executive summary7,500-75,000 miles per year. the vehicles themselves last 160,000-200,000 miles....

A THOUGHT REPORT BY

The Road to 2030: Vehicle Production and Sales in the Autonomous Era 2

The dual trends of shared ownership and autonomous technology are transforming the dialogue about the auto industry. Though they haven’t yet impacted vehicle production, these themes loom ever larger for all involved in the business of personal transportation.

Executive Summary

ALG and its parent company TrueCar sit at the intersection of these trends. As the fore-casting division of TrueCar, ALG works closely with automakers, lenders, fleet operators, and insurers to plan for the future. Our indepen-dence affords us a unique perspective.

Instead of bringing more speculation to the conversation, we took a step back and created a model for U.S. vehicle demand that incorporates the impact of shared ownership and autonomous technology. This approach provides a framework for thinking about the future, and the flexibility to

explore divergent scenarios. The outcomes are driven by just a handful of key assump-tions about how people use vehicles.

The most extreme scenario in this Thought Report shows a 26 percent reduction in passenger vehicle demand by 2030, with higher use of AV technology slashing the number of units in operation, or what is often called the vehicle parc, by over 50 percent.

But our model also generates a sce-nario in which fully autonomous vehicles arrive and overall vehicle output remains

the same. And if people increase their commuting distances and insist on sin-gle-occupancy trips in AVs, we may see a net increase in vehicle production.

While the future of personal mobility is decidedly uncertain, demand modeling and scenario planning shed light on the range of possibilities. We invite you to join us in exploring the future of our industry.


The good news is that the dynamics of the auto industry, while incredibly complex at a micro level, are much clearer when viewed from 30,000 feet. People, pets and personal cargo need to be moved. We move them in vehicles that have 2-8 seats and travel 7,500-75,000 miles per year. The vehicles themselves last 160,000-200,000 miles. Boil it all down and we arrive at a replace-ment rate of 15 million to 18 million units per year in the U.S. While economic cycles, population growth, and technology impact these ranges, they don’t change the basic mechanism that drives vehicle demand.

Media channels burst with speculation, providing almost daily updates on the progress of autonomous vehicle technology. Will fully autonomous vehicles (AVs) be in commercial operation within five years or will it take much longer? Are they an exis-tential threat to the auto industry or simply a shift in how it serves consumers’ needs? Do they represent evolution or revolution?

With a long list of questions in front of us, we took some time to distill this complex problem. Our goal: create a framework for thinking about how auton-omous vehicles will impact our industry.

Over the past two years the self-driving car has emerged from the realm of science fiction. Once seen as a distant possibility, the concept of driv-ers ceding control to artificial intelligence (AI) is now firmly planted in our cultural dialogue.

Introduction: Taking the Long View

The Road to 2030: Vehicle Production and Sales in the Autonomous Era

Introduction: Taking the Long View

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This report presents three specific scenar-ios for 2030 (or whenever we hit the tenth year of AV production.) While they’re clearly divergent, they’re clearly not exhaustive. Our goal is to spark a broad conversation about the fundamentals of vehicle demand in a self-driving world. We hope that you’ll use this framework to create some sce-narios of your own. Let’s get started.

It currently takes the U.S. vehicle fleet more than 10 years to turn over, or replace the majority of vehicles on the road. This rate is largely driven by household economics: purchase cost, practical service life (a function of repair costs) and household income.

Let’s assume that things get properly inter-esting about 10 years after those first AVs emerge from the assembly line. That’s the point at which highly divergent outcomes are plausible: will AVs be relegated to taxi-like services in urban markets? Will they trans-form the way we commute? Will suburban households continue to own vehicles, or will they avail themselves of autonomous vehicle fleets (AVFs) that offer low costs and freedom from ownership concerns in exchange for a few minutes of waiting?

From there we chose to focus on the medium-term future of our industry – what do things look like in the period from 2030-2040? To a certain extent the time horizon is arbitrary. We simply don’t know how quickly AVs will proceed down the commercialization path.

Conventional vehicle projects require at least five years to design, tool and pro-duce their first unit. Without knowledge of firm plans by automakers or technology firms to mass-produce a Level 4 AV, we assume that the first models will enter full-scale production no sooner than 2020.

And while it will be a watershed moment when the first AVs roll off an assem-bly line, that event won’t turn the world on its head – at least not right away.


as VMT = [PMT / Occupancy], as household VMT is primarily a byprod-uct of the need to move people.

