The VMT Rebound

Now that we’ve discussed barriers to adoption and the likely scale of the deployment phase, let’s talk about the second order effects that are likely to develop as AVs take market share. Among the forecasts of the Consensus Model, I believe the prediction of constant Vehicle Miles Traveled (VMT) per person to be the most suspect. Traveling a mile in an AV is going to be deliriously cheap, and when we make something cheaper, we consume a lot more of it.

This concept is not new. In the nineteenth century, the economist William Stanley Jevons observed that as coal got cheaper, consumers used more coal. Economists have subsequently observed the Jevons Paradox in action in other markets, including traffic congestion, energy efficiency, and the consumption of basic materials.

The futurist George Gilder, who was influential during the early years of the Internet, makes a related point. Every economic age, Gilder says, is defined by a key scarcity and a key abundance. When a new technology emerges to change the relationship between scarcity and abundance, old business models break as consumers adopt new behaviors that would have been impossible under the old framework. As the deployment phase matures, the business models that win are ones that would have looked wasteful, frivolous, and even decadent under the ancien régime.

The most important scarcities imposed by transportation are time, attention, cost, and the actions of rival drivers. Driving somewhere takes time. During that time, it takes nearly your full attention (one hopes). Every mile you drive costs money, mostly in the form of fuel and vehicle depreciation. And if you want to travel to the same place as everyone else, at the same time, you will face traffic and pay more in time and attention cost to get there.

So — how cheap will AVs be compared to traditional vehicles? Brian Johnson, in his recent report for Barclays, provides framework for assessing the cost reductions. Johnson suggests that there will be four types of vehicles in the future: traditional cars that still require a human driver, “family autonomous vehicles” which are owned by consumers and used exclusively within a single household, “shared autonomous vehicles” (SAVs) owned and deployed by fleets in a model similar to Uber and Hertz, and “pooled shared autonomous vehicles” (PSAVs) that are like the preceding category, but which carry more than one passenger at time, like Uber Pool or Lyft Line.

Oddly, Johnson’s slides implies that fuel cells will power the SAVs and PSAVs of the future, despite much greater progress by battery-powered cars. For our purpose, we can ignore this technicality and continue to assume that most AVs will also be EVs.

Johnson’s analysis, which assumes 12,000 VMT/year, suggests that traveling one mile in an SAV or PSAV will cost between $0.08 to $0.44. On an annual basis, that works out to $960 to $5280. According to the AAA, an American driving an average sedan will spend $0.58 per mile, for an annual spend of $8700, though their analysis assumes VMT of 15,000 per year. Even accounting for the different VMT assumptions, it’s clear that SAV services will reduce the cost of transportation for consumers by thousands of dollars a year.

More importantly, the cost in attention associated with transportation will drop to nearly zero. The average American could shift some of the 5.5 hours of television watched per day into the car, and end up with vastly more personal time once freed from the need to pay attention to the road. This possibility has led many people to predict that AVs could enable further suburban sprawl as the costs of transportation fall. A person who moves to a more distant exurb but commutes via a PSAV will pay less money for transportation, have more time for entertainment, and will also pay lower, exurban prices for their housing. It will be an irresistible combination, and it will be just one of many ways that VMT will ratchet upwards once each marginal mile loses its cost in dollars and attention.

Autonomous vehicles will also allow new transportation use cases to emerge. Short haul flights and regional train travel will be hard pressed to compete against trips in FAVs, if the electricity needed to drive an autonomous Tesla from Los Angeles to San Francisco costs less than $10.00. Compared to a $75 fare on Southwest, the cost savings will be high (especially if more than one person is traveling) and the time penalty will be minimal for distances of under 500 miles, when calculated on a door to door basis. Even for those consumers who choose to forego ownership of a vehicle to use a shared service, AVs promise vastly cheaper inter-city travel. For a service like Megabus, the cost reduction when batteries replace gasoline and software replaces drivers is going to be massive. The existing fleet of A320 and 737 aircraft may get redeployed to long haul flights.

It shouldn’t be surprising that planners and analysts look at the prospect for higher VMT enabled by AVs and react with apprehension. After all, when humans are driving internal combustion engine vehicles, increasing VMT increases the associated externalities: traffic, carbon emissions, and the need for parking lots, among many other associated costs. Writing in Slate, Joseph Coughlin and Luke Yoquinto of MIT conclude with a cautionary note:

But still, the high-speed autonomous commute stands as a real possibility. That’s why we should start thinking now about its implications — both positive and negative. We need to make a deliberate decision about how we will live in the future, before the self-driving car makes it for us.

