For an industry that moves the global economy, freight logistics still struggles with one of its most basic inefficiencies: empty trucks on the road.

Deadhead miles also known as empty miles represent one of the most pervasive yet under-addressed problems in freight. Every year, fleets around the world burn billions of gallons of fuel hauling nothing. These miles generate no revenue. They drain profits. They emit unnecessary carbon. And they persist not because of technology gaps, but because of coordination gaps.

But coordination is exactly where AI thrives.

Understanding the Scope of the Problem

Let’s quantify it: in the U.S. alone, 25% to 30% of all truck miles are driven empty. That means nearly one-third of the time, trucks are traveling with zero cargo and full operating costs.

Why does this happen?

A driver completes a delivery in one city.

Their dispatcher or broker is still looking for a backhaul.

There’s no nearby load prearranged.

The driver moves to another city empty.

And the cycle repeats.

These empty trips are rarely planned but they are extremely common. And across thousands of vehicles, the waste adds up fast.

Why Traditional Solutions Fall Short

Dispatching has traditionally been reactive. Human coordinators wait for a truck to become available, check nearby loads, and assign the next job manually. But this approach has two major flaws:

1. It’s slow. By the time a match is made, the driver may already be in motion.

2. It’s fragmented. Carriers, shippers, and brokers operate in silos, limiting data visibility.

Even digital load boards haven’t fully solved this issue, because they still depend on manual decisions and post-event matching.

To truly reduce deadhead miles, the solution must be predictive, data-driven, and scalable.

The AI Shift: From Load Matching to Load Intelligence

Artificial intelligence has introduced a fundamental shift in how freight is managed not just through automation, but through insight. By integrating multiple data sources and continuously learning from behavior, AI enables real-time load orchestration across the entire freight network.

Key capabilities include:

1. Predictive Availability: AI forecasts when a truck will finish a delivery and where it will be, allowing systems to line up the next load in advance.

2. Geospatial Optimization: Using live GPS and traffic data, AI identifies routes that reduce travel time and emissions.

3. Driver Behavior Learning: AI understands driver habits, preferences, and schedules to make smarter assignments.

4. Multi-party Collaboration: AI can coordinate across multiple carriers and brokers, breaking down the data silos that cause inefficiencies.

This is how modern freight systems are moving from reactive matching to real-time, intelligent logistics flow.

Case Study Reference: TruckSync’s Role in Reducing Empty Miles

One of the clearest examples of AI-powered freight coordination comes from TruckSync, a digital freight platform featured in Valerii Khomynskyi’s 2025 article, “TruckSync: Transforming Freight Operations for a Sustainable Future.”

In the article, Khomynskyi explores how TruckSync’s use of AI and predictive analytics enables more efficient dispatching, especially when it comes to minimizing deadhead mileage. The platform:

I. Aggregates load board data and real-time vehicle positions

II. Uses machine learning to recommend optimal backhauls

III. Factors in weather, road conditions, and fuel efficiency metrics

TruckSync has helped reduce empty miles by up to 21% in certain fleet applications, significantly cutting both costs and emissions. The results are compelling not just for environmental reasons, but because they deliver immediate financial ROI using the vehicles companies already own.

Khomynskyi’s work positions TruckSync as a leading example of how AI coordination not just new fuels can drive the freight industry toward sustainable transformation.

Why This Matters: Practical Sustainability at Scale

Unlike long-term investments in alternative fuel or electric truck technology, AI-driven deadhead reduction is:

1. Cost-effective

2. Scalable

3. Deployable today

In many cases, the first sustainability win a carrier can claim doesn’t come from switching fuels, it comes from eliminating waste. Reducing deadhead miles by 15–25% can cut thousands of gallons of diesel use annually and drastically reduce a fleet’s carbon output all without any major capital expense.

For many carriers, especially those with tight margins or aging fleets, this is a more realistic and actionable path to green operations.

Looking Forward: What’s Next in AI Freight Coordination

As freight digitization accelerates, AI is evolving from a “nice-to-have” to a core operational requirement. Here’s what’s on the horizon:

Carbon-aware dispatching: AI that selects routes and loads not only for efficiency, but for carbon optimization

Federated learning across fleets: Shared intelligence that allows systems like TruckSync to improve collectively from usage across networks

I. Real-time ESG reporting: Automated sustainability metrics tied directly to load data and vehicle behavior

II. Elastic freight networks: AI creating dynamic, responsive networks of available capacity across regions

III. The carriers adopting these tools early are setting themselves up not just for compliance but for competitive dominance in a shifting market.

Conclusion: AI Isn’t Replacing Trucks: It’s Empowering Them

The logistics world is at a tipping point. Emissions standards are tightening, shippers are demanding greener partners, and operational costs continue to rise. While everyone looks to electrification as the future, a quieter revolution is already delivering results.

Artificial intelligence is solving one of freight’s oldest inefficiencies empty miles by turning guesswork into coordination.

As Khomynskyi’s research into TruckSync illustrates, this approach works. It’s not hypothetical. It’s measurable. And it’s here now.

Sustainability isn’t always about changing what moves the truck. Sometimes, it’s about changing how the truck moves and why it moves at all.

Solving Freight’s Silent Problem: How AI Tackles Empty Miles at Scale