• Annual vehicle mileage = 12,000 miles for household vehicles and 48,000 miles for fleet-owned passenger vehicles.

• Temporal utilization (a function of annual mileage and an average speed of 35 mph) = 4% for household vehicles and 16% for fleet-owned passenger vehicles.

• Vehicle lifespan (the final odometer reading at the scrap yard) = 160,000 miles for household vehicles and 200,000 miles for fleet-owned passenger vehicles.

Let’s start with an important distinction – the goal of this report is to present scenarios, not forecasts. By scenario we simply mean an outcome based on a set of assump-tions. Scenarios let us gauge a range of impacts against a chosen baseline.

In this case our baseline is a vision of 2030 in which nothing about the U.S. vehicle market has changed. What does this look like?

• Passenger miles traveled (PMT) scales linearly with population growth (0.7% per year according to IHS.)

• 94% of PMT continues to be served by privately owned, conventionally operated vehicles. The balance is served by taxi, livery, TNC, car-sharing and daily rental vehicles (collectively, “fleet-owned passenger vehicles.”)

• Vehicle occupancy is constant at 1.67 people per household vehicle and 2.09 for fleet-owned passenger vehicles.

• Demand for vehicle miles is expressed

At first glance, trying to predict the impact of AV technology on sales volume may seem absurd. After all, even the relatively simple exercise of forecasting 2016 sales is fraught with challenges. Stir a paradigm shift into the mix and roll the timeline to 2030? How do we intend to do that?

Methodology: The Fundamentals of Vehicle Demand



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Based on these assumptions, we estimate 16.5 million new units will be needed to meet organic demand in 2030. The auto market is highly cyclical, but this exercise strictly measures the impact of technology on the organic sales rate. Any of our scenarios can be scaled up or down to account for the health of the U.S. economy in the target year.

From here the possibilities are vast. Will autonomous vehicles be owned by households and used just like con-ventional vehicles? Will they come to market as fleets of polite robo-taxis? At this point it’s anyone’s guess.

To quantify the impact of these scenarios and any combinations thereof, we took a hard look at the drivers of organic vehicle demand. This seemingly complex calculation can be distilled to just three key drivers:

1. Ownership mix – fleets vs. privately owned vehicles

2. Occupancy of each vehicle type

3. Lifespan of each vehicle type

Just three drivers? Once you’ve cho-sen a target year and a population growth rate, then yes – these are the three key drivers of vehicle demand.

What about growth in per-capita mileage? It’s conceivable that people will travel further once their seat time is freed up for other uses. A long commute becomes more palatable, and if autonomy eases congestion, a 60-minute drive could take a driver 60 miles rather than 20 miles. Simply layer a growth assumption into the population growth number to compute the effective impact. For example, a 0.5% annual increase in per-capita PMT on top of 0.7% population growth: [(PMT_Current Year) * (1 + .005 + .007)] ^ (Target Year – Current Year).

It’s also conceivable that autonomous vehicles will make unmanned trips even if they’re privately owned (to retrieve family members, goods, etc.) This can be layered into the occupancy assumption. For exam-ple, a typical household vehicle currently conveys 1.67 passengers. This value falls to 1.25 if 25% of a privately owned AV’s miles are unmanned (i.e. occupancy = 0.)

Extra Trips and Empty Miles



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You may be wondering why annual vehicle mileage didn’t make the list. While it may not be intuitive, the organic steady-state sales rate is not affected by vehicle utilization. This is because the vehicle replacement rate is governed by total annual VMT and the average lifespan of vehicles in the vehicle parc (the Units in Operation.) In the equations on the right, you can see that an increase in Annual Utilization (in miles) will reduce both Units in Operation and Lifespan (in years) by the same percentage. Thus the increase in Utilization does not have a net impact on Annual Vehicle Demand. A smaller vehicle parc with higher temporal efficiency turns over at the same rate as a larger parc with lower efficiency. Lifespan is the key driver – a vehicle parc comprised of units that last longer will have a lower replacement rate. (Note that replacement rate, sales rate and demand are all interchangeable in the context of organic steady-state calculations. This implies that vehicles sales are 1:1 with replacements – a sale only takes place when another vehicle reaches the end of its life.)

Units in Operation

PMTOccupancy ×

Annual Utilization [in miles]

=

Lifespan (in years)

Lifespan [in miles]Annual Utilization

[in miles]=

Annual Vehicle Demand

Units in OperationLifespan [in years]=

Thus:

(Note the absence of Annual Mileage in the final equation.)

