Three Revolutions in Urban

TRANSPORTATION
How to achieve the full potential of vehicle electrification, automation and shared mobility in urban transportation systems around the world by 2050

Lew Fulton, UC Davis
Jacob Mason, ITDP
Dominique Meroux, UC Davis

Research supported by:
ClimateWorks Foundation, William and Flora Hewlett Foundation, Barr Foundation

Acknowledgements
The authors wish to thank ClimateWorks Foundation, William and Flora Hewlett Foundation, and Barr Foundation for
their generous financial support, which made this work possible. We also thank the individuals who participated in theadvisory committee for this study and provided valuable comments. Members of that group included Pierpaolo Cazzola(IEA); Holger Dalkmann, Dario Hidalgo, and Diego Canales (WRI); Phillippe Crist (ITF); Shomik Raj Mehndiratta (World
Bank); Tim Wallington (Ford); Jarret Walker (unaffiliated); Anthony Eggert (ClimateWorks); Margarita Parra (Hewlett
Foundation); Colin Hughes, Aimee Gauthier, Clayton Lane, Kathleen Letchford, Ana Nassar, Bernardo Serra, Diego da
Silva, Gabriel Oliveira, and Clarisse Linke (ITDP); and Dan Sperling, Susan Pike and Mollie D’Agostino (UC Davis).
Thanks also to Jacob Teter and other staff at the International Energy Agency for insights related to the urban analysis
developed with the IEA Mobility Model (MoMo) that this analysis draws on. However, this analysis has been undertakenby the UC Davis and ITDP, and does not necessarily reflect the views of the International Energy Agency.
Finally, the authors thank all the members of the production teams at UC Davis and ITDP who carried out editing, proof
reading, and layout of this report. This includes Stephen Kulieke, Beth Bourne, Rosa Dominguez-Faus, and Kelly Chang(UC Davis); Jamie Knapp (J Knapp Communications); and Jemilah Magnusson (ITDP).
Many other people too numerous to name helped this project succeed, and we thank them. Of course, any errors or
flaws in the end product are the responsibility of the authors alone.

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Three Revolutions in Global Transportation

Contents
Acknowledgements
Executive Summary
1. Introduction
2. Study Design, Methodology, and Scenarios
3. The Three Revolutions: Status and Potential
Electrification
Automation
Shared Mobility
4. Future Scenarios: BAU, 2R, and 3R
The BAU Scenario
The 2R Scenario: Electrification and Automation
The 3R Scenario: Adding Shared Mobility
Results: Passenger Travel Projections Across the Scenarios
Scenario Impacts on Energy and CO2 Emissions
The Costs of 3 Revolutions
5. Policy Narratives
BAU Scenario Policy Narrative
2R Scenario Policy Narrative
3R Scenario Policy Narrative
6. Conclusions, Uncertainties, Next Steps
Bibliography

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Three Revolutions in Global Transportation

Executive Summary
The world is on the cusp of three revolutions intransportation: vehicle electrification, automation,
and widespread shared mobility (sharing of vehicle
trips). Separately or together, these revolutions will
fundamentally change urban transportation around theworld over the next three decades.

by encouraging a large increase in trip sharing, transit
use, and active transport through policies that support
compact, mixed use development, cities worldwide
could save an estimated $5 trillion annually by 2050
while improving livability and increasing the likelihood of
meeting climate change targets.

Each revolution addresses different societal needs, but
can also lead to societal costs:

Methodology•• Vehicle electrification can cut vehicle energy use
and CO2 emissions. However, for electrification tohave maximum benefits, power generation must be
strongly shifted away from fossil fuels and deeply
decarbonized. In addition, these vehicles will likely
remain expensive for at least one more decade.
•• Automation can provide important safety benefits,
reduce labor costs, and enable cheaper traveland more productive use of time. However, bylowering the cost of travel in terms of time andmoney, automation would likely induce more traveland dramatically reduce the number of jobs intransportation.
•• Shared mobility, whether through shared vehicle trips
or public transport, can lead to more efficient use ofurban space, reduce traffic congestion, enable more
walking and cycling, cut energy use and emissions,and generally improve urban livability. However,this would require large increases in load factors(passengers per vehicle trip), and a range of strong
policies to achieve.
Together, the positive and negative aspects of eachrevolution will interact in many complex and difficultto-predict ways. This report may be the first to attempt
to quantify how these major changes could evolve andinteract on a global and regional basis out to 2050. It
considers possible end states, as well as transitional
pathways and policies needed to get there.
Our central finding is that while vehicle electrification
and automation may produce potentially important
benefits, without a corresponding shift toward sharedmobility and greater use of transit and active transport,these two revolutions could significantly increase
congestion and urban sprawl, while also increasing the
likelihood of missing climate change targets. In contrast,

We build on two recent reports published by ITDP and
UC Davis’s STEPS program: “A Global High Shift Cycling
Scenario” (2015) and “A Global High Shift Scenario:
Impacts and Potential for More Public Transport, Walking
and Cycling with Lower Car Use” (2014). Both reports tooka scenario approach to consider the role of different travelmodes in providing mobility, and the amount of potential
energy savings and CO2 reduction that could come froma less car-centric world in the future (Mason, Fulton, &McDonald, 2015; Replogle & Fulton, 2014).
This report expands upon the scope of the previous
studies by considering the role of electrification,
automation, and ride sharing (more people per vehicle)
in developing future scenarios. The possible types ofimpacts are well documented, and researchers havebegun to estimate how various combinations of impacts– such as people spending more time in their cars, oron-demand mobility trips substituting for public transport– may affect travel and energy use. But most studies havenot explicitly projected numeric scenarios into the future
or attempted to characterize how various interactions
could play out. As with our previous modal shift studies,
this study is global, breaking the world into eight regions
including five major markets: United States, Europe, China,
India, and Brazil.
We have developed our present analysis using threemain urban travel scenarios: a business-as-usual scenario,a technology-dominated 2 Revolutions scenario, and atechnology + high shared-mobility 3 Revolutions scenario.
These are elaborated from a base year of 2015 through2050 as follows:•• Business-as-usual (BAU) scenario – This scenario
assumes few changes from 2017 travel patterns andcurrent trends through to 2050. No major revolutions
occur. It assumes internal combustion engine (ICE)light-duty vehicles (LDVs) remain dominant or grow in
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Three Revolutions in Global Transportation

dominance, depending on the country, through 2050,
and applies population and growth projections withthese assumptions in mind.
•• 2 Revolutions (2R) scenario – This is a technologyfocused scenario that includes rapid vehicle
electrification along with – but starting later – rapid
automation. Electric vehicles (EVs) achieve asignificant share of vehicle sales by 2025 in leading
countries, with automated EVs reaching this stage
about five years later. Both are dominant around theworld by 2050. This scenario contains no significant
increase of shared vehicle trips through newtechnology; it preserves the BAU trends toward a
private-car-dominated world.
•• 3 Revolutions (3R) scenario – This scenario
includes widespread vehicle electrification andautomation, and adds a major shift in mobility
patterns by maximizing the use of shared vehicle
trips. This scenario includes all three revolutions, andis a strongly multi-modal scenario, with increased
availability of vehicles for shared trips, increased
public transport availability and performance(including on-demand small bus services, larger buses
and rail), and significant improvements in walking andcycling infrastructure and therefore in travel by thesemodes.
Other scenarios with different combinations of theserevolutions could be considered; the choices madehere are intended to simplify these complex scenariosand highlight certain features. And although we cannot
accurately predict the interactions that each step of eachrevolution will have on the others, our scenarios create
paradigms of travel that we can use to quantify the energy
and CO2 impacts and begin to develop policies to guidethe world toward the most societally optimal outcomes.

