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Lightning Talk - Transport: Overcoming Deadhead in the Trucking Industry Using Efficient Load Matching
- 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
Editor's Notes
- I want to talk about an important problem in the trucking industry, namely deadhead. Deadhead is simply the amount of empty miles driven by a truck. These miles are wasteful, because they don’t serve any purpose, but they create negative externalities through congestion and air pollution Now why do trucks drive empty? There are two distinct reasons: There is spatial and temporal dispersion of economic activity. A trucker might just have delivered a shipment, and now wants to go home Then there is a very different reason caused by information frictions that create matching inefficiencies. Maybe there is actually a load, but the trucker can’t find them The question I want to ask today is: “How can… And “I will evaluate this effect”… After “can’t find a load”: This creates a matching problem
- I want to talk about an important problem in the trucking industry, namely deadhead. Deadhead is simply the amount of empty miles driven by a truck. These miles are wasteful, because they don’t serve any purpose, but they create negative externalities through congestion and air pollution Now why do trucks drive empty? There are two distinct reasons: There is spatial and temporal dispersion of economic activity. A trucker might just have delivered a shipment, and now wants to go home Then there is a very different reason caused by information frictions that create matching inefficiencies. Maybe there is actually a load, but the trucker can’t find them The question I want to ask today is: “How can… And “I will evaluate this effect”… After “can’t find a load”: This creates a matching problem
- Let me first talk a bit about how the truck transportation market works in the United States: -It is a highly fragmented market with hundreds millions of shipments every year. -There are about half a million trucking companies with millions of truck drivers competing for these shipments -And these million of actors are connected by over 17,000 registered freight brokers Here I will use data from Uber Freight. Uber Freight is literally Uber for Freight. It offers a mobile app that allows truckers to book loads right on the app. “It processes” And the technology has “potential for centralization…”
- This is exactly what Uber Freight built. Uber Freight introduced a feature called load bundling. Here, an “algorithm creates low-deadhead combinations …” This has the advantaged that…” “. Taking away the information friction Here is an example of a bundle: The first load goes from Houston to Dallas, then drives a little bit empty, and goes back to Houston. Overall, out of 563 miles, only 30 are driven empty
- So, how do we measure deadhead? Actually, we don’t, but we can infer it from the data
- Now, how do I evaluate the impact of the feature? First, we need to think about what is the counterfactual?
- I will spare you the exact calculations 9.6 million metric tons per year in the US