Annual Vehicle Demand

PMTOccupancy ×

Lifespan [in miles]=



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What about per-mile costs, the micro-economics of household transportation? No doubt the relative cost of traveling will be an important driver. Look closely and you’ll see that this is already captured by the ownership mix assumption. If high occupancy robo-taxis, with their opaque partitions and antibacterial seats, can get passengers from A to B at half the cost of a privately owned vehicle, surely people will migrate to them en masse. Departure latency will mitigate the migration, but market dynamics will bake this into the price.

Transportation Network Companies are already quantifying the cost of latency. Having a vehicle that’s on-call 24/7, as we do with private ownership, is an evolving luxury feature. At present it’s essentially binary. We choose between the immediate departure afforded by a private vehicle and the inherently delayed departure of a TNC or taxi. In high-density areas we can eas-ily envision a future with differential pricing for two minute, five minute, and 10 minute wait times – latency as a continuum.

Things will be much the same in terms of leaving personal items in one’s own car or customizing the interior environment. Fleets may offer lockable storage bins, stored seat positions and climate settings, and per-haps even food and beverage services to lure consumers away from privately owned vehicles. Errands may be handled auton-omously before the evening commute – a vehicle arrives with groceries and dry cleaning already on board. The market will undoubt-edly find a colorful range of new ways to serve consumers. Digital content is the obvi-ous one, but there will be many others. The availability and pricing of these features will have a major impact on the ownership mix.

With these themes in mind, let’s dive into three specific scenarios.

Couldn’t ride-sharing cause a shift away from private ownership all by itself? Why are we framing the discussion around autonomy instead of Transportation Network Companies like Lyft and Uber?

The short answer is that the cost of a human driver creates a natural cap on the viability of TNCs for most trips. Deutsche Bank estimates that it costs $1.54/mile to travel by TNC in the 20 largest U.S. metro areas, compared to $0.90/mile for a privately owned vehicle. This cost delta means that ride-sharing is simply too expensive to replace private ownership outside of urban cores. And while the cost does come down if you share trips with strangers via Lyft Line and UberPool, those savings are eroded by longer wait times. Lastly, it’s worth noting that this sizable TNC cost disadvantage exists despite widespread discounting and promotional fares (driven by market share wars between the two companies.) This is unlikely to change until the driver is replaced by an algorithm in an AV.

Ride-Sharing without Autonomy


What if they’re wrong, and auton-omous vehicles are relegated to the fringes of the market?

This scenario assumes that autonomous fleets fully displace current taxi, livery and TNC miles, as well as all daily rental and car-sharing miles. At the house-hold level, it assumes that premium AVs capture 2% of passenger miles and that these vehicles are still privately held. (i.e. roughly 20% of privately owned luxury vehicles achieve Level 4 autonomy.)

Scenario 1 views 2030 through a skeptical lens. It recalls the broken technological promises of the past – EV and fuel cell vehicle prolifera-tion, solar panels on every roof, and supersonic air travel at reasonable prices. History is full of broken promises, but proponents of vehicu-lar AI claim that things are different this time.

Scenario 1: In With a Whisper


Scenario 1: In With a Whisper

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With the notable exception of paid drivers, industry stakeholders let out a collective sigh of relief in this case. The status quo is preserved for vehicle manufacturers with no change to sales against the 2030 baseline. This is because our assumptions for occupancy and lifespan do not change just because the driver is removed from the equation. Taxi-like services still convey 25% more passengers than privately owned vehicles, at 2.09 vs. 1.67 today. (Ridesharing optimization replaces the human driver with an additional passenger.) And autono-mous fleets maintain the long 200,000-mile service life of conventional fleet vehicles.

Similarly, we assume that privately owned AVs perform at par with their conventional counterparts. Household trips convey an average of 1.67 passengers and the private AV units are scrapped at 160,000 miles.

All in all, there’s not much to report here. Let’s see what happens if we turn up the heat.

Figure 1 Migration of Passenger Miles

94%Private

Conventional

92%PrivateConventional

2%Private Autonomous

6%Fleet Autonomous

6%Fleet

Conventional

Current Future


Does it sound far-fetched? If so, roll the clock out to 2040 or 2050 – the percent-age impact to sales (relative to baseline) is the same regardless of the target year.