While calls for technocratic planning are understandable, preemptive solutions are unnecessary for adjusting to the increase in VMT that AVs will bring. All the negative externalities that would call for policy solutions are collapsed by the technology of AVs/EVs themselves. Traffic congestion will be reduced by the better driving, closer spacing and platooning capabilities of software. Pollution and carbon emissions would be irrelevant if the AVs were also EVs, and in fact the carbon advantage of EVs would increase over time as the grid deploys more renewables to meet renewable portfolio standards. And of course there would be far less need for parking.

Also, demand aggregation, currently in its infancy, has the potential to further reduce the cost of PSAV services. Just this week, Uber began testing “smart routes” that incentivize users with lower fares to catch an UberPool on a major street nearby, making the overall system more efficient. There will be more innovation along these lines. Some of the ideas that have been discussed include neighborhood commuter jitneys that depart for downtown every 15 minutes, AVs as demand aggregation for public transport, and AV versions of company-specific transportation options, like the Google bus service in the Bay Area.

Finally, new users of transportation, like children and seniors, are likely to travel many multiples of the distance they do today once AVs lower the costs and barriers to their safe transportation.

Every one of these AV use cases will share a common characteristic: they will be outrageously cheap (in dollars, but also in time, attention, and non-excludability) compared with today’s transportation options. If there is a rebound effect, the question then becomes — how big will it be? How elastic is the demand curve for transportation? A few other cases of rebound effect are instructive.

Among household appliances, none have gotten more efficient, more quickly, than refrigerators and air conditioners. Through 2010, “the average refrigerator sold in the United States…uses three-quarters less energy than the 1975 average, even though it is 20% larger and costs 60% less.” But consumers have spent that savings on more refrigeration. A suburban house is now likely to have a fridge in the kitchen, one in the basement, a wine chiller somewhere in the house, and a mini fridge in a home office, in addition to the 20% larger refrigerator in the kitchen. The story is similar with air conditioning. Between 1993 and 2005, “the energy efficiency of residential air-conditioning equipment improved twenty-eight per cent, but energy consumption for A.C. by the average air-conditioned household rose thirty-seven per cent.”

Back to AVs. The Consensus Model holds that vehicle sales will fall, and the total automotive fleet will shrink, in response to the deployment of SAV & PSAV services. Barclays forecasts that the fleet will be 60% smaller and sales will be down 40%.

Their assumptions, presented in some of the previous slides shown above, are that an SAV will displace 9 traditional vehicles and travel 64,000 miles per year. They further assume that a PSAV will displace up to 18 traditional vehicles, but that they will also travel only 64,000 miles per year. That’s not very different from a New York City taxi, which travels 70,000 miles per year.

These load factors are strangely low. A car traveling 64,000 miles per year at an average of 20 miles per hour will only be in service for 8.7 hours per day. There are reasons to believe AVs will travel much faster than 20 miles per hour, and that they will be in use more than 9 hours per day.

First, platooning will allow AVs on freeways to travel at much higher speed and density than traditional cars. Second, at full deployment, waiting at red lights will be a thing of the past, as this video shared by Benedict Evans sugests.

Third, charge times are likely to be only an hour or two per day, using modern high power charging infrastructure. Fleet owners will have every incentive to charge quickly, getting their cars back on the road to earn revenue.

So, how much more transportation will people consume? With costs likely to fall between 50% to 90%, a consumer could increase VMT by anywhere from 2X to more than 5X while spending no more on transportation than today. Relative to the Consensus Model, I believe we will have more people, in more AVs, traveling more miles every year, requiring a larger fleet than is assumed, and higher annual vehicle production. Further, once the security risks have been addressed early in the installation phase, during the deployment phase this increase in VMT will be accompanied by none of the costs and externalities associated with higher VMT today.

Finally, we must consider the possibility, which may be an inevitability, that VMT with a person in the car will come to be the minority of miles traveled by AVs on the highways of the future. In his post, Benedict Evans hints at the kinds of on-demand services that might emerge:

They have the potential greatly to expand the adoption of on-demand, and so to transform who buys cars and why. Removing the drivers from an on-demand car service cuts the cost, since you don’t have to pay them and also since lower accident rates mean cheaper insurance (though this applies to your own car too). But in addition, autonomous cars expand supply for on-demand services, since many more cars are available to be used for on-demand when their owners aren’t using them. This will creates all sorts of second-order effects and feedback loops.

Any service priced at a premium today for its on-demand convenience, and available only to the subset of the population able to pay for convenience, could be ten to a hundred times larger once AVs lower the price of convenience nearly to zero. Delivery services are just the easiest type of service to imagine; there will be others.

We can begin to glimpse the future demand response to AVs. People won’t travel just 12,000 to 15,000 miles per year. They might travel 30,000 to 50,000 miles per year. And they might generate non-occupied AV journeys bringing them goods and services on demand that would create another 50,000 to 100,000 VMT per year. Total transportation demand, in the face of per mile cost reductions of 50% to 90%, might logically respond by rebounding to a new equilibrium where consumption is 10X the prior demand.