How efficient load matching can reduce deadhead miles in trucking
By: Kilian Heilmann
Tags: Trucking, Bundling, Deadhead, Uber, Freight

Empty miles make up a significant share of all miles driven in the trucking sector. In this article, I investigate how to measure deadhead on the Uber Freight platform and show that algorithmic load bundling can decrease deadhead miles by about 22.6%.

What is deadhead?

Deadhead miles (or empty miles) are miles driven by a truck with an empty trailer carrying no loads. Deadhead is a true inefficiency that is borne by all participants in the freight industry: carriers do not earn money on empty miles, but spend time and money on gas and other operating costs. Carriers generally pass some or all of the costs of deadhead miles to shippers. Lastly, society loses out through the negative impact on the environment through congestion, air pollution and greenhouse gas emissions created by wasteful empty miles.

The prevalence of deadhead in the freight sector is difficult to assess, and representative data on the share of empty miles is hard to come by. While self-reported deadhead tends to be in the range of 10–15% of all miles driven, survey estimates tend to show larger shares of empty miles in the range of 15–30%, amounting to tens of billions of empty miles per year.¹ In the analysis below, I will use data from the Uber Freight platform to put hard numbers on the extent of deadhead in the trucking sector.

Causes of deadhead

The reasons for deadhead are manyfold and it helps to distinguish two major causes. Some deadheading is the natural consequence of geographic dispersion of shipping facilities and imbalances in shipment flows across space. For example, produce season can lead to a large demand for outgoing loads in one area that cannot be matched with incoming shipments.

In addition, part of the empty miles problem is caused by inefficient route planning. Choosing the best itinerary out of thousands of available loads can quickly become a very complex search and matching problem. Carriers have various ways to solve this problem, and decisions are largely impacted by the size of their fleet. While larger fleets have the scale to invest in route optimization technology, smaller carriers often lack the visibility of all available loads to create efficient routes.

What is algorithmic load bundling?

Uber Freight is tackling this matching problem with algorithmic load bundling introduced in September 2019. This feature allows carriers on the Uber Freight app to book a bundle consisting of two loads. This combination brings them back to where they started from as efficiently as possible. The two loads are combined algorithmically to minimize deadhead and to maximize paid time on a trip.

Example of an algorithmic bundle from Houston to Dallas/Fort Worth, and back to Houston

Algorithmic load bundles reduce the need for carriers to browse through all available loads and combine them into trips. The figure above shows an example of an algorithmic bundle that was booked on Uber Freight’s app. The first load (also called the headhaul) is picked up in Houston and dropped off just north of Fort Worth. The second load (called the backhaul) is then picked up about 30 miles away in Southeast Dallas and dropped off back in Houston. The overall trip accounts for 563 miles, and thus only about 5.3% of all miles after the initial pickup are driven empty — or in other words, the round-trip achieves an utilization of 94.7%.² How do these numbers compare to typical deadhead rates and how can algorithmic bundling help carriers to improve utilization?

Measuring deadhead on the Uber Freight platform

Evaluating the effect of algorithmic load bundles requires a consistent measure of deadhead, i.e. the number of empty miles on a trip. However, deadhead is not explicitly measured on the platform; I only observe loads booked with Uber Freight, and cannot track activity of trucks when they are not performing a load on the platform. I therefore need to approximate the deadhead of round-trips from observed patterns in the data. I do this by measuring driving distances between loads that are likely driven empty.³

Diagrammatic representation of deadhead measure

The diagram above illustrates my deadhead metric. What is observed in the data are the two loaded trips A-B and C-D. I quantify destination-market deadhead miles as the distance between the drop-off location of the first load (B) and the pick-up location of the second load (C). Similarly, I estimate source-market deadhead as the distance between the drop-off location of the second load (D) and the pick-up location of the first load (A). This part consists of two deadhead trips that take place during different times: the initial empty trip from the truck parking position (E) to the pick-up (A), and the unloaded miles to return from the drop-off (D) back to the truck parking position (E). Since E is not observed in the data, I use the distance between D and A as the lower bound of the total source-market deadhead.

Research design

What is the impact of algorithmic bundles on carriers’ empty miles? To evaluate the causal effect, I need to construct a counterfactual for deadhead that carriers would have incurred in absence of the feature. As the counterfactual, I look at what I call organic bundles. These are manually booked load combinations that look just like algorithmic bundles: they start and end in the same market, they are performed within a reasonable time frame, and they do not allow enough time in between to carry an unobserved load. These conditions ensure that the round-trip is taken completely on the platform and that I can use the methodology above to measure its deadhead miles.

A naive approach would compare deadhead miles of algorithmic bundles to those of organic bundles (or to industry averages) in the same period. Such a contemporaneous comparison, however, would be misleading because of selection bias. The bundling algorithm takes utilization into account when creating bundles, and thus the treatment group in this comparison has low deadhead by design. The less efficient load combinations with higher deadhead will then necessarily be in the group of organic bundles. With the algorithmic bundles being positively selected, the treatment effect of this naive analysis will overestimate the true effect of the bundling technology.

To solve these issues and to create a fair comparison group for algorithmic bundles, I implement a before-after design: I compare empty miles of algorithmic bundles of carriers who use the feature against those same carriers’ organic bundles before the feature became live. In other words, I ask the question: how much more efficient are algorithmic bundles compared to the round-trips that the same carriers booked before the feature was available? My design solves the selection issue between the treatment and the control group, and provides a causal estimate of the algorithmic bundling feature on deadhead incurred by carriers.

Geographic distribution of algorithmic and organic bundles

Results and conclusion

The before-after comparison between the deadhead of algorithmically bundled loads and organic bundles before the feature was live shows a decrease in deadhead of about 17.2 miles per round-trip. These results hold up even if I statistically control for different origin-destination combinations. This is important as the geographic extent of algorithmic bundles and organic bundles does not match perfectly (as depicted in the map above).

This effect represents a 22.6% decrease in empty miles compared to what carriers booked before algorithmic bundling was available, equivalent to an increase in truck utilization by 4.2 percentage points from 81.6% to 85.8% on absolute scale. Overall, the results highlight how a matching technology like algorithmic bundling can reduce wasteful deadhead miles, lower greenhouse gas emissions,⁴ and maximize the time carriers earn money on their trips.