Findings
Our central finding is that the 3R scenario is the best
option for reducing energy use and CO2, and performs
significantly better than 2R in these respects as wellas on total measured cost. The 3R scenario would also
dramatically reduce the number of vehicles on theworld’s roads. This finding is true worldwide and for eachindividual country or region studied.
The following summarizes all key findings:•• The 2R scenario, which includes electrification
and automation but with a private-car-dominatedworld, may provide significant energy and CO 2

3R Scenario Global Results
Compared to the BAU case in 2050, the 3R scenario
produces impressive global results. It would:•• Cut global energy use from urban passenger
transportation by over 70%•• Cut CO2 emissions by over 80%•• Cut the measured costs of vehicles,
infrastructure, and transportation system
operation by over 40%•• Achieve savings approaching $5 trillion per year

savings, mostly after 2030, and only with largescaledecarbonization of electricity production. In the
2R scenario, vehicle travel rises higher than in the
BAU, but vehicle-related emissions and energy
use are eventually cut significantly, with specificCO2 reductions dependent on the extent to whichelectricity production decarbonizes around the world.
If the world’s electricity production is not completely
decarbonized by 2050, this scenario may produce
more CO2 emissions in 2050 than is consistent withtargets to limit global temperature rise to 2°C (or less)compared to preindustrial levels.
•• An autonomous vehicle (AV) world without
electrification (i.e. using ICEs) and without trip
sharing would not cut CO2 emissions out to 2050.
We estimate that the lower travel time “costs”
provided by self-driving vehicles would likely lead toa significant increase in vehicle travel, on the order of
15-20% compared to the BAU (with a wide range of
uncertainty). The increased efficiency of AVs would
offset some or all of this travel to keep energy andCO2 close to BAU levels; but it is the widespread useof electrification in AVs that dramatically reduces
vehicle-related pollution and CO2 emissions in this
scenario. The increased travel of AVs could trigger
more traffic congestion, though their improved roadspace efficiencies and coordinated travel patterns
might mitigate some of these impacts. We do not
attempt to estimate congestion impacts in this study.
•• The 3R scenario performs significantly betteron energy and CO2 , as well as on livability. This
scenario has the potential to deliver an efficient,
low-traffic, low-energy, and low-CO2 urban transportsystem around the world. In this scenario, thewidespread adoption of on-demand travel with
substantial ride sharing, along with greater use of(high-quality) public transport, cycling, and walking
reduces car travel by well over half in 2050, and the2Three Revolutions in Global Transportation

number of cars by nearly three-quarters comparedto our BAU. It would reduce traffic congestion andparking needs dramatically, opening up tremendous
amounts of urban space for walking, cycling, andother uses. This scenario – with energy use and CO2
emissions in 2050 less than one-third of the BAU andabout one-half that of the 2R scenario, and with fully
decarbonized electricity production – yields a very
low CO2 picture worldwide.
•• Ride sharing must deliver high-occupancy-vehicle
travel, both in light-duty taxi-style vehicles and insome larger vehicles such as minivans and small
buses. Ride hailing services do not help bring about
this scenario if they are dominated by single-occupant
trips. Thus, we distinguish “ride hailing” from “ridesharing” where the latter means separate trips areshared in a single ride. Our assumed load factors(average passengers per trip) in ride-hailed vehicles
rises over time in the 3R scenario and is about 3040% higher than in the 2R or BAU scenarios by 2050.
•• The 3R scenario also delivers large cost savings.
The costs of urban travel would likely be much lower
overall in the 3R scenario than in the 2R or BAU
scenarios, considering a wide range of out-of-pocket
costs, including vehicle purchase and operation,
fuel purchase, the costs of operating transportation
network companies (TNCs) as well as public transportsystems, and the costs of building and maintaining
road and transport infrastructure. These savings
emerge mainly after 2030 and relate mainly to lower
costs of vehicle purchase (given far fewer vehicles
purchased), energy cost savings, and road andparking infrastructure cost savings. The 2R scenario
saves some costs by 2050 compared to BAU fromlower-cost EV and AV operation and by eliminating
most drivers, but these savings are mostly offset byhigher cost vehicles and induced, increased travel.
•• Other potentially important benefits are moredifficult to quantify. Though not specificallycalculated, the value of CO2 and criteria pollutant
emissions reductions are potentially important in
the 2R scenario, along with the value of congestion
reduction in the 3R scenario. Both scenarios should
provide substantial safety benefits if automation lives
up to its safety potential and given the much less cardominant world in 3R. Quantifying these impacts is an
important area for further research.
•• The 3R scenario achieves its energy, CO2 andcost savings by creating a far more efficienttransportation system than in the BAU or 2R
scenarios, including:* Lower overall travel demand due to shorter travel
distances from more compact cities* More transportation choices, with walking andcycling rising significantly over time given safer
conditions and better infrastructure* A much larger share of travel provided by more 		
efficient modes (bus and rail systems as well as
smaller, right-sized vehicles, whose size 		
better matches travel demand)* A higher average load factor (people per trip)* More intense vehicle use, requiring far fewer 		
vehicles to meet passenger travel needs (since
personal vehicles currently remain idle 90+% of
the time)* Lower parking and road-building requirementsfrom less vehicle travel), with associated costsavings.
•• The 3R scenario would also dramatically reduce
the number of vehicles on the world’s roads by
2050. The current global urban stock of LDVs,
around 750 million (out of 1.1 billion total, urban +non-urban), reaches 2.1 billion by 2050 in our BAU
and 2R scenarios. In the 3R scenario it drops instead,
to about 500 million. In 3R these far fewer vehicles
are highly productive, carrying many more people on
more trips per day than average vehicles in the otherscenarios. Fewer vehicles in 3R allows the world tobuild far fewer parking spaces and lots, and frees
up considerable space for other activities. Total LDV
travel also drops by half in the 3R scenario comparedto the 2R scenario, meaning less congested andsafer roads. In turn, the urban landscape can be
repurposed and reoriented toward more cycling andwalking.
•• Our findings are broadly consistent across world
regions, despite very different starting points.
Since countries like the United States, with its cardominated transportation system, are very different
from, say, India, with its wide range of modes sharing
the streets, these scenarios also look quite different.
In fact, for India and most other emerging economies,
the high levels of shared vehicle trips in the 3R
scenario (at least in terms of the dominance of masstransit mobility) already largely exists, and the main
challenge is to preserve it. In general, those regionswith existing high levels of public transport, walking,and cycling see these travel modes decline in the
BAU scenario as well as in the 2R scenario, as cars
become dominant everywhere; in contrast, theyretain or gain on public transport ridership in the 3R
scenario, thanks to major investments in systems andstrong linkages with shared vehicle trips. A 50% or
3Three Revolutions in Global Transportation

greater reduction in both the numbers of vehicles andCO2 emissions in 2050 appears possible everywherein the 3R scenario vs. the BAU scenario.

Policy Implications of the Scenarios
The intensity of policies likely required to achieve eachscenario tends to increase moving from the BAU caseto the 2R and 3R scenarios, with the latter scenario
requiring the most ambitious policy scheme to achieve
the maximum societal benefits. All policies envisioned
in these scenarios would require some flexibility anditeration, as it cannot be fully known how various factors
will interact with each other. They would also probably
vary by country given local conditions and preferred
policy strategies. The following summarizes our policy
needs assessment:•• The 2R scenario will require a dual-policy focus
incentivizing EV uptake and enabling automation.
The scenario includes strong, proactive vehicle
electrification policy incentives, resulting in thewidespread adoption of EVs with steady 30% ormore annual increases in these vehicles for the next
20 years and beyond, and particularly rapid growth
between 2020 and 2035. Enabling policies include
ongoing purchase incentives and public awarenesscampaigns, strong government coordination andsupport of expanded EV charging infrastructure,research support, elimination of petroleum subsidies,
as well as electricity decarbonization policies such ascarbon taxes or cap-and-trade systems.
•• Achieving widespread driverless vehicles must
focus on barrier removal. The commercial vehicle
sector is eager to take advantage of the tremendous
cost-saving potential of automation technologies.
Therefore, the 2R scenario assumes that a relaxed
regulatory environment will enable rapid adoption ofdriverless cars. This differs from the BAU scenario,
where there is an assumption that heavy regulatoryburdens delay adoption of AVs. In the 2R scenario
the policy climate is favorable enough to ensure awidespread uptake of automation by commercial
fleets by 2025, with households following suit shortly
thereafter. But in 2R, on-demand mobility does not
grow, and preferences for private vehicle ownershipand solo driving endure, with longer trips and evenzero-occupant driving becoming commonplace.

contain as a core policy on-demand ride sharing
incentives, such as vehicle travel fees tied to vehicle
occupancy (such fees could also be applied to private
vehicles). These could also vary with the length oftrips, vehicle carbon intensity, and level of congestion.
Policies could also restrict or heavily charge forprivate ownership of AVs, and/or their undertaking ofzero-occupant trips. Incentives would urge a better
match between vehicle size and occupancy, which will
make travel more efficient. Bicycle and e-bike sharing
systems would be encouraged. Multimodal urban
planning investments in walking, cycling, and public
transit infrastructure and services would likely help
reduce and shorten vehicle trips. These investments
would be most effective if coupled with provenpractices such as implementation of compact, mixeduse urban plans centered on linking concentrated
development zones through public transit, andfeaturing designated cycling and walking zones.
•• In 3R, governments play a central role. The 3R
scenario may need governments to coordinate both
AV infrastructure and management of public andprivate trips, broadening the definition of publicly
funded transportation in favor of seamless regional
travel networks. Central to this transition is thegovernment role for filling gaps, and maintaining
equitable access and mobility for all individuals,
regardless of income, disability or access to asmartphone or vehicle.
Overall, this analysis suggests that a combination ofelectrification, automation, and multimodal shared vehicle
trips would bring by far the greatest societal benefits
for every country in this study. But achieving the full 3R
scenario will require unprecedented levels of policy
support; it will require creativity and vigilance to ensurethat not one or two, but all three, revolutions move
forward and to prepare cities around the world for a newera of travel.

