Empty Miles: The 15-25% Problem Costing Trucking Billions
15-25% of all miles driven by trucks are empty — no freight, no revenue. Here is how deadhead miles cost the industry billions annually and what AI is doing about it.
TRU LOAD Editorial
Industry Analysis
The Empty Miles Problem
Picture this: a truck hauling 40,000 pounds of freight from Dallas to Chicago on I-35. The driver delivers the load, collects the bill of lading, and then drives 80 miles — empty — to a pickup location where the next load is waiting. Those 80 miles generate zero revenue while burning fuel at $0.70/mile (ATRI), accumulating wear at $0.20/mile in maintenance, and consuming Hours of Service that could have been spent earning.
This is deadhead — the industry term for empty miles. And it is a massive, systemic problem.
Industry estimates put deadhead at 15-25% of total miles driven by trucks in the United States. On 11.46 billion tons of freight moved annually (ATA), the trucks hauling that freight drive billions of miles with empty trailers.
The Financial Impact
For Individual Drivers
An owner-operator running 120,000 miles per year at 20% deadhead drives 24,000 miles empty. At the average all-in operating cost of $2.27/mile (ATRI, 2023), those empty miles cost $54,480 per year — with zero revenue to offset them.
Even reducing deadhead from 20% to 15% saves 6,000 miles and approximately $13,620 annually. Reducing to 10% saves $22,700 annually. These are not theoretical savings — they flow directly to the bottom line.
For the Industry
With 3.54 million truck drivers in the US (BLS) and hundreds of billions of miles driven annually, the aggregate cost of deadhead runs into the tens of billions of dollars. Every empty mile also represents:
Why Deadhead Persists
Geographic Imbalances
Freight does not flow symmetrically. Some regions are net producers (export more freight than they receive) and others are net consumers. This creates structural imbalances where capacity to move freight inbound exceeds demand for outbound freight, and vice versa.
Classic examples:
Timing Mismatches
Even when freight exists in both directions, the timing does not always align. A driver delivering at 3pm may not find a suitable return load until the next morning, creating an overnight gap that consumes HOS without generating revenue.
Information Gaps
Traditional load boards show available loads, but they do not proactively optimize for deadhead reduction. A driver looking for loads from Chicago may see options in multiple directions without understanding which minimizes their overall empty miles across a multi-load trip plan.
Single-Load Thinking
Most load matching — even on modern platforms — focuses on the next load rather than the next three loads. A slightly lower-paying load that positions the driver for an excellent second and third load may generate more total revenue than the highest-paying immediate option followed by high deadhead.
How AI Reduces Empty Miles
Chain Load Matching
AI systems can evaluate not just the immediate load but the entire sequence of loads over the coming days. By looking ahead to what freight is available at each potential delivery destination, the system can recommend loads that minimize cumulative deadhead across a multi-day trip plan.
Example: Instead of taking a $3.00/mile load from Dallas to an area with limited outbound freight (resulting in 150 miles of deadhead to the next pickup), AI might recommend a $2.80/mile load from Dallas to a freight-rich area where the next load is available with only 15 miles of deadhead. Over two loads, the second option generates more total revenue and fewer empty miles.
Predictive Positioning
AI can analyze historical freight patterns to predict where capacity will be needed in the coming days and weeks. This allows proactive positioning — taking loads that move drivers toward areas of anticipated demand rather than chasing spot loads reactively.
Real-Time Market Visibility
AI systems continuously monitor load availability across the entire marketplace (500,000+ registered carriers, millions of active loads), identifying opportunities that a driver manually searching a load board would never find.
Driver-Specific Optimization
By learning each driver's preferences, equipment type, home base, and schedule, AI can optimize specifically for that driver — not just minimizing deadhead in general, but minimizing deadhead while also respecting home time preferences, rate minimums, and lane preferences.
The Environmental Angle
Every empty mile driven burns diesel and emits CO2 for zero economic output. With the freight industry under increasing pressure to reduce its environmental footprint, deadhead reduction is one of the highest-impact sustainability levers available.
If the industry could reduce average deadhead from 20% to 12% through AI optimization:
This is not hypothetical — it is measurable, achievable, and economically motivated. Reducing deadhead saves money AND reduces emissions. It is one of the rare win-win opportunities in sustainability.
Getting Started
For individual drivers and small carriers (91% of carriers have 6 or fewer trucks per FMCSA), reducing deadhead starts with three changes:
For every percentage point reduction in deadhead, an owner-operator running 120,000 miles saves approximately $2,724 annually at current operating costs. That adds up fast.
*Sources: American Trucking Associations (ATA), American Transportation Research Institute (ATRI, 2023), Bureau of Labor Statistics (BLS), Federal Motor Carrier Safety Administration (FMCSA)*