In specific terms, we’ve pegged this scenario as follows:

• All taxi, livery, TNC, daily rental and car-sharing passenger miles are served by AV fleets (6.3% of total PMT, as in Scenario 1).

• 25% of household PMT is now served by AV fleets rather than private vehi-cles (29.7% of total PMT when combined with the bullet above).

• An additional 25% of household PMT is served by privately owned AVs with Level 4 capability (23.4% of total PMT).

This is where things get interesting. Scenario 2 is a vision of 2030 in which we’ve resolved the col-lision liability question, Vehicle-to-Infrastructure technology (a key accelerant) has been embraced by NHTSA and taxpayers, and the majority of Americans are comfortable abdicating their piloting duties. As a result, we say goodbye to single-occu-pancy commuting in privately owned vehicles.

Scenario 2: Ready for Prime Time



14


94%Private

Conventional


23%PrivateAutonomous

30%Fleet Autonomous6%

Fleet Conventional

Current Future



15

This shifts over 23% of passenger miles to higher-occupancy, longer-lasting vehicles. The result: an 8.6% reduction in vehi-cle sales. (Note that as in Scenario 1, the migration of privately owned vehicles to Level 4 autonomy does not affect sales.)

What happens if these trends are carried even further?

Figure 4 Sales Walk

The same is true of Occupancy and Lifespan, resulting in a 1% overlap here.

Note that Occupancy and Utilization have a compound effect which produces a 3.7% overlap when both are changed, as in this example.

Figure 3 Units in Operation Walk

100.0%Baseline Sales

- 4.8%Occupancy ∆ - 4.8%

Lifespan ∆ 91.4%Scenario 2 Sales

100.0%Baseline UIO

- 4.9%Occupancy ∆

- 18.5%Utilization ∆ 80.3%

Scenario 2 UIO


This is the scenario in which longstanding industry stakehold-ers lose their footing and are forced to assess the relevance of their business models. Vehicle production takes a sizable hit, but the biggest impacts are sustained by businesses that serve the ancillary needs of vehicle owners. As PMT shifts to fleets the need for expansive networks of insurance agencies, auto parts stores, repair shops, and consumer lending operations is radically diminished. Incumbents in these areas of the value chain may be able to pivot toward serving fleet customers, but the structure of their organizations will change dramatically. And what becomes of automotive retailers? They’re well positioned to maintain AV fleets and their real estate may be valuable to fleet operators for downtime storage. There’s certainly room for savvy retailers to adapt. Rather than speculate too deeply on how this shift will manifest across the industry, let’s take a look at the numbers.

Scenario 3: The Fleet Revolution



17

In specific terms, we’ve pegged this scenario as follows:

• All taxi, livery, TNC, daily rental and car-sharing passenger miles are served by AV fleets (6.3% of total PMT, as in Scenarios 1 and 2).

• 75% of household PMT is now served by AV fleets rather than private vehicles (76.6% of total PMT when combined with the bullet above).

• An additional 10% of household PMT is served by privately owned AVs with Level 4 capability (thus 85% of household PMT is autonomous).


94%Private

Conventional


9%Private Autonomous

77%Fleet Autonomous

6%Fleet

Conventional

Current Future



18

The net impact is a sobering 26% reduc-tion in annual production and sales. Recall that this outcome is driven by the assump-tion that fleet vehicles have 25% higher occupancy (2.09 vs. 1.67 passengers) and 25% longer lifespans (200,000 miles versus 160,000.) Unwinding these two assumptions brings us back to the base-line of 16.5 million units in 2030, while boosting them to 50% above current levels yields a dizzying sales decline of 41%.

At this point we hope you’re fired up and eager to create your own scenarios. Get in touch and tell us what you think the industry will look like in 2030.

Figure 7 Sales Walk

Note 2.9% overlap.

Note 11.1% overlap.Figure 6 Units in Operation Walk

100.0%Baseline Sales

- 14.4%Occupancy ∆

- 14.4%Lifespan ∆

74.1%Scenario 3 Sales

100.0%Baseline UIO

- 14.8%Occupancy ∆ - 55.5%

Utilization ∆

40.8%Scenario 3 UIO


option for a client meeting. Dynamic pricing will deliver real-time price signals that drive behavior and boost market efficiency.

This flexibility is something the auto industry will ultimately embrace, for it opens up a rich new landscape of ways to serve consumers. As they spend less on vehicle hardware they’ll spend more on other aspects of the mobility experience. An exciting market expansion is within our sights. We look forward to identifying and seizing these opportunities with our partners in the years to come.