•• The 3R scenario will require strong additional
support for ride sharing, public and active transport,
and land-use planning that helps to shorten mostvehicle trips. In addition to including those policies
assumed for the 2R scenario, the 3R scenario would
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1. Introduction
During the 20th century, several revolutions occurred intransportation systems around the world – most notably
the internal combustion engine (ICE), mass productionof automobiles, high speed urban and interurban railsystems, and construction of major roadway and limitedaccess expressway networks. However, in the latter partof the century innovation slowed. Now, in 2017, mostpeople still move around cities primarily in vehicles withICE gasoline or diesel engines, always with a driver, andoften with the only occupant serving as driver.
This report will discuss how the following threeadvancements in technology are set to make dramaticchanges:•• Electrification – After an initial surge and rapid
decline in the late 19th century, the electric vehicle(EV), either hybridized with engines or entirely runningon batteries, has re-emerged as a viable technology.
By early 2017, 2 million electric and plug-in hybrid
passenger cars (and 200 million 2-wheelers) areplying the world’s roads (Lutsey, 2017).
•• Automation – Although they are not commercial
yet, technologies to automate vehicles, eventually
including eliminating the need for drivers, are moving
rapidly. U.S. Society of Automotive Engineers (SAE)level 4 driverless cars appear on track to beginentering commercial fleets by the early 2020s (SAE,2016).

never fully materialize. They may go in different directions
and interact in unpredictable ways. They could lead to“heaven or hell” scenarios, depending on the impacts
on travel, traffic congestion, safety, energy use, andemissions that result from their combined uptake (Chase,2014).
Despite the uncertainty, there is a growing literature onthese phenomena individually, and an emerging one on
their potential interactions (Anair, 2017; Cohen & Shirazi,
2017; Handy, 2017; Ory, 2017; Polzin, 2017). However, thisis one of the first studies that attempts to craft coherent
future transitional scenarios. Via three scenarios we
explore the potential impacts of the three revolutions
on travel patterns, vehicle sales and stocks, energy use,
CO2 emissions, and costs. We undertake this examination
on a worldwide basis and with individual results for key
countries and regions.
Many other scenarios could be developed, but the
specific ones chosen for this report are designed tohighlight a technology dominated world with and without
an additional revolution in the travel patterns the world
adopts.

•• Shared vehicle trips – While public transportsystems emerged around two centuries ago, andtaxi services and carpooling have allowed people toshare trips for decades, new technology creates thepotential for nearly all trips to be easily shared among
multiple riders. This development could revolutionize
transportation. Cutting the cost of ride hailing in half
or more, ride sharing has the potential to attract
large numbers of travelers and dramatically cut the
numbers of vehicles on the world’s roads. However,these benefits are only significant if they reduce thenumber of trips taken. Taxi services or transportation
network companies (TNCs) that are not shared do not
reduce trips.

These three revolutions are highly uncertain in many
ways. They may happen soon, take decades to mature, or
5Three Revolutions in Global Transportation

2. Study Design, Methodology, and Scenarios
This report builds on two previous urban travel studies
undertaken by UC Davis and ITDP: “A Global High
Shift Cycling Scenario” (2015) and “A Global High ShiftScenario: Impacts and Potential for More Public Transport,
Walking and Cycling with Lower Car Use” (2014). These
reports developed an approach of comparing a businessas-usual (BAU) future to one that considers dramaticallyincreasing investments into sustainable transportation
infrastructure along with other measures to promote
efficient modes, namely public transport, cycling, andwalking. These “High Shift” studies envisioned cities thatare far less car-dependent, energy- and CO2-intensive,and – perhaps surprisingly – far less expensive forsociety.
This study uses the same basic methodology as theprevious studies, including the development of a BAU
scenario and several high shift scenarios. However, here
we are considering a more complex set of dynamics
than in the previous studies, since this includes an effort
to understand the combined impacts of three separate
revolutions. And although this approach is built upon thefoundation of the other studies, it is worthy of a different
title than the other studies, hence our “3 Revolutions”
frame.
This study specifically considers the following:•• Explicit pathways for increased electrification in all
types of urban vehicles and modes, and plausible
time frames for a “revolution” electrification case ineach world region.
•• Plausible pathways and time frames for the
introduction of vehicle automation, and assumptions
about the characteristics of these vehicles and how
they may change over time.
•• The potential for increased trip sharing outside of
public transport systems, and better integration
between smaller vehicles and public transport
vehicles such as small and large buses, and trunk
metro, bus rapid transit, and light rail systems.
•• Levels of public transport ridership, cycling, andwalking in our 3 Revolutions case that are consistent
with those in our High Shift reports, though themassive expansion of shared trips in smaller
commercial vehicles leads to some adjustments inpublic transport, cycling, and walking levels.

As we did in the previous two High Shift studies in thisseries, we utilize basic data and projections from the
Mobility Model (MoMo) created by the International
Energy Agency (IEA, 2016), though we have created a new
spreadsheet tool specific to the current study, focusedon modeling the three revolutions and considering
scenarios where they are combined in different manners.
Our projection system tracks the numbers of vehicles
of all major types, their cost, daily and annual travel,
average passenger loadings, fuel use, and CO2 emissions
worldwide, broken into eight countries and regions.
These variables are linked using basic arithmetic
relationships that allow, for example, the calculation of
total vehicle and passenger kilometers for each modeand summed across modes, total numbers of vehicles
in use, total vehicle and passenger kilometers (pkm) oftravel, total fuel use and CO2 emissions, etc. The data
have generally been validated for 2015, but in somecases broad assumptions are made, such as the average
passenger loadings in buses in parts of the world. We
track the stocks of all vehicles but only track sales of lightduty vehicles (LDVs), both household and commercial
LDVs, and how changes in the number of vehicles
needed to deliver the transportation service (measured
in pkm) translates into sales, taking into account vehicle
usage rates and lifetimes, such as the faster turnover ofcommercial vehicles given their much more intensive daily
use than household vehicles.
The analysis is conducted across eight countries andworld regions: United States, Organization for Economic
Cooperation and Development (OECD) Europe, China,
India, Brazil, Other Americas, Africa/Middle East, andOther Europe/Asia. The particulars of each region arereflected in the initial data and BAU projections for each.
For example, some regions, such as India, already havevery high levels of shared vehicle trips, including public
transport use. Others, like the United States, have very
low levels of shared vehicle trips, while Europe falls in the
middle. Our results bring out some of these particularitiesof each region, though in this main report we focus on
global totals and offer some examples from different
countries. We plan to produce separate materials withmore details on our results by country/region.

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Three Revolutions in Global Transportation

In this report, we consider three main scenarios:•• Business as Usual (BAU) – Current trends continue
without any revolutions. ICE vehicles remain dominant
through 2050. All vehicle trips continue to require
drivers. In countries like the United States, 85%of trips remain in cars (of increasing size), mostfrequently with a single occupant. In other countries,
public transport shares decline as ridership grows
only slowly while car ownership and travel steadilyrise. This scenario may or may not be likely, but in any
case, it provides a useful basis for comparison withthe other three scenarios.
•• 2 Revolutions (2R) – In this scenario, we consider
electrification and automation. It is natural to thinkthat these revolutions will co-evolve because of theirco-benefits; electric autonomous vehicles (AVs) can
recharge themselves easily at convenient times, andEVs can easily supply power to the hardware needed
to automate vehicles. EVs can also help to lower the
per-kilometer travel cost of high-use AVs. Electric,
driverless vehicles likely will be expensive to produce,at least over the next 10-20 years, but inexpensive
to operate both privately and commercially. It is
certainly possible to have one without the other (ineither direction), but together they provide a truetransformative technological and travel revolution.

