The document discusses how technology, specifically a centralized matching system developed by a digital freight broker, can reduce 'deadhead' miles in the trucking industry, which represent empty miles driven without a load. By utilizing a load bundling algorithm, the platform was able to decrease average deadhead miles by 22.6% per round-trip, potentially reducing greenhouse gas emissions by 2.2% per trip. If adopted industry-wide, these improvements could result in a significant reduction of approximately 9.6 million metric tons of emissions annually.

1. Decarbonizing Transport: LightNing Talks
Overcoming Deadhead in the Trucking Industry Using
Efficient Load Matching
Kilian Heilmann, Lyft Inc.

2. Deadhead in the trucking industry
● Deadhead = miles a truck drives empty without a load: wasteful
● Deadhead has negative externalities through congestion and air pollution
● Why do trucks drive empty?
○ Natural deadhead due to geographic and temporal dispersion of economic activity:
there is no load
○ Deadhead due to information frictions creating matching inefficiency: truckers can’t find
a load
● Question: How can technology and marketplace design solve the matching problem?
● Today, I will evaluate the effect of a centralized matching technology developed by a digital
freight broker on deadhead in the trucking industry

3. Background: Truck transportation market
● Very fragmented market with...
○ …millions of shipments (truck loads)
○ …half a million trucking companies
○ …over 17,000 registered brokers
● Uber Freight is a tech-enabled freight broker
connecting shippers to truckers using a mobile app
● The Uber Freight platform processes hundreds of
thousands of truck loads
→ Potential for centralization of the matching
process

4. Load bundling
● Uber Freight introduced feature in
September 2019
● Bundling algorithm creates low-deadhead
combinations of two truck loads to form
round-trips (called bundles), and allows
truckers to book them together
● Advantage: No need for truckers to
browse through thousands of available
loads to create efficient routes themselves

5. Measuring deadhead from the data
● I don’t measure deadhead directly, but we can
infer it from the data
● I only observed loaded miles
● Focus on users that primarily use the Uber
Freight platform and measure deadhead as
distances that are likely being driven empty
● Average deadhead in the Uber Freight data =
18%
● How to evaluate the feature in the presence of
network effects?

6. Evaluating load bundling: Counterfactual
● Basic idea: Compare bundles to round-trips that
were booked without the feature
● Since the algorithm only picks low-deadhead
trips, treatment group is positively selected
● Instead, I go for a before-after comparison and
compare deadhead of drivers that use bundling to
their previous deadhead on the same routes
● Results: Average deadhead decreases by
17 miles or 22.6% per round-trip (even when
controlling for drivers and location fixed effects)

7. Environmental consequences
● Trucking sector in the US has a massive environmental impact (436.5m tons of GHG in
2017) and the potential to decrease greenhouse gas emissions is huge
● We can use emissions factors to translate avoided miles into avoided greenhouse gas
emissions (Assumption: emissions are proportional to ton-mileage, 161.8 g/ton-mile)
● Back-of-the-envelope calculation:
○ Bundling reduces deadhead by about 17 miles
○ A typical trip is around 415 miles long, of which 76 are deadhead
○ Driving empty emits about half the emissions of driving with an average load
○ 17 * EF_empty / ((415-76) EF_full + 76 EF_empty) = 0.022 metric tons per trip
● Bundling reduces per-trip emissions by .02 metric tons or 2.2%
● If the whole industry were to reduce emissions by 2.2%, this would translate to about
9.6 million metric tons per year

8. Thank you very much