As we noted briefly at the outset, value creation in an autonomous world will be all about charging for the experience of getting from A to B. The hardware will play a key role in this experience but it will drive profits in a very different way. A fleet-oriented vehicle parc effectively decouples hardware sales from profits, affecting a radical shift in the mobility value chain. As vehicles last longer, automakers can produce fewer units while serving the same cus-tomer base. That customer base is paying for movement and their price sensitivity transforms when they’re not signing up for a three- to six-year engagement with a single piece of hardware.

In this new paradigm, household travel costs can flex from day to day. A consumer can opt for a luxury experience on a Friday after-noon following a tough week at work. On Monday morning she might choose the cheapest option for a trip to the office or a status-forward

In this brief survey of future vehicle production scenarios we focused on the three key drivers of vehicle demand. We learned that the complex-ity of the auto market is distillable into a manageable macro model. At this point you may be questioning whether we’ve missed the point by focusing on vehicle production. The answer depends on your role in the ecosystem. For automotive lenders, insurance companies and suppli-ers, the answer is clearly no: production volume and private ownership are vital to these links in the value chain, and are likely to remain so. But the story is very different for automakers, retailers, and fleet operators.

Closing Remarks


Total U.S. Light Vehicle Sales (Retail + Fleet) in 2030 with constant per-capita PMTSensitivity Analysis: The Impact of Autonomous Fleets

Sensitivity to occupancy (@ fleet AV lifespan = 500k miles)Share of total PMT served by Autonomous Fleets



Sensitivity to lifespan (@ fleet AV occupancy = 5.0)Share of total PMT served by Autonomous Fleets



0% 5% 25% 50% 75% 100%

Avgerage Occupancy (passengers per fleet AV)

1.2 16,876,844 16,402,784 14,506,543 12,136,243 9,765,942 7,395,642

1.6 16,876,844 16,310,338 14,044,316 11,211,788 8,379,260 5,546,731

2.0 16,876,844 16,254,871 13,766,979 10,657,114 7,547,250 4,437,385

2.4 16,876,844 16,217,893 13,582,088 10,287,332 6,992,577 3,697,821

3.3 16,876,844 16,171,670 13,350,974 9,825,105 6,299,235 2,773,366

5.0 16,876,844 16,123,191 13,108,582 9,340,320 5,572,059 1,803,797

0% 5% 25% 50% 75% 100%


1.2 16,876,844 16,957,457 17,279,909 17,682,974 18,086,040 18,489,105

1.6 16,876,844 16,726,343 16,124,340 15,371,836 14,619,332 13,866,829

2.0 16,876,844 16,587,675 15,430,999 13,985,153 12,539,308 11,093,463

2.4 16,876,844 16,495,229 14,968,771 13,060,698 11,152,625 9,244,552

3.3 16,876,844 16,379,672 14,390,986 11,905,129 9,419,272 6,933,414

5.0 16,876,844 16,258,476 13,785,006 10,693,168 7,601,330 4,509,493

0% 5% 25% 50% 75% 100%


1.2 16,876,844 17,188,571 18,435,478 19,994,112 21,552,747 23,111,381

1.6 16,876,844 16,899,678 16,991,017 17,105,190 17,219,363 17,333,536

2.0 16,876,844 16,726,343 16,124,340 15,371,836 14,619,332 13,866,829

2.4 16,876,844 16,610,786 15,546,555 14,216,267 12,885,979 11,555,690

3.3 16,876,844 16,466,340 14,824,325 12,771,806 10,719,287 8,666,768

5.0 16,876,844 16,314,845 14,066,849 11,256,855 8,446,860 5,636,866

0% 5% 25% 50% 75% 100%

Avgerage Lifespan (miles per fleet AV)