•• 3 Revolutions (3R) – Here, the third revolution, an
increase in shared vehicle trips, is overlaid on the first
two. We view this revolution in an expansive sense:
private vehicles replaced with ride hailing of TNC
vehicles, shared vehicle trips leading to much higher
average vehicle occupancy, and all this coupledwith a strong role for public transport and active
travel. These all fit together well since the world withmore shared vehicle trips will see vastly fewer carsand open up an enormous amount of urban space
for things like walking and cycling. Of the three
revolutions, widespread shared mobility may be themost challenging to achieve and most dependent onstrongly supportive policies. The potential for getting
large numbers of people to share rides is highly
uncertain, especially as travel costs drop from theother two revolutions. Strong financial incentives will
likely be needed to encourage trip sharing and use of
public transport in the face of otherwise cheap pointto-point services in single-occupant services.
We also briefly consider the potential impacts of therevolutions individually, and as suboptimized versions of
the main scenarios.

Synergies Achieved by Combining Revolutions
Much of the analysis in this paper hinges on the types of synergies that could occur by combining theserevolutions. Some of these synergies are listed below (Anair, 2017):•• Electrification can assist in the power and electronic demands of AVs.
•• Automation can assist electrification in terms of battery operation and recharging management, suchas automatically seeking opportunities to recharge during slow periods.
•• Similarly, AVs can help manage recharging of shared vehicles between trips and extend their effective
daily driving range in this manner.
•• Automation can lower the costs of sharing vehicle trips including public transport services by
eliminating driver costs, which can be 50% or more of ride-hailing costs. However, this also could lower
the costs of non-shared ride-hailing trips enough that there is less incentive to share trips or even to
take public transport.
•• Trip sharing and strong public transport can help overcome the tendency of automation to trigger
increases in travel, as consumers will pay for trips at the margin, and may continue to budget their timespent in travel in a similar way as they do today (rather than purchase more comfortable vehicles andspend more time in them).
•• Widespread trip sharing and use of public transport can cut the number of vehicles in use dramaticallyand reduce traffic levels and congestion significantly, and (on a societal basis) provide cost savings that
more than offset the higher purchase costs of automated EVs.

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Three Revolutions in Global Transportation

3. The Three Revolutions: Status and Potential
This section provides a brief introduction and status reporton each of the three revolutions, taking into accounttechnology development, extent of market development,
and various barriers that the revolution faces to achieving
large scale adoption.

Electrification
Electric vehicles have already arrived: in 2016, the numberof different (4-wheel) models available in countries aroundthe world exceeded 100, including everything fromelectric minicars to plug-in hybrid sport-utility vehicles(Fulton, Seleem, Boshell, Salgado, & Saygin, 2017). In
2017, several 200+ mile (300 kilometer) all-electric models
will be introduced, possibly ushering a new era of higher
range EVs, and reducing what has been a major barrier towidespread adoption. And yet the global market share of
EVs is less than 1%. Are they succeeding or failing? Here
we briefly review their status and consider their future
potential.

Global Electric Vehicle Market
Varying levels of vehicle electrification, including
traditional hybrid vehicles (that don’t plug in), havebecome available across popular vehicle platforms
over the last two decades. The year 2011 stands as the
beginning of the modern era of lithium-ion battery electricand plug-in hybrid vehicles, with the introduction of the
Nissan Leaf and Chevy Volt. Since then there has beena steady increase in global sales, with cumulative global
sales reaching 1 million in 2015 and recently passing 2
million. In 2016, China led the world in EV sales at about
630,000, while Norway led in EV market share of its
country’s total auto sales at 29% (Lutsey, 2017).
Although they are not plug-in vehicles, hybrids have
proven both cost-effective and popular for taxi fleets. As
an example, nearly 66% of the New York City medallion
taxis were hybrids in November 2016 (calculated fromNYC Open Data). Meanwhile plug-in vehicles have nosignificant presence in taxi fleets and mixed success in car
share fleets.

Vehicle Technology Challenges
Despite the progress in EV sales, growth has been
inhibited by several factors, including limited driving range(of most fully-electric vehicles), higher vehicle purchasecost, greater time to charge an electric battery, a lack ofpublic charging stations, and a recent decline in oil prices
worldwide.
Increased driving range would enable more effective
deployment of EVs, especially in car share and ride share
fleets, because range is inversely related to frequency ofneeding to pull a vehicle out of operation to recharge. The
recent and expected future trend in EV models is in thisdirection. Among the many examples, the 2017 BMW i3
EV with a 94 Amp-hour battery offers 114 miles of electricrange, up from 81 miles in the 2016 model. Notably,the 2017 Chevrolet Bolt EV offers 238 miles of range, asubstantial improvement over the 82-mile range of thesmaller 2016 Chevrolet Spark EV.

Charging Infrastructure
Greater density of charging infrastructure makes EVs a
more viable option. More specifically, availability of fastcharging stations is necessary for car share – and evenmore so for ride share – fleets to minimize recharging
downtime. Car share and ride share fleets have lower
handling costs if their vehicles are closer to EV charging.
BMW ReachNow’s European sibling, DriveNow, andDaimler’s car2go electric fleets operate in some cities
with a high density of charging infrastructure, including
Copenhagen, Vienna, and Amsterdam. In Seattle andPortland, ReachNow’s fleet is composed of roughly 20%
BMW i3 EVs, with the balance in other BMW and Mini
gasoline cars.
In contrast to these success stories, in 2016 car2goreplaced approximately 400 Smart EVs serving 40,000
members in San Diego with gasoline Smart cars, citinga lack of charging stations, range anxiety, and a 20%
unavailability rate due to charging time or a low state ofcharge (Garrick, 2016). This outcome speaks to charging
density as a prerequisite for EV use in car share fleets.
Overall the technology and market outlook for EVs
appears promising, though the timing of when thetechnology will translate into a “revolution” remains to be
seen. As we discuss in the policy sections of this paper,
it seems likely that strong supporting policies will beneeded for many years to achieve a full transition fromtoday’s dominant ICE vehicles.

8

Three Revolutions in Global Transportation

Automation
The technology to enable vehicle automation has
emerged rapidly, causing much excitement andgenerating much attention, but currently little driving
automation is used in commercially available vehicles.
Significant technological, legal, and cultural hurdles must
be addressed before fully automated vehicles take to theroads. This section describes the types of automation
available, discusses associated costs, and provides
insights on when these technologies are anticipated tomove forward.

Automated Technology
The U.S. Society of Automotive Engineers (SAE) (U.S.
Department of Transportation, 2016) defines a full rangeof automation levels (SAE, 2016). Level 1 is widespreadand level 2 is rapidly being introduced in many models.
Level 3, including hands-free driving, is just emerging
and only legal in some areas. Levels 4 and 5, with true
full-time driverless operation, is not known to be fully
legal anywhere in the world as of early 2017, except for
operation by test fleets.
For this study, when characterizing and projecting
automation we only consider levels 4 and 5 driverlessvehicle operation. This choice reflects a necessity toreach complete automation to see the types of major
impacts on travel patterns that we assume are associatedwith automation, such as people willing to spend moretime in vehicles, and vehicles designed with passengers
much more than with drivers in mind. In a fully AV world,
especially where these vehicles are privately owned
and operated, we expect to see some larger, more
comfortable vehicles (perhaps even some with sleeping
capability), and expect to see some increase in theamount of driving these vehicles (and people who ownthem) do each year. Empty running (i.e. zero-occupant)vehicles may also emerge as a significant new source oftraffic.

