are empty.
The calculation is:
(1,500 ÷ 10,000) × 100 = 15%
Therefore, the truck’s empty-mile percentage is 15%.
What Is a Good Empty Mile Percentage?
There is no single target for every trucking company.
For instance, specialized carriers may face different conditions than general freight carriers.
Likewise, some routes offer more backhaul opportunities than others.
Equipment requirements, freight demand, and customer locations also affect results.
Therefore, companies should compare their performance with similar operations. They should also monitor changes over time.
Why Do Trucks Run Empty?
Several factors contribute to empty miles in trucking.
Uneven Freight Demand
Some regions produce more outbound shipments than inbound shipments.
As a result, carriers may struggle to find return loads.
Limited Load Visibility
Dispatchers often search multiple load boards and communication channels.
Consequently, they may miss suitable freight opportunities.
Equipment Restrictions
Not every truck can transport every shipment.
For example, refrigerated freight requires appropriate equipment. Therefore, an available dry van cannot accept every nearby load.
Scheduling Conflicts
Pickup appointments may not match driver availability.
Additionally, hours-of-service rules can limit available driving time.
Disconnected Logistics Systems
Shipment information may be stored across emails, documents, and transportation systems.
As a result, matching available trucks with suitable loads becomes more difficult.
These challenges highlight the need for better freight visibility and planning.
7 Ways to Reduce Empty Miles in Trucking
Reducing empty miles requires a combination of technology and operational planning.
The following strategies can help carriers and freight brokers improve efficiency.
1. Use AI-Powered Freight Matching
AI-powered freight matching helps logistics teams identify suitable loads for available trucks.
Traditional matching often requires manual searches. In contrast, AI-assisted systems can evaluate several factors at once.
These factors may include:
- Truck location
- Pickup distance
- Equipment type
- Available capacity
- Delivery requirements
- Driver availability
- Deadhead distance
For example, a nearby load may offer better overall value than a higher-paying load located far away.
By comparing these factors, AI can help dispatchers prioritize suitable opportunities.
As a result, companies may reduce unnecessary repositioning and improve truck utilization.
2. Improve Backhaul Planning
Backhaul planning helps carriers find paying freight for return journeys.
Instead of waiting until delivery is complete, dispatchers can search for return loads earlier.
For example, a truck traveling from Atlanta to Nashville may find another shipment heading toward Atlanta.
Consequently, the return trip can generate additional revenue.
However, dispatchers should evaluate pickup distance and scheduling requirements before accepting a backhaul.
A profitable backhaul should improve overall trip economics, not simply eliminate empty miles.
3. Compare Revenue per Total Mile
Many carriers evaluate loads using revenue per loaded mile.
However, this approach ignores empty repositioning.
A better comparison includes both loaded and deadhead miles.
Revenue per Total Mile = Freight Revenue ÷ Total Trip Miles
Consider two load opportunities.
| Metric | Load A | Load B |
|---|---|---|
| Freight revenue | $1,800 | $1,650 |
| Loaded miles | 600 | 550 |
| Deadhead miles | 150 | 25 |
| Total miles | 750 | 575 |
| Revenue per total mile | $2.40 | $2.87 |
Load A offers higher gross revenue. However, Load B generates more revenue per total mile.
Therefore, Load B may provide better trip economics.
Nevertheless, dispatchers should also review fuel costs, driver time, tolls, and future load opportunities.
4. Improve Real-Time Truck Visibility
Accurate truck location data supports better dispatch decisions.
For example, a dispatcher can identify available trucks near an upcoming pickup.
In addition, real-time updates help teams understand when equipment becomes available.
This information can reduce unnecessary searches and improve planning.
However, location alone does not determine whether a load is suitable.
Equipment requirements, pickup times, and driver availability must also be considered.
5. Build Balanced Freight Networks
Some transportation lanes create repeated empty return trips.
Therefore, carriers should review historical shipment patterns.
This analysis can identify regions with limited backhaul demand.
Additionally, companies can explore relationships with shippers that move freight in the opposite direction.