120K 16,876,844 16,408,793 14,536,588 12,196,332 9,856,077 7,515,821

160K 16,876,844 16,314,845 14,066,849 11,256,855 8,446,860 5,636,866

200K 16,876,844 16,258,476 13,785,006 10,693,168 7,601,330 4,509,493

240K 16,876,844 16,220,897 13,597,110 10,317,377 7,037,644 3,757,911

320K 16,876,844 16,173,923 13,362,241 9,847,638 6,333,036 2,818,433

500K 16,876,844 16,123,191 13,108,582 9,340,320 5,572,059 1,803,797

1M 16,876,844 16,078,097 12,883,107 8,889,371 4,895,635 901,899

0% 5% 25% 50% 75% 100%


120K 16,876,844 16,933,100 17,158,125 17,439,405 17,720,686 18,001,967

160K 16,876,844 16,708,075 16,033,002 15,189,159 14,345,317 13,501,475

200K 16,876,844 16,573,061 15,357,928 13,839,012 12,320,096 10,801,180

240K 16,876,844 16,483,051 14,907,879 12,938,914 10,969,948 9,000,983

320K 16,876,844 16,370,538 14,345,317 11,813,791 9,282,264 6,750,738

500K 16,876,844 16,249,025 13,737,751 10,598,658 7,459,565 4,320,472

1M 16,876,844 16,141,013 13,197,692 9,518,540 5,839,388 2,160,236

0% 5% 25% 50% 75% 100%


120K 16,876,844 17,158,125 18,283,247 19,689,651 21,096,055 22,502,458

160K 16,876,844 16,876,844 16,876,844 16,876,844 16,876,844 16,876,844

200K 16,876,844 16,708,075 16,033,002 15,189,159 14,345,317 13,501,475

240K 16,876,844 16,595,563 15,470,440 14,064,036 12,657,633 11,251,229

320K 16,876,844 16,454,923 14,767,238 12,657,633 10,548,027 8,438,422

500K 16,876,844 16,303,031 14,007,780 11,138,717 8,269,653 5,400,590

1M 16,876,844 16,168,016 13,332,707 9,788,569 6,244,432 2,700,295


Sensitivity Analysis: The Impact of Autonomous Fleets

22

Change in U.S. Light Vehicle Sales (Retail + Fleet) vs. Baseline with constant per-capita PMT







0% 5% 25% 50% 75% 100%


1.2 2% -1% -12% -26% -41% -55%

1.6 2% -1% -15% -32% -49% -66%

2.0 2% -1% -17% -35% -54% -73%

2.4 2% -2% -18% -38% -58% -78%

3.3 2% -2% -19% -40% -62% -83%

5.0 2% -2% -21% -43% -66% -89%

0% 5% 25% 50% 75% 100%


1.2 2% 3% 5% 7% 10% 12%

1.6 2% 1% -2% -7% -11% -16%

2.0 2% 1% -6% -15% -24% -33%

2.4 2% 0% -9% -21% -32% -44%

3.3 2% -1% -13% -28% -43% -58%

5.0 2% -1% -16% -35% -54% -73%

0% 5% 25% 50% 75% 100%


1.2 2% 4% 12% 21% 31% 40%

1.6 2% 2% 3% 4% 4% 5%

2.0 2% 1% -2% -7% -11% -16%

2.4 2% 1% -6% -14% -22% -30%

3.3 2% 0% -10% -23% -35% -47%

5.0 2% -1% -15% -32% -49% -66%

0% 5% 25% 50% 75% 100%


120K 2% -1% -12% -26% -40% -54%

160K 2% -1% -15% -32% -49% -66%

200K 2% -1% -16% -35% -54% -73%

240K 2% -2% -18% -37% -57% -77%

320K 2% -2% -19% -40% -62% -83%

500K 2% -2% -21% -43% -66% -89%

1M 2% -3% -22% -46% -70% -95%

0% 5% 25% 50% 75% 100%


120K 2% 3% 4% 6% 7% 9%

160K 2% 1% -3% -8% -13% -18%

200K 2% 0% -7% -16% -25% -35%

240K 2% 0% -10% -22% -34% -45%

320K 2% -1% -13% -28% -44% -59%

500K 2% -2% -17% -36% -55% -74%

1M 2% -2% -20% -42% -65% -87%

0% 5% 25% 50% 75% 100%


120K 2% 4% 11% 19% 28% 36%

160K 2% 2% 2% 2% 2% 2%

200K 2% 1% -3% -8% -13% -18%

240K 2% 1% -6% -15% -23% -32%

320K 2% 0% -10% -23% -36% -49%

500K 2% -1% -15% -32% -50% -67%

1M 2% -2% -19% -41% -62% -84%


Occupancy assumptions: Average household vehicle occupancy is 1.67 per http://nhts.ornl.gov/2009/pub/stt.pdf; we assume that baseline occupancy for taxi, livery, TNC, rental, and car-sharing vehicles is 25% higher (2.09 vs. 1.67) and serves a combined 6.25% of total PMT.