Incremental Purchase Cost of Automation
One major question surrounding AVs is their cost. Thereis a wide range of reported costs of current prototypes
compared to conventional vehicles. Future cost estimatessuggest strong reductions over time, as shown in Figure 1.
A key reason for the large spread and rapid reduction incost estimates by 2020 is the rapid decline in some key
component costs. The cost of LiDAR (Light Detection andRanging) was estimated to be near $75,000 in 2014, andby early 2016, Velodyne began selling a form of LiDAR for

Figure 1. Estimates of past and projections of future incremental cost of AVs over conventional vehicles. EnoTransestimates are low for 2013 because they assume mass production. BCG: (Davies, 2015); EnoTrans: (Fagnant &Kockelman, 2013); Google Car: (“Google’s Autonomous Vehicle,” 2012); IHS: (IHS Markit, 2014); RMI: (Johnson &Walker,2016); Tesla: Tesla lists “Enhanced Autopilot” for $6,000 and “Full Self-Driving Capability” for an additional$4,000 on its web site when you configure any of their cars. While cars can be ordered with this functionality, Tesla
has not enabled full self-driving capability on the software end as of this paper’s publication.
9

Three Revolutions in Global Transportation

$500 per unit to Ford (McFarland, 2015). Furthermore, thecomponents used for full automation may change overtime, or may vary by manufacturer. For example, Tesla
has indicated no plans to use LiDAR. It seems reasonable
that by 2025, AVs will cost, at most, $10,000 more thanequivalent conventional vehicles. Similarly, electrificationof the drivetrain and cost of batteries together may
cost about $10,000 more than a conventional vehicle,
reflecting declining battery costs but rising energy storageof batteries on the average EV. Our EV cost projections
are roughly consistent with reports such as (McKerracher
et al., 2016), though somewhat lower than reports that use
higher future battery cost projections such as (Elgowainy
et al., 2016).
We assume that the two together cost about $18,000
more than a comparable ICE vehicle. This drops to about$10,000 by 2050. Even by 2030, autonomous EVs used
intensively in car sharing roles are estimated to earn backthe higher purchase cost from energy savings within theirfirst three years of operation.

Timing of Autonomous Vehicles
Perhaps the biggest question with AVs is when they will
really hit the streets. While some analysts still believe
it may be decades before these vehicles overcome alltechnical and legal barriers, many automakers are stating
they will have models ready in the near future, including
some indicating a 2020 or 2021 time frame, as shown in
Table 1.
AVs are restricted in most countries at this time, however,policies to reduce some restrictions are emerging.
Hands-free driving and testing of fully driverless vehicles
are allowed in certain areas. In the United States,
the Michigan state legislature recently passed a law
permitting automakers to operate networks of self-driving
taxis within the state, perhaps one of the first jurisdictionsin the world to do so. Overall we assume a limited rolloutof driverless vehicles through the early 2020s in leading
countries, followed by mass market rollouts beginningabout 2025.

Table 1. Examples of AVs in development
Company

Vehicle Brand

ModelPowertrain

Production
Goal

Notes

NissanNissanLeaf

Electric

2020

GM

Chevrolet

Bolt

Electric

Testing 40 cars in
San Francisco andScottsdale

FCA

Chrysler

Pacifica Hybrid

Plug-in Hybrid

Testing 100 vehicles
with Google

Ford

Ford

FusionHybrid

Volvo

Volvo

XC90

HybridUber

Ford

Fusion Energi

Plug-in Hybrid

Uber

Volvo

XC90

HybridDaimler

MercedesBenz

F 015 Luxury in
MotionHydrogen Fuel
Cell Plug-In
HybridResearch Vehicle

HyundaiHyundai

Ioniq

Electric

Testing 3 vehicles inSouth Korea

HyundaiHyundaiTucsonHydrogen Fuel
Cell

Testing 2 vehicles inSouth Korea

2021

10 Three Revolutions in Global Transportation

Potential Energy Impacts of Large-scale Vehicle Automation
A wide range of potential impacts of full vehicle automation have been discussed in the literature (Beiker &Meyer, 2014). In terms of energy use (and consequently CO2), the range of potential impacts is estimated to be
wide and uncertain, due to impacts on many aspects of travel and vehicle efficiency (Brown, Gonder, & Repac,
2014; Wadud, MacKenzie, & Leiby, 2016). As shown in Figure 2, these include improved technical vehicle
efficiency, eco-driving, reduced traffic congestion and platooning. On the other hand, reductions in travel
cost and new traveler groups could lead to significantly more driving, while faster driving and increased useof energy-using features could lead to more energy use per kilometer. The net effects tend toward significant
increases in driving and efficiency, with a wide range of possible net impacts on energy use, from large
increases to large decreases. As discussed later in the report, we use fairly conservative estimates on most ofthese impacts and their combined effects, but acknowledge the uncertainty.

Figure 2. Potential changes in energy consumption due to vehicle automation
Source: Wadud, MacKenzie, & Leiby, 2016

Shared Mobility
Shared mobility has grown substantially around the world in the past five years with the introduction and growth ofmany new business models. The following definition is illustrative:
Shared mobility – the shared use of a vehicle, bicycle, or other mode – is an innovative transportation
strategy that enables users to gain short-term access to transportation modes on an ‘as-needed’ basis.
The term shared mobility includes various forms of carsharing, bikesharing, ridesharing (carpoolingand vanpooling), and on-demand ride services. It can also include alternative transit services, such asparatransit, shuttles, and private transit services, called microtransit, which can supplement fixed-routebus and rail services. With many new options for mobility emerging, so have the smartphone ‘apps’ that
aggregate these options and optimize routes for travelers (Shaheen, Chan, Bansal, & Cohen, 2015).
The terms ride hailing and ride sharing have become somewhat equated in common usage but should be kept
differentiated, since a hailed ride is not necessarily a shared ride.
11 Three Revolutions in Global Transportation

Ride Sharing Versus Ride Hailing, and Related Terms
There has become considerable confusion around and misuse of the terms “ride sharing” and “sharedmobility” in recent years. We clarify these terms here, and contrast these with “ride hailing.” We also
introduce the term “trip sharing.” There are two basic concepts:•• Ride sharing (or trip sharing or shared mobility) – This refers to rides or trips that are actually shared
between different individuals or different parties and paid separately. It can also more broadly include
public transit services.
•• Ride hailing (or ride booking) – This refers to any app-based system to secure a ride from a taxi or other“on-demand” ride service provider such as GrabTaxi, Uber, Lyft, Ola, Easy Taxi or other TNCs. These rides
may or may not be shared.
It is important to keep the two concepts separate. On-demand ride-hailing services are not ride-sharing
services unless they exclusively offer shared rides (such as a micro transit bus system). We consider it
misleading to use the terms “ride sharing” and “shared mobility” to refer to a ride-hailing service in a
generalized manner, and while we use these terms to refer to any situation with truly shared trips, we try to
avoid their use for the more general situation of ride hailing, throughout this report. We also use “trip sharing”
in this report to emphasize true shared mobility, and to avoid overuse of the terms ride sharing and sharedmobility.

The following section discusses the future potential ofeach shared mobility type and explores the ability ofsharing to decrease the total number of vehicle kilometers
traveled on streets by increasing the occupancy ofvehicles for trips.

Ride Hailing and Ride Sharing
The rise of TNCs like Ola, GrabTaxi, Uber, and Lyft has
been especially rapid since about 2012. Launched in
2009, Uber reached 1 billion trips worldwide by the endof 2015, and 2 billion within the following six months.
Currently, the environmental impact of ride-hailingservices is receiving considerable attention in the UnitedStates, with a key question being whether rides tend
to substitute for higher CO2 trips. At issue is whether
TNCs help cut car ownership and use rates generally,or whether they compete with public transport, thereby
undermining CO2 benefits (Alba, 2015). Another questionis whether shared rides account for a significant share oftrips.
In any case, ride sharing use is spreading to all parts of
the world, at least to major cities. Figure 3 shows maps
from late 2016 for South America, China, and Southeast
Asia reflecting the spread in these services.

Figure 3: EasyTaxi availability in South America (left); KuaidiONE availability in China (middle); GrabTaxi availability
in Southeast Asia (right)
12 Three Revolutions in Global Transportation

Potential Impacts of Largescale RideSharing Services
If most people switched to ride-sharing services, withmostly shared trips, instead of driving their own vehicles,
the numbers of vehicles that would be needed tomove people around would decline dramatically – bothbecause of higher load factors and the intensive useof each vehicle. A recent MIT simulation found that,mathematically and logistically, a fleet of 3,000 vehicles
with capacity of four passengers, or 2,000 vehicles withcapacity of ten passengers could meet 98% of the trip
demand of 14,000 New York City taxis, with ride wait times
averaging just 2.7 minutes (Alonso-mora, Samaranayake,
Wallar, Frazzoli, & Rus, 2017). Similarly, the International
Transport Forum developed a simulation of Lisbon
showing that the city could serve its typical daily travel
patterns with only 10% of the vehicles currently used, witha combination of 8- and 16-passenger vehicles (OECDInternational Transport Forum, 2015). Of course, thesesimulations assume people are quite adaptable, readyto make major changes to how they conduct their daily
travel, abandon the use of their own vehicles, and arewilling to get into vehicles with strangers. (In a world withsmall autonomous taxis, rides between two people would
be shared without even a driver on board).
While ride sharing can cut the numbers of vehicles inuse, cutting traffic is not assured: for example a systemdominated by single-occupant ride-hailed vehicles would
not reduce vehicle trips compared to similar private
vehicle trips; moreover, rides could be shifted from public
and/or active transport. The International Transport Forum(ITF) study showed that a shift from walking and cycling toshared vehicle travel could result in a significant increasein vehicle kilometers traveled, thus increasing congestion
and travel times, even as vehicle occupancies increase(OECD International Transport Forum, 2015). A recent
analysis of New York City indicates that the rapid increasein ridership in TNC vehicles in 2015 and 2016 coincidedwith a decline in bus and metro travel during this period(Schaller, 2017). A world of ride hailing, but without true
ride sharing and strong public transport and active travel
aspects, could be a very high traffic, congested, higher
energy-use and CO2 world.
Thus the success in ride sharing as an energy-efficientand space-efficient mode will depend both on the
average number of riders per trip, which must be
significantly higher than modes like private automobiles,
and on its ability to draw riders from these less-efficientmodes, rather than from public transport services. This isparticularly important in countries where public transport
ridership and vehicle load factors are very high – i.e. mostof the developing world.