For example, one customer may generate regular outbound shipments. Another customer may offer compatible return freight.
Together, these relationships can support more balanced transportation networks.
As a result, carriers may reduce dependence on last-minute load searches.
6. Automate Freight Document Processing
Freight planning depends on accurate shipment information.
However, logistics teams often receive load details through emails, PDFs, and rate confirmations.
Manual data entry can slow down the planning process. It can also introduce errors.
Document automation helps convert freight information into structured records.
For example, automated systems can extract:
- Pickup locations
- Delivery destinations
- Appointment dates
- Equipment requirements
- Shipment references
- Load information
As a result, dispatchers can access more consistent data.
FreightGraph AutoFill supports this approach by helping logistics teams reduce repetitive freight data entry.
Learn more about FreightGraph AutoFill.
7. Track Fleet Performance Metrics
Measuring performance is essential for reducing empty miles.
Therefore, carriers should monitor several key indicators.
Empty Mile Percentage: Measures the share of total miles traveled without paying freight.
Revenue per Total Mile: Shows how much freight revenue is generated across all miles driven.
Loaded Mile Utilization: Measures the percentage of total miles driven with revenue-generating freight.
Average Repositioning Distance: Tracks the empty distance traveled before the next pickup.
Trip Contribution Margin: Compares trip revenue with the costs directly associated with completing that trip.
Together, these metrics provide a clearer picture of transportation efficiency.
Furthermore, regular reporting helps teams identify recurring problems and measure improvements.
How AI Freight Matching Reduces Deadhead Costs
AI freight matching improves how logistics teams compare available loads with available trucks.
Instead of relying only on manual searches, dispatchers can use technology to evaluate more options.
Location-Based Matching
AI-assisted systems can identify loads near available trucks.
As a result, dispatchers can prioritize shipments requiring less empty travel.
Equipment-Based Matching
Matching systems can also consider trailer types and shipment requirements.
For example, a refrigerated load requires compatible temperature-controlled equipment.
Therefore, equipment filtering helps eliminate unsuitable options.
Schedule-Based Matching
Pickup and delivery windows influence load selection.
In addition, driver availability and hours-of-service limits affect feasibility.
By considering these constraints, matching systems can help identify practical opportunities.
Cost-Based Load Evaluation
Freight matching can also support comparisons between revenue and travel distance.
For instance, a dispatcher may compare two shipments with different rates and pickup locations.
Consequently, the team can make a more informed decision about total trip value.
However, AI recommendations depend on accurate data and appropriate business rules.
Human oversight remains important, especially when schedules or operating conditions change.
AI Freight Matching vs. Traditional Freight Matching
Traditional freight matching often involves manual searches and direct communication.
In contrast, AI-assisted matching can organize and compare more information automatically.
| Feature | Traditional Matching | AI-Assisted Matching |
|---|---|---|
| Load discovery | Manual searches | Automated filtering |
| Truck location | Manual review | Location-based matching |
| Equipment checks | Manual verification | Automated compatibility checks |
| Deadhead distance | Manual calculation | Automated distance comparison |
| Scheduling | Dispatcher review | Constraint-based matching |
| Load evaluation | Manual comparison | Data-driven recommendations |
| Final approval | Dispatcher | Dispatcher |
Both approaches require reliable shipment information.
However, automation can reduce repetitive work and help dispatchers focus on higher-value decisions.
Real-World Example: Reducing Deadhead Through Better Load Selection
Consider a hypothetical dry van carrier with an available truck in Columbus, Ohio.
The dispatcher identifies two possible shipments.
Option A
- Freight revenue: $1,600
- Loaded distance: 500 miles
- Deadhead distance: 110 miles
- Total distance: 610 miles
Revenue per total mile:
$1,600 ÷ 610 = $2.62
Option B
- Freight revenue: $1,500
- Loaded distance: 470 miles
- Deadhead distance: 20 miles
- Total distance: 490 miles
Revenue per total mile:
$1,500 ÷ 490 = $3.06
Option A generates $100 more revenue.
