Empty-Mile Repositioning Evidence Research 2026

Research question: How can a small trucking operation evaluate an empty-mile repositioning decision when the next load, waiting time, service commitment, and return opportunity are uncertain? Empty miles are often treated as a failure in isolation, although a reposition can protect a customer appointment or place equipment near stronger freight. A defensible review needs the decision context, not only the odometer.

Method and evidence scope

This study compares Bureau of Transportation Statistics freight data, Federal Highway Administration freight-performance material, Energy Information Administration transportation energy data, FMCSA hours-of-service resources, and Transportation Research Board publications. They provide national context, planning concepts, fuel context, regulatory boundaries, and research methods. They do not produce a benchmark for one carrier's margin or prove that a repositioning move was correct. The analysis is a framework for reconstructing local decisions.

Define the movement before scoring it

Record the location and time at the decision, the vehicle's available hours, the committed delivery, the offered next load, the likely empty distance, the loaded distance, waiting alternatives, and the reason for choosing a move. Include what was known then, not only the outcome later. This prevents hindsight from turning a reasonable protection move into an avoidable deadhead or making a lucky outcome look like a repeatable method.

Revenue per loaded mile is incomplete

A lane comparison should distinguish loaded miles, empty miles, total trip miles, driving time, waiting time, tolls, fuel assumptions, and service risk. A load with a strong loaded-mile figure may consume a long reposition and a tight appointment. A lower apparent rate may reduce total empty distance or protect a recurring customer commitment. The operation should choose its own cost and priority rules, but publish the inputs beside the result so a reviewer can challenge the assumption.

Evidence for the counterfactual

To learn from a decision, retain the alternative that was considered: another load, a wait, a return, or no movement. Capture its quoted terms, expiration, pickup window, expected empty miles, and source. If no alternative was available, record that fact. External freight data can frame broader patterns, but it cannot reconstruct a local load-board moment after the offer disappears. A counterfactual marked unknown is more honest than a fabricated comparison.

The record should also preserve the clock behind the choice. Note when the offer was received, when the dispatcher answered, and when the truck could realistically arrive. Those timestamps distinguish a missed opportunity from an option that was never feasible, especially when a short appointment window or lawful driving limit narrowed the choices.

Operational measures that travel well

Measure empty miles as a share of total miles, reposition miles by reason, time from unload to next dispatch, accepted-load conversion after reposition, and the percentage of moves with a recorded alternative. Review these by lane, equipment type, week, and customer commitment. Avoid a single target that encourages unsafe or commercially irrational moves. Pair the mileage measure with service outcomes and lawful available time.

What a pattern might mean

Repeated repositioning toward one market may indicate a deliberate network strategy, weak backhaul planning, or an over-optimistic assumption about available freight. Repositioning after late tender notices may point to customer coordination rather than lane quality. A high empty-mile share with incomplete decision notes says first that evidence is weak. These are hypotheses to test with local records, not causal findings derived from national sources.

Separate planning quality from freight luck

An accepted load is not automatically evidence that the preceding move was well planned, and a rejected offer is not automatically evidence that it was poor. Review the forecast and realized outcome separately. Record whether the next load was known, probable, or speculative when the truck moved, and whether a customer commitment constrained the choice. Over several weeks, this lets an operator test whether the planning signal is improving without rewarding decisions simply because the market happened to move favorably.

Hours, safety, and decision ownership

FMCSA hours-of-service rules set constraints on what a driver can lawfully do; they do not prescribe a profitability choice. Dispatch administration can assemble offers, distances, appointments, and timestamps. The accountable operations leader must weigh service, safety, equipment, and commercial risk. No optimization score should pressure a driver to exceed lawful limits or ignore a required rest period.

Limitations and role boundaries

Fuel prices, weather, congestion, load availability, customer penalties, and equipment constraints change quickly. EIA and freight datasets are context, not a live quote or local cost model. This report does not promise a savings percentage, set a rate floor, or recommend a specific route. Preserve the decision snapshot, record uncertainty, and review the pattern with the person authorized to change planning policy.

Evidence-led conclusion

Empty-mile analysis becomes useful when each move is evaluated against the information and alternatives available at the time. BTS and FHWA sources help define freight context, EIA informs fuel assumptions, and FMCSA sets the operating boundary; none can decide a carrier's local tradeoff. The evidence-led conclusion is to measure reposition reason, total movement, counterfactual quality, and service result together rather than labeling every empty mile a loss.

Source register

1. BTS freight transportation, Bureau of Transportation Statistics, accessed 2026-08-25.
2. FHWA freight performance measures, Federal Highway Administration, accessed 2026-08-25.
3. EIA transportation energy data, U.S. Energy Information Administration, accessed 2026-08-25.
4. FMCSA hours of service, Federal Motor Carrier Safety Administration, accessed 2026-08-25.
5. Transportation Research Board, National Academies of Sciences, Engineering, and Medicine, accessed 2026-08-25.