Car Sharing and Bike Sharing
As of 2017 there is a much smaller market for car sharing
than ride sharing in most countries; however, the general
concept of car sharing offers a transportation solution
for users who don't own a car but would like occasional
access to a car for more than a single short trip. Car
sharing offers the benefit of serving more people pervehicle than if those people were to use private vehicles,
resulting in less need for parking and user cost savings
through more efficient vehicle utilization. Environmental
benefits can be achieved if the car share vehicles on
average have lower emissions by being of newer modelyear than private vehicles they replace, and by inducing
a net reduction in VMT because of consumers’ perceived
higher per-mile costs.
Car share business models include traditional round-trip,
one-way and free-floating, peer-to-peer, and fractional
ownership. Within these models, charges can include anhourly rate, and in some instances, a per-mile charge as
well.
Bike share platforms are becoming increasingly popularfor commuters in dense urban areas worldwide. Most
systems are station-based and allow one-way trips,
though “dockless” cycling systems have emerged, withsome advantages (Handy, 2017). Electric bicycles, ore-bikes, can offer an even more utilitarian transportation
option than regular bicycles in bike share. In a recent
study, e-bikes were found to be used for trips twice thedistance of regular non-electric bike share bicycles in
China (Campbell, Cherry, Ryerson, & Yang, 2016). In ourprevious High Shift Cycling study we found that bikesharing has reached more than a thousand cities in
the past few years, though still represents a tiny shareof cycling trips around the world (Mason et al., 2015).
Continued growth could change this, and bike sharesystems can also introduce many new people to urban
cycling, who eventually acquire their own bicycle.

Dynamic Shuttle Services
A dynamic shuttle is a smaller shuttle bus that can serve
more passengers than a taxi, and offers a more flexible
transportation solution than traditional fixed-route public
transport buses. Larger on-demand bus systems also
exist, such as Chariot and Bridj, with the prospect ofwidespread on-demand microbus services around theworld as a potentially optimal size and low-cost travel
option. This approach can also help public transport
agencies become more efficient and cut costs, byproviding targeted on-demand services in areas that havetrouble supporting standard fixed-route services due toinsufficient ridership.
13 Three Revolutions in Global Transportation

However, in the event of an increase in the use of ondemand services, it will be important that systems
and governments ensure that shared mobility remains
affordable for a range of people who depend on public
transport, including people with disabilities, older adults,
and low-income passengers who have benefited fromsubsidized public transit services (Polzin, 2017).
Increased use of shared mobility systems, public transport
and active travel (walking and cycling) may present thegreatest potential of the three revolutions to usher in an
era of sustainable transportation in cities. The benefits
they offer include traffic reduction, energy and emissions
reductions, and lower overall systems costs (Handy, 2017).
We explore this in the scenarios presentation that follows.

14 Three Revolutions in Global Transportation

4. Future Scenarios: BAU, 2R, 3R
Given the major interactions associated with the three transportation revolutions, as well as for economy ofpresentation, it makes sense to consider our 3 Revolutions scenarios together, and in the context of the business-asusual case. Table 2 presents key characteristics included in each scenario. As can be seen, the scenarios are built as aseries of progressions adding an additional layer at each step. The basic characteristics of each scenario follows.
Table 2. Key characteristics of the 3 Revolutions scenarios
Use ofAutomation

Use ofElectrification

Use of Shared
VehiclesUrban
Planning/
Pricing/TDM
Policies

Aligned with
2°C (or Lower)
Scenario

BAU, limited
Intervention

Low

Low

Low

Low

No

2R with highelectrification,
automation

High

High

Low

Low

Maybe

3R withhigh sharedmobility,
publictransport,
walking andcycling

High

High

High

High

Yes

The BAU Scenario
As presented in the previous reports, the projected future
growth of urban travel around the world is several fold,
and up to tenfold for particular countries and regions.
Total urban (metro area) population is projected by theUnited Nations to increase by 60% from about 4 billion
people in 2015 to 6.5 billion in 2050; these urbanites
are projected to collectively become more than twice as
wealthy as the average urban dweller worldwide is today(with poorer countries such as India seeing a fivefold or
greater increase in incomes, though to levels that remain
far below OECD countries, and many or even mostpeople in poorer countries won’t have access to private
cars in 2050). As a result of this city growth and income
growth, mobility levels will skyrocket. For example, theIEA projects a nearly tenfold increase in car travel in India
between 2010 and 2050.
Our BAU scenario reflects these projections. While
many cars will exist outside urban areas, just the urban
population of cars around the world grows from 750
million in 2015 to 2.1 billion in 2050. Traffic congestion
increases commensurately – even if many new roads are
15 Three Revolutions in Global Transportation

built to accommodate traffic increases, countries will havea very hard time keeping up. Cities around the world are
already heavily congested with car traffic, sometimes with
only a very small level of car ownership. Whether the BAU
projection of cars and car travel can be accommodated
without complete seizures of road traffic networks is itself
a fair question, and thus our BAU scenario that assumes
functional road systems may be unrealistic in this regard.
This future also assumes that investments into alternativetransportation modes – everything from buses and railsystems, to bicycles and even an extensive system of safe
sidewalks in cities – lags behind what is needed to retain
current mode shares, as more people gain access to cars
(as well as motorized 2-wheelers). This is a world where
people fear crossing the street due to traffic, fear cycling
due to lack of safe cycling infrastructure, and sometimes
fear taking public transport due to personal safetyconcerns. This is also a world where public transportsystems are not well designed and do not have adequate
investment to ensure they are of high quality and highcapacity. We do not see rapid growth, for example, in bus
rapid transit systems, which allow buses to cut throughgridlocked car traffic and move people faster than they
can in private vehicles.
This future results not only in possible gridlock in theworld’s cities, it likely fails to stem the trend of high injury
and death rates on the world’s roads – over 1.2 million
deaths in 2015 alone. And a world that continues torely on ICE vehicles, even though these will continue tobecome cleaner, may have trouble achieving truly clean
air in its larger cities. This is on top of our BAU projectionof a 50% increase in energy use and CO2 emissions
during a period where CO2 emissions must drop
dramatically to achieve a 2˚C or lower temperature limit toarrest climate change.

The 2R Scenario: Electrification
and Automation
This scenario includes both a rapid increase in electric(non-autonomous) vehicles and, later, autonomous EVs.
There are many important assumptions and these are laidout below.