However, Option B requires 120 fewer total miles.
Therefore, Option B generates more revenue per total mile.
Now assume an illustrative avoidable operating cost of $1.20 per mile.
Option A produces:
$1,600 − (610 × $1.20) = $868
Option B produces:
$1,500 − (490 × $1.20) = $912
Under these assumptions, Option B provides $44 more contribution.
This example demonstrates the value of considering deadhead distance during load selection.
Nevertheless, actual profitability also depends on driver time, tolls, waiting periods, and future freight opportunities.
Can AI Eliminate Empty Miles Completely?
AI can help reduce unnecessary empty travel. However, eliminating every empty mile is generally unrealistic.
Some repositioning is necessary because of freight demand, equipment requirements, and scheduling restrictions.
For example, a carrier may need to travel to another market to find profitable freight.
Additionally, accepting a low-paying backhaul is not always the best decision.
Sometimes, repositioning to a stronger freight market creates better future opportunities.
Therefore, the goal should be to reduce unnecessary empty miles while protecting overall profitability.
AI can support this goal by improving freight visibility and load selection.
How FreightGraph Supports Smarter Freight Operations
Reducing empty miles requires accurate data and better coordination between freight demand and available trucks.
FreightGraph focuses on applying AI and automation to logistics workflows.
Its freight matching approach supports more informed decisions about load and truck compatibility.
Meanwhile, document automation helps logistics teams organize shipment information and reduce manual work.
Together, these capabilities support more efficient freight operations.
For example, structured load information can help teams compare pickup locations, equipment requirements, and available capacity.
As a result, dispatchers can spend less time organizing information and more time evaluating suitable shipments.
Actual improvements depend on freight availability, operational conditions, and system implementation.
Explore more about AI in Freight Operations.
Frequently Asked Questions About Empty Miles in Trucking
What are empty miles in trucking?
Empty miles in trucking are miles traveled without paying freight. They are also called deadhead miles. As a result, these trips create operating expenses without directly generating freight revenue.
How do you calculate deadhead miles?
Deadhead miles are the distance a truck travels without a revenue-generating load. For example, driving 80 miles from a delivery location to the next pickup creates 80 deadhead miles.
What causes empty miles in trucking?
Common causes include uneven freight demand, limited load visibility, equipment restrictions, and scheduling conflicts. In addition, disconnected logistics systems can make matching more difficult.
How can trucking companies reduce empty miles?
Companies can improve backhaul planning, use AI freight matching, and track truck availability. Furthermore, better load selection and network planning can reduce unnecessary repositioning.
Can AI reduce deadhead miles?
Yes, AI can help identify suitable freight opportunities based on location, equipment, and timing. However, actual reductions depend on freight availability and operational decisions.
What is backhaul optimization?
Backhaul optimization involves finding profitable loads for return or repositioning trips. As a result, carriers may improve equipment utilization and reduce empty travel.
Is the highest-paying load always the most profitable?
No. A higher-paying load may require more empty travel or additional operating expenses. Therefore, carriers should compare total trip costs and contribution margins.
What is a good empty-mile percentage?
There is no universal target. Instead, carriers should compare results with similar operations and monitor their performance over time.
Conclusion: Reduce Empty Miles and Improve Trucking Profitability
Empty miles in trucking create ongoing financial and operational challenges.
Every unnecessary trip consumes fuel, driver time, and equipment capacity.
However, better freight planning can help carriers reduce these costs.
AI-powered freight matching supports smarter load selection by considering truck location, equipment requirements, and scheduling constraints.
In addition, backhaul planning and accurate shipment data can improve transportation efficiency.
Therefore, logistics companies should combine technology with clear performance metrics and experienced dispatching.
The goal is not simply to move trucks more often. Instead, it is to move freight more profitably.
Improve Freight Efficiency With FreightGraph
Better freight decisions begin with accurate information.
FreightGraph helps logistics teams explore AI-powered freight matching and document automation.
By improving access to structured freight data, companies can support faster planning and more informed operational decisions.