Industry
Travel and Logistics
Data and AI in Travel Industry for a Memorable Passenger Experience
Highlights
- Deadhead miles are a systemic margin killer—empty or unproductive movements across trucks, ships, aircraft, and last-mile transport silently eroding profitability without generating revenue.
- Idle time equals lost money. Transport economics must optimise total asset utilisation, not just loaded miles, because every idle minute due to empty movements shortens asset life and destroys value.
- Artificial intelligence (AI) and machine learning (ML) can transform freight matching from search to prediction, intelligently pairing loads and capacity using routes, preferences, and historical behaviour to minimise deadhead by design.
- Reducing deadhead miles directly cuts fuel emissions, thus helping companies improve environmental, social, and governance (ESG) outcomes and focus on sustainability.
On this page
Introduction
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.
Trends
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:
- Imbalance of trade: Significant imbalance of trade between major economies is one of the most prominent trends in global transportation. For instance, there is disparity in the volume of goods shipped from China to the United States compared to the return leg. Chinese manufacturers export vast amounts of consumer goods, electronics, and machinery to the US. However, the demand for American exports back to China is comparatively lower, resulting in many shipping containers and vessels returning to China either partially loaded or completely empty on the backhaul. This phenomenon is not limited to China and the US; similar imbalances occur on routes such as Europe to Africa or Australia to Asia, where the flow of goods is heavily weighted in one direction, which increases deadhaul.
- Geopolitical concerns: Geopolitical events, such as wars and regional conflicts, have an adverse impact on shipping and logistics. For example, tensions in the Red Sea or the South China Sea can force commercial vessels to take detours, avoiding conflict zones, thereby increasing the time, distance, and costs associated with both outbound and return journeys. Because of the Russia-Ukraine conflict, many freight operators have been forced to reroute shipments around closed or unsafe territories, leading to increased fuel consumption and longer backhauls. This can also make it harder to secure cargo for the return trip, amplifying the risk of running empty. In addition, sanctions or trade restrictions often curtail the volume of goods allowed for export or import, exacerbating the issue of empty returns.
Challenges
Deadhaul is a drag on profitability.
It causes many challenges to a business:
- Deadhaul often fails to generate sufficient revenue to cover operating costs. Backhaul legs frequently face weak or inconsistent demand, forcing carriers to accept lower rates or return empty.
- An unproductive backhaul still incurs the full cost of fuel, labour, tolls, maintenance, and asset depreciation, turning what should be a revenue generating asset into a cost centre. When backhaul does not at least breakeven, it compels carriers to raise outbound prices or absorb sustained profitability erosion.
- Every empty or unproductive journey consumes fuel, generates emissions, and contributes to congestion without delivering any economic value. Whether it is a truck returning without cargo, a ship sailing with empty containers, or a cab driving back without a passenger, deadhaul directly increases carbon footprint per unit of freight moved. A single 168 km empty return trip by a cab can emit nearly 27 kg of CO₂, doubling the carbon footprint of the journey without generating any revenue or value.
Opportunities
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.
Solutions and benefits
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.
Conclusion
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.