Uber and Lyft publish research from 6 US cities, excluding NYC, that finds their cars cause less congestion than personal vehicles but impact core areas heavily
and half of the time, they're not even carrying passengers. https://www.citylab.com/... Jeff Bercovici / @jeffbercovici : Congratulations to Uber and Lyft for dropping this terrible news about how they're increasing traffic congestion on a day when no one cares https://www.citylab.com/... @citylab : In San Francisco County, Uber and Lyft make up as much as 13.4 percent of all vehicle-miles driven. https://trib.al/TLYZMFD pic.twitter.com/OBMqXYKtG7 NEO Sierra Club / @neosierraclub : There are 823.76 million VMT in these 6 cities daily. Based on Uber & Lyft's own data, they are responsible for adding 39.65M miles of driving to these cities' streets every single day. Oh, and that's also equal to 17,657 TONS of CO2 per day. https://twitter.com/... Kriston Capps / @kristoncapps : Uber and Lyft are responsible for 7 percent of traffic in D.C. and almost twice that in S.F. That's nothing compared to the national picture of vehicle miles traveled—but it's not nothing for cities struggling with congestion. Exclusive from @mslaurabliss: http://bit.ly/338LOKZ @citylab : Though Uber and Lyft are contributing to an increase in congestion in some cities, their share is still vastly dwarfed by personal vehicles, according to a new report. https://trib.al/TzL6f7f Laura Bliss / @mslaurabliss : Uber and Lyft have long been challenged by claims that they're making traffic worse. In a new analysis, exclusive to @CityLab, the ride-hailing companies respond with data that indicates that it's true. But they also point the finger at private cars. https://www.citylab.com/... Jim Charlier / @thecharlier : We have designed a surface transport system that rewards and encourages vehicular travel through massive investment programs and subsidies. We should not be surprised when smart tech entrepreneurs take advantage of our poor policy choices. https://www.citylab.com/... Jonathan Fertig / @rightlegpegged : Really hope this point doesn't get lost as this article ricochets around the web: “Private vehicles [are] the true culprits in overall traffic congestion, accounting for 87 to 99% of total VMT in the analyzed regions.ross the U.S.” https://www.citylab.com/...
Context & Ripple Effects
This is Uber and Lyft publishing their own numbers after two years of independent research doing the opposite: a [[a:941600|study found San Francisco congestion grew about 60% between 2010 and 2016 with ride-hail accounting for more than half the increase]], and an earlier [[a:931907|nine-city study tied services like UberPool to congestion by luring riders off transit and walking]]. By releasing six-city data themselves — excluding NYC — the companies get to frame the debate around their preferred comparison: ride-hail versus personal cars, not ride-hail versus transit.
The self-reported figures still concede the critics' core points: ride-hail vehicles are unoccupied roughly half the time, they make up as much as 13.4% of vehicle-miles in San Francisco County and about 7% in D.C., and the impact concentrates in city cores where transit competes most directly. Groups like NEO Sierra Club are already converting the companies' own VMT data into emissions estimates (~39.65M daily VMT across the six cities).
First-order effects
- City regulators in the six studied markets now have company-published VMT baselines to cite when pricing curb access, per-ride fees, or core-zone restrictions — and the 'heavily impacts core areas' finding hands them the targeting map.
- Uber and Lyft absorb a reputational hit even from their own report: half-empty vehicle-miles undercuts the 'we reduce congestion' pitch, as Jeff Bercovici's note about the timing suggests the companies knew it.
Second-order effects
- Transit agencies gain ammunition to argue subsidized ride-hail cannibalizes riders — the pattern [[a:941318|Innisfil, Ontario lived through when its Uber-in-lieu-of-bus experiment forced fare hikes and ride caps]] as adoption grew.
- If cities start charging per vehicle-mile rather than per passenger trip, the deadhead problem becomes a direct cost line for Uber and Lyft, pressuring them toward higher occupancy (pooling) or fewer empty repositioning miles.
Third-order effects
- Self-published platform data is becoming the currency of urban transport policy: whoever controls the VMT dataset shapes whether regulators price ride-hail as a congestion solution or a congestion source.
- If the core-concentration pattern holds across cities, ride-hail regulation is likely to split geographically — light-touch in sprawl, per-mile fees and restricted zones in dense centers — changing unit economics differently in each.
The trend: Ride-hail platforms are shifting from disputing congestion research to co-authoring it with their own data, as cities move toward measuring and charging vehicles by the mile rather than the trip.