AI Over Fuel: Rethinking Freight Efficiency by Cutting Empty Miles, Not Just Emissions

For years, the conversation around sustainable freight has focused on what we power our trucks with. Diesel? Electric? Hydrogen? But perhaps we’ve been asking the wrong question. Instead of only focusing on what moves the truck, what if we rethought when and why it moves at all?

Behind the scenes of global logistics is a quiet but urgent problem: deadhead miles, the stretches where trucks drive empty, burning fuel, producing emissions, and generating zero revenue. This inefficiency exists at scale, and it’s not a hardware problem. It’s a data problem. Fortunately, artificial intelligence is beginning to fix it.

The Cost of Driving Empty

Every mile a truck drives costs money, fuel, labor, wear, insurance, opportunity. But when that truck isn’t carrying cargo, those costs don’t return value.

In the U.S. freight industry, up to 30% of all truck miles are classified as “empty miles.” That’s a staggering amount of fuel burned and carbon released into the atmosphere for no gain. For an industry under pressure to reduce both emissions and expenses, this isn’t just a logistical issue, it’s an operational failure.

Deadhead miles are a symptom of outdated coordination systems: fragmented load boards, manual dispatch, reactive planning. But with the logistics sector digitizing at a rapid pace, these inefficiencies are no longer inevitable.

Artificial Intelligence: From Load Matching to Load Forecasting

Modern freight management systems equipped with AI can now operate on an entirely new level. They don’t just find available shipments, they predict where loads will be, where drivers will end up, and what routes will deliver the most value.

Here’s how AI is transforming the freight ecosystem:

1. Predictive Load Assignment: AI uses machine learning to anticipate load availability based on past data, market fluctuations, and seasonal trends.

2. Dynamic Routing: AI can update route instructions based on real-time conditions, traffic, weather, incidents reducing both travel time and fuel use.

3. Smart Scheduling: AI optimizes delivery windows to minimize driver downtime, idle hours, and missed opportunities.

4. Carrier-Shipper Synchronization: By aligning shipper demand with available capacity in real time, AI creates a smoother logistics network with fewer gaps.

This transformation is making real-time freight orchestration possible and deadhead miles are being targeted as a key area for gains.

Evidence in Action: TruckSync and the Future of Intelligent Dispatch

In a 2025 industry article titled “TruckSync: Transforming Freight Operations for a Sustainable Future,” researcher Valerii Khomynskyi explores the impact of AI-driven logistics coordination on environmental and financial outcomes.

At the heart of the article is the TruckSync platform, an AI-powered freight management system that automates load matching and dispatch decisions. Unlike traditional systems, TruckSync isn’t waiting for a truck to return empty before looking for a load. It anticipates when and where capacity will free up and prepares the next shipment in advance.

According to the analysis, TruckSync helped reduce deadhead miles in mid-sized fleets by as much as 21%, translating into:

1. Fuel savings in the thousands of gallons

2. CO₂ emissions reductions measurable in tons

3. Fewer hours lost to idle vehicles

4. Improved profitability per route

It’s a clear case of how intelligent coordination, not new engines can bring freight closer to environmental and economic targets.

A Shift in Strategy: Cutting Emissions Without Changing the Truck

While low-emission trucks are an important long-term goal, they’re expensive and slow to scale. Most freight operators today still rely on diesel fleets often for good reason: availability, range, infrastructure.

AI offers these carriers a powerful interim solution: reduce emissions by reducing waste. If a truck drives 20% fewer empty miles, its net emissions go down, even if it still runs on diesel. And this can be done:

Without replacing a fleet

Without retrofitting vehicles

Without waiting for infrastructure to catch up

This approach flips the sustainability conversation on its head: emissions reductions can be driven by intelligence, not necessarily by hardware.

Long-Term Implications: AI as the Freight Industry’s Brain

As more logistics operations move into the cloud and integrate real-time data systems, AI is poised to become the central decision engine for freight. And it’s not just about empty miles.

We’re entering a new era of freight optimization that includes:

I. CO₂-aware load balancing, where carbon output becomes a factor in choosing routes

II. ESG reporting automation, driven by live emissions and fuel efficiency data

III. Collaborative logistics, where AI platforms help companies share capacity and avoid empty return trips

IV. Resilient routing, where machine learning models adjust operations in response to geopolitical, weather, or supply chain disruptions

In this future, AI isn’t just a feature, it’s the foundation.

Conclusion: Freight’s Most Valuable Miles Are the Ones You Don’t Drive

Sustainability in freight isn’t only about cleaner trucks. It’s about smarter choices. It’s about eliminating unnecessary movement, improving coordination, and maximizing value per mile.

As Khomynskyi’s research on TruckSync illustrates, AI is already proving capable of reducing deadhead miles and enhancing both economic and environmental outcomes. For logistics companies looking to evolve, AI isn’t just a tech upgrade, it’s a strategic lever for survival and leadership.

The question isn’t whether AI will shape the future of freight, it already is. The real question is how soon your operation will get on board.