“Autonomous Vehicle” refers to a vehicle capable of Level 4 autonomy per NHTSA• http://www.nhtsa.gov/About+NHTSA/Press+Releases/

U.S.+Department+of+Transportation+Releases+Policy+on+Automated+Vehicle+Development

VMT:• https://www.fhwa.dot.gov/policyinforma-

tion/tables/vmt/vmt_forecast_sum.pdf• http://www.fhwa.dot.gov/policyinformation/

travel_monitoring/14dectvt/14dectvt.pdf• https://www.fhwa.dot.gov/policyinformation/

travel_monitoring/15novtvt/• https://www.fhwa.dot.gov/policyinformation/

travel_monitoring/15novtvt/page3.cfm• http://www.advisorperspectives.com/dshort/

updates/DOT-Miles-Traveled.php• http://www.ssti.us/2014/02/

vmt-drops-ninth-year-dots-taking-notice/

Population (321,418,820 as of July 1, 2015):• http://www.census.gov/popclock/

Household VMT by trip type:• http://www.citylab.com/commute/2015/03/

driving-in-america-is-approaching-a-new-normal/388421/• https://www.fhwa.dot.gov/policy/2010cpr/execsum.cfm• http://traveltrends.transportation.org/Documents/

B2_CIA_Role%20Overall%20Travel_web_2.pdf• https://www.eia.gov/conference/2014/

pdf/presentations/mcguckin.pdf• http://www.post-gazette.com/news/

transportation/2016/01/16/Survey-finds-most-commuters-travel-alone-by-car/stories/201601160020

Occupancy:• 1.55: http://css.snre.umich.edu/css_doc/CSS01-07.pdf• 1.67: http://nhts.ornl.gov/2009/pub/stt.pdf (page 39)

Taxi VMT as % of total:• https://www.fhwa.dot.gov/planning/tmip/

publications/other_reports/commercial_vehi-cles_transportation/sum_sect4.cfm#fn4_9

Annual mileage for NYC taxi:• http://www.nyc.gov/html/tlc/downloads/

pdf/2014_taxicab_fact_book.pdf

Boston taxi utilization:• http://www.cityofboston.gov/mayor/

pdfs/bostaxiconsultant.pdf

Cost per mile – TNC vs. privately owned vehicle:• http://www.businessinsider.com/

uber-lyft-cost-versus-car-by-metro-area-2016-3

UberX vs. taxi economics:• http://valleywag.gawker.com/

beautiful-illusions-the-economics-of-uberx-1589509520

Private vehicle average annual miles:• Driver-basis: https://www.fhwa.dot.

gov/ohim/onh00/bar8.htm• Vehicle-basis: http://www.afdc.energy.gov/data/

AV impact on vehicle occupancy and GHG:• http://orfe.princeton.edu/~alaink/SmartDrivingCars/

PDFs/aTaxi_GHG_Emissions_Greenblatt.pdf

AV impact on total miles:• http://fortune.com/2015/11/17/

la-auto-show-vehicle-miles/

Daily rental market size:• http://www.autorentalnews.com/fileviewer/2229.aspx

Holding period:• http://www.fool.com/investing/general/2012/07/13/

surprising-facts-about-the-rental-car-industry.aspx• http://www.autorentalnews.com/channel/

remarketing/news/story/2016/01/manheim-index-up-risk-average-mileage-dips.aspx

Age of vehicles in US:• http://www.rita.dot.gov/bts/sites/rita.dot.gov.

bts/files/publications/national_transporta-tion_statistics/html/table_01_26.html_mfd

• http://www-nrd.nhtsa.dot.gov/Pubs/809952.pdf

Others:• http://miter.mit.edu/articlebalancing-act-fu-

ture-car-sharing-and-driving-service/• http://www1.nyc.gov/assets/operations/downloads/

pdf/For-Hire-Vehicle-Transportation-Study.pdf• https://social.ford.com/content/campaign/media-trends/• http://fivethirtyeight.com/features/

how-suburban-are-big-american-cities/• http://alankandel.scienceblog.com/2014/02/07/

annual-per-capita-california-driv-ing-1-5-times-the-national-average/

• http://www.mckinsey.com/indus-tries/high-tech/our-insights/disruptive-trends-that-will-transform-the-auto-industry

Notes

References


Daniel Malik Director, Product [email protected]

Marilynn Youngs VP, OEM Solutions [email protected]

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