Assumptions for EVs•• By 2020, 5 million EVs are sold annually worldwide(compared to 750,000 in 2016), with sales
continuing to rise sharply thereafter. The steep partof the “S-curve” of sales for EVs occurs between
2020 and 2030 in the world’s leading nations andregions (the United States, Europe, China and Japan).
Other nations follow and by 2040 automated EVs
dominate LDV sales worldwide. EVs also dominate
sales of 2-wheelers and buses after 2030 worldwide.
•• By 2050 few non-EVs are sold anywhere. SomeEVs may well be plug-in hybrids, but even these we
assume are basically phased out by 2050 as longer
range EVs and fast charging become ubiquitous.
•• By 2030, EVs will have an average range of 250
miles (400 kilometers) and an incremental costof about $10,000 per vehicle. Costs could decline
more than this, but our assumed increase in driving
range requires larger battery packs, which we take
into account in our cost estimates. These incremental
costs continue to decline to near zero in 2050. EVs
in 2030 save enough on energy costs to pay backwithin five years, even less for high-distance drivers.
•• By 2050 electricity grids are substantiallydecarbonized worldwide. In our main 2R scenario,
we assume that electricity grids are decarbonized
at a steady rate, consistent with the IEA “4DS” (4˚Cscenario). This means that electric AVs can achievesignificant CO2 reductions, even if they drive many
trillion kilometers per year. However, as we show
below, in a 2˚C or lower world, grids must be nearlycompletely decarbonized worldwide by 2050.
We consider this as a sensitivity case where EVs
worldwide become truly zero-carbon vehicles by
2050.
•• A critical assumption is supportive government
policies. This scenario probably does not happen
without strong policies to encourage uptake of
EVs as they continue to mature. A range of policies
are already in place in many countries (including
incentives for installing charging infrastructure,access and parking advantages, and tax incentives)
and these must continue as EV markets develop. In
particular, it seems likely that vehicle price incentives
will be needed through 2025 or 2030 given ongoingincremental first costs, though what levels will beneeded over this time frame to spark and sustain an“S-curve” revolution are far from clear.
Electrification rates of new LDV sales in two example
countries, the United States and India, are shown in Figure
4. Private vehicles experience slower electrification thancommercial vehicles until about 2030 then catch up.

16 Three Revolutions in Global Transportation

Figure 4. EV sales shares for selected countries, vehicle type, years and scenarios

Assumptions for Automated EVs•• Commercial availability of fully driverless vehicles
begins shortly after 2020. In the more advanced
economies such as the United States, Europe, Japanand China, vehicle automation (and policies thatallow or promote it) advance in the coming five years
through various levels, such as hands-free driving,fully autonomous capability but with a driver in theseat, to fully driverless (SAE level 4: no steering
wheel or pedals) by 2020 or soon after (SAE, 2016).
Such vehicles enter full commercial operation withlargescale production by 2025.
•• The cost of driverless vehicles declines rapidly, as
it has already started doing. While in small volumes
these vehicles may costs tens of thousands of dollars
more than conventional vehicles, we assume that by
2030, in reasonably large volumes these vehicles areabout $10,000 more expensive than conventional
vehicles, and down to $5,000 more by 2040, apartfrom possible new features such as entertainment
systems in more comfortable, larger interiors. The
combination of electrification and automation isestimated to cost close to $20,000 more thanconventional vehicles in 2030, declining to below$10,000 more in 2040. Energy savings over the lifeof the vehicle offset some of the higher first costs,
especially for commercial, high travel, vehicles.
•• Due to policies described below, fully driverlessvehicle sales in leading countries ramp up rapidly

after 2025. By 2030 sales are entering a steep“S-curve” phase, where most commercial enterprises
go driverless, and where most households in leading
countries choose to buy a driverless car by 2035.
Other countries follow by five or at most 10 years, witha high level of driverless vehicle sales in all countries
by 2045.
•• AV sales start with commercial operations, buthouseholds follow soon after. It appears likelythat the strongest business cases for adopting
driverless vehicles will be commercial operators whootherwise pay their drivers, so it can be expectedthat these (including TNCs and some public transport
operations) will be early adopters. However, it also
seems likely that “pioneer” households will adopt
these vehicles as soon as possible. Thus we havethem follow soon after, and as part of the 2R scenario,
these households use their own AVs into the future
rather than significantly increase use of sharedmobility systems.

17 Three Revolutions in Global Transportation

As shown in Figure 5, AV sales are assumed to increase rapidly after 2025 in the 2R scenario and reach up to 25%
market share in 2030 in leading countries. Commercial vehicle fleets are assumed to be rapid adopters and reachhigher shares by 2030, but since there are many more private vehicles in the 2R scenario, the total numbers of AVs inhouseholds eventually far surpasses those in commercial fleets. In trailing countries such as India the process happens
more slowly, but sales of AVs to commercial operations still approach 20% by 2030, with households and fleets
reaching 50% or more by 2050.

Figure 5. AV sales shares for selected countries, vehicle types, years, and scenarios•• Household-owned driverless cars will be larger andmore comfortable. AVs may be designed to be much
more comfortable and to better support non-driving
activities, with amenities such as “mobile offices”
or with home theatre video systems. We assume asignificant increase in SUV-sized vehicles (or evenlarger vehicles such as vans but with similar weight
and fuel economy as today’s larger SUVs), offsettingsome of the energy savings of electrification. And with
the increased comfort and elimination of driving, thetime cost of driving in AVs will be significantly lower,
since people won’t mind being in their vehicles forlonger periods of time when they do not have to drive
them, and can conduct other activities.
•• Given the reduction in time cost, people will drivein their vehicles significantly more than they dotoday. In all regions of the world, we assume a 10-15%
increase in driving per capita (and per vehicle) in
personal AVs relative to BAU. This could also include
increases in zero-occupant vehicle travel, as people
assign vehicles to conduct tasks such as retrieving
family members or even packages. We assumeanother 5% increase in vehicle travel from this in our
scenarios, resulting in an overall 15-20% increase in
vehicle travel, though we acknowledge the effectcould be more significant. (As described for 3R below,

in contrast to privately owned vehicles, we assume
only a small travel increase for publicly shared AVs
since their use will be paid per kilometer of travel.)•• The rapid rise in the use of driverless cars inhouseholds precludes a rapid growth in sharedmobility. People are content to continue to travel in
their own vehicles, which are now more comfortable
and can be sent on errands without occupants.
This reinforces the ownership model that is alreadyattractive and leads to high car ownership rates
worldwide by 2050, similar to the BAU. Ride hailingserves a niche activity in cities as it does today.
•• During the transitional (roughly 2025 to 2050)
time frame there remain many legacy vehicles that
require drivers. We estimate that even with a 10- to
15-year transition in sales in the leading countries,
there would remain significant non-AVs on roads in
2040 and still some remaining by 2050. The mixtureof the two types of vehicles will create its own issues
and perhaps hamper some of the efficiency andcongestion reduction benefits of the driverless cars.
We do not attempt a detailed analysis of this issue but
flag it for further study.

18 Three Revolutions in Global Transportation

The 3R Scenario: Adding
Shared Mobility
The third scenario overlays shared mobility and strong
policies for urban planning that favor compact cities,
walking, cycling, and public transport. In the 2R scenario,
solo car travel becomes more convenient and cheaper
per mile. Public transport systems also get cheaper, withdrivers eventually eliminated, but still have a difficult timecompeting with private cars and 2-wheelers. In the 3R
scenario, we assume the opposite is true: sharing rides(in the form of on-demand ride hailing services as well as
public transport) becomes extremely popular. Some of thismay occur just from a cultural change in countries aroundthe world, though as we describe in the policy section
below, we don’t expect this type of scenario to occur
without strong policies to give shared mobility, especiallyhigh occupancy public transport, cycling, and walking, adistinct advantage over private, single-occupant travel.

Assumptions for the 3R Scenario•• By 2020 shared mobility represents a significant
share of urban travel in most major cities of theworld. Thus shared mobility gains substantial traction
before automation even begins. This includes arange of ride-hailing services and vehicle types, withmore right-sized vehicles for different types of trips.
Average load factors (people per trip) rise significantly
in countries (such as the United States) where it iscurrently below two, and stays high in countries (suchas India) where it is already high. In 2015, most of theworld experienced well over two people per ride in
LDVs. This is preserved and even increased in the 3R
scenario.
•• Car sharing also grows, but since automation begins
to increase rapidly after 2020, we assume that by
2030 car sharing is indistinct from ride sharing – inboth cases an empty vehicle is hailed on the street
or summoned from a parking place to provide
mobility services. We also assume that taxis become
indistinct from TNCs – all ride-sharing services use
apps and encourage sharing via pricing systems.
•• One result of this revolution in on-demand mobility
is a steady decline in privately owned vehicles. After2020 sales and use of commercial TNC vehicles
rises rapidly and by 2025 there is a resulting decline
in purchases of household vehicles, although legacy
stocks of household cars remain for over two decades
and could create a glut of unneeded private vehicles.
We assume these cars are driven less and less overtime.

