Highlights
Deadhead miles create a perennial problem for the logistics and transportation industry.
The business of transportation is to ferry goods or people from point A to B for a certain fee. The costs of transportation include leasing or buying, running, operating, and maintaining the fleet. To earn revenue, you must carry cargo and people. If we don’t have cargo, we don’t have revenue. Hence, headhaul, backhaul, and dead-haul are very important concepts in transportation.
Headhaul, or the outbound route, is where demand exceeds supply. So in a free-market economy, transporters controls prices simply because there is not enough vessels or trucks to carry cargo. Time is vital in transportation because the commodity could be perishable, seasonal, or is an input to a process to create another product. Besides, faster movement that saves on time is what all stakeholders prefer.
Backhaul is the return journey: bringing back your equipment to the origin for the next run. In the case of backhaul, the buyers are in control because supply exceeds demand. The truckers need cargo (paid miles) to get back to base so that it can cover the expenses.
Deadhaul (empty equipment) is a net loss–no cargo, so vehicle returns to base without revenue. The aim of every transporter is to minimise deadhaul: at least aim for breakeven. The alternative is to price deadhead miles into headhaul run so that backhaul trip costs are covered.
Current trends indicate that the transportation industry faces challenges due to imbalance of trade, ongoing geopolitical concerns, and trade wars
Our experience in transportation indicates that carriers likely face:
Deadhaul is a drag on profitability.
It causes many challenges to a business:
The opportunity is to optimise the entire transportation process using artificial intelligence and machine learning (AI-ML).
While deadhead miles are a substantial industry problem, there is an opportunity to leverage artificial intelligence and machine learning to solve it.. Transportation inefficiencies are created by fragmented decisions in demand forecasting, route planning, capacity allocation, pricing, and asset scheduling—often managed in silos and based on static rules or human judgment. AI-ML enables these elements to be connected and optimised as a single system by learning from historical movement patterns, trade imbalances, demand cycles, route behaviours, and carrier preferences. Instead of reacting to empty returns after they occur, AI-ML can predict where demand will emerge, where capacity will be stranded, and how assets should be positioned proactively to maximise utilisation. This shifts transportation management from manual, experience-driven planning to predictive, data-driven optimisation—reducing deadhead and idle time, improving margins, and simultaneously lowering fuel consumption and emissions across the end-to-end logistics value chain.
The technology behind ride hailing apps and digital freight matching can address the problem of deadhaul.
Ride-hailing apps have solved the problem for personal travel to a large extent. Anywhere in the city you could be close to your customer who connects to you through ride-hailing apps. We cannot eliminate deadhead miles, but with these apps, the driver can drastically reduce them.
Digital freight matching is a technology-driven approach to reducing deadhead miles by intelligently connecting shipper demand with available carrier capacity. Much like ride-hailing platforms, digital freight matching platforms create an open marketplace where loads and vehicles can be matched in near real time based on location, route, timing, and capacity. Instead of carriers manually searching for return loads or relying on brokers and fragmented networks, technology enables faster discovery of suitable cargo, improving the likelihood of paid backhauls. When enhanced with AI-ML, digital freight matching can move beyond simple search and bidding to predictive recommendations—anticipating demand patterns, positioning capacity proactively, and optimising utilisation across the network. This results in fewer empty miles, better asset productivity, lower operating costs, and reduced emissions, making digital freight matching a foundational enabler of smarter, more sustainable logistics.
AI-ML provides us with an opportunity to solve the problem of deadhead miles.
Digital freight matching and ride-hailing apps build a case for AI-ML investment. Today, digital freight matching tools still depend on a carrier to search for available shipper cargo and match it up. By leveraging AI and ML, the effectiveness of these technologies can be improved. Algorithms can study parameters critical to the business, like load size, route, carrier preferences, and capacity. As a result, we have a better understanding of the demand and supply, commodity types, and availability of equipment (vehicles) which can provide a dynamic pricing model to improve the process of matching and also squeeze every available mile. By studying patterns, AI can create customer and carrier delight by proactively matching the most profitable cargo for the carrier and the best price for the cargo owner, with the ultimate goal of zero deadhead.