•• Another feature of the 3R scenario is a revolution
in types and roles of public transport, along with asteady increase in its use around the world. After
2020, public transport services become more
tailored to a shared vehicle world. Major travel
corridors continue to be served by efficient bus
systems such as bus rapid transit, and major cities
continue to build rail systems for the busiest travel
routes. Smaller buses, with 8-16 seats, grow innumber, as these are almost capable of providing
point-to-point services and can be summoned – atleast to locations nearby specific residences if not
to the door. Even with a driver, on-demand small bus
and van services provide a very low-cost, convenient
travel option for many types of intermediate trips indense areas. As vehicles become automated, thecost of small-bus travel drops further to become thecheapest per-passenger-kilometer on-demand travel
option in the world.
•• The result of all these changes in 3R is an“ecosystem” of public transport and ride hailingservices that are harmonious and complementary.
Small vehicle ride hailing does not displace trips
from larger public transport services, except where
currently large vehicle public transport is poorly
utilized and inappropriate given corridor demand.
One result of this ecosystem is significantly higher
load factors (average passengers per trip) in all
vehicular modes.
•• This scenario also features a range of policy andplanning initiatives to make cities much morecycling and pedestrian friendly. Sidewalks and bikelanes are added to create continuous networks andensure maximum safety for these travelers. A generaleffort to develop more compact cities with shorter
trip requirements is also assumed, with trip lengths
dropping by 10-15% compared to the BAU scenario,
rather than increasing as they do in the 2R scenario.
These efforts are crucial to preventing new vehiculartravel options from displacing large numbers ofwalking and cycling trips by lower costs and inducing
more dispersed development patterns which increase
travel distances and make cycling and walking
infeasible.
Figure 6 shows the sales of private and shared LDVs inthe United States and India by year for the BAU and 3R
scenarios. It also breaks out these sales bars by ICE, EV,
and automated EV to give a sense of how these three
revolutions interact in the 3R scenario. Compared to the
BAU as well as 2R scenario, the 3R scenario reaches
high levels of light-duty shared mobility vehicle sales by
2030, with India (and all other countries) not far behind
19 Three Revolutions in Global Transportation

U.S. levels (between 40% and 50% sales by 2030). In the United States, most of these are either electric or automatedand electric, whereas in India most are ICE and not automated. However, by 2050, nearly all new TNC vehicles areautomated and electric, and account for over 75% of LDV sales worldwide.

Figure 6a-b. Sales of private and shared vehicles for United States and India by scenario, year and vehicle type

20 Three Revolutions in Global Transportation

True Shared Mobility?
One major question across these types of shared mobility, in particular for ride sharing, is whether many ofthese trips are truly shared – i.e. by separate travelers – making these services really different from classic
taxi services. The extent to which rides are shared in services in different parts of the world is not well
documented. A typical car trip in the United States has fewer than 1.3 people, while in a country like India it
might have well over two.
Many TNCs offer incentives for ride sharing, passing through the savings inherent in adding people to theride. Lyft has reported that up to 50% of their riders have opted for their lower-cost carpool Lyft Line platformin cities where that option is available. If this type of sharing occurred at much higher volumes in the future
and was not the result of fewer people walking, cycling, or taking public transport, the impacts on travel
would be profound. Far fewer vehicles would be needed to move a given number of travelers.
But it is not clear this will occur, at least without policy support to encourage it. For example, one dynamic
between shared mobility and automation is that driverless ride-hailing services may become so inexpensivethat the incentive to share rides to save cost will be substantially reduced. Even if most vehicle trips areshared, if a significant portion of those trips were formerly made by walking, cycling, or public transport, theamount of vehicle travel could increase significantly, increasing congestion and reducing the ability of many
people to access opportunities.
In designing our 3R scenario, we assume that a range of policies are implemented that promote true ridesharing and result in 30-40% more passengers per vehicle in 2050 than in the other scenarios. (In the UnitedStates, for example, this would represent an increase from 1.3 to 1.8 passengers per car trip.)

Results: Passenger Travel Projections Across the Scenarios
By combining all of the assumptions and partial projections shown in the previous section with our scenario tool, we
can create a complete picture of passenger travel by mode worldwide, summing across the eight countries and regionsin this study (Figure 7). These results reflect myriad assumptions and estimates, but also some basic arithmetic: Total
pkm by mode is equal to the number of vehicles by mode, the annual travel of these vehicles, and their average loadfactor.

Figure 7. Total passenger kilometers of travel by mode, scenario, and year worldwide
21 Three Revolutions in Global Transportation

For example, the much higher travel levels of people
in shared AVs in the 3R scenario in 2050 reflects hugeincreases in the numbers of these vehicles, their higher
travel per vehicle than private vehicles, and their relatively
high load factors. These averages mask many regional
differences but are useful to show the stark differences intravel across the scenarios.

comparison to both the 2R and BAU scenarios). This is thenumerical manifestation of the ecosystem of harmonious
and complementary travel modes mentioned above. By
2050 the vast majority of urban LDV travel takes place inon-demand shared mobility services, with private vehicle
travel declining steadily over the two-decade period as
private cars eventually are scrapped and not replaced.

On closer examination, a number of notable results
are shown in Figure 7. In 2030, across all eight regions
studied, there are still relatively few AVs plying the world’s
roads. This reflects the fact that only leading countries
are deeply into selling these vehicles in that year, andthe vehicles still represent a small share of stocks evenin these countries. However, by 2050 in the 2R scenario,
well more than half of private vehicles are driverless. Thishas the effect of increasing driving rates significantly in 2R
compared to both the BAU and 3R scenarios. Assuming
the time cost of travel is halved, along with the possibilityof zero-occupant trips, we assume a 15-20% increase indriving per automated car vs. non-automated car, aroundthe world.

The impacts on vehicle travel, taking into account the
numbers of vehicles and their use to fulfil the projectedpassenger travel by mode, is shown in Figure 8. Herethe differences between 3R and the other scenarios isgreater, since travel in the 3R scenario is supported withfewer vehicles and vehicle kilometers carrying more
people per trip. In fact the growth in vehicle kilometers
between 2015 and 2050 worldwide in the 3R scenario is
only about 30%, even though passenger travel grows by
over 60%. In contrast, vehicle travel grows dramatically in
the BAU scenario and even more so in the 2R scenario,
with nearly a tripling between 2015 and 2050 worldwide.
The 2R scenario also reflects the rapid rise in automatedEV driving after 2030. (Given the already complex natureof the figure, non-automated EVs are not shown).

In the 3R scenario, significant shared travel does occur
by 2030, along with increased travel by minibus andother forms of public transport, cycling and walking (inFigure 8. Vehicle kilometers of travel by motorized mode, scenario, and year worldwide(walking, cycling and e-bikes not shown)

22 Three Revolutions in Global Transportation

Considering Rapid Transitions
The scenarios in this report depict very rapid transitions to a different future. How do such rapid transitions
occur? Clearly the new technologies and travel services must be compelling for consumers. The “tipping
point” concept is important here – trends start slowly, technologies improve, more people learn about
them, and at some point sales expand beyond pioneers and early adopters to mass market. We depict
about a 10-year period when the market share of AVs moves from about 10-90%. This certainly reflects
a rapid increase in mass awareness of and desire to own such vehicles. Is this likely? Perhaps not, but if
these vehicles offer enough advantages at an acceptable price, this scenario seems quite possible.
Another question is whether the suite of electrification and automation technologies can evolve thisquickly and costs can come down by, let’s say 2025, to support this revolution. It also depends on enabling
policies. Finally, it will depend on the ability of vehicle manufacturers to shift plants and equipment, aninvestment challenge on the order of trillions of dollars. The faster this must happen, the more expensive it
could be – from the point of view of both retiring useful equipment and raising capital.

Vehicle Sales and Stocks
As shown for electrification, automation and shared mobility in a previous section, the sales increases and changes
in mode shares assumed in these scenarios are rapid and can have fairly profound effects on vehicle stocks. In fact,
personal and business decisions around vehicle holdings are what determine sales in the first place. Using an annual
stock adjustment model and typical scrappage rates for LDVs, we estimated the impacts on stocks from the LDV sales
trends in our scenarios. In Figure 9 we show the sales and stock effects for the 2R and 3R scenarios in the UnitedStates, as an example.

Figure 9a-d. LDV sales and stock evolution in the 2R and 3R scenarios, U.S. example
23 Three Revolutions in Global Transportation

There are at least two important trends:•• The first is that in the 2R and 3R case, stocks ofconventional vehicles last far beyond their declining
sales shares. This is not surprising. But it does raise
a question: if a conventional vehicle bought in, say,2025 lasts until 2045, how does the world rapidlytransition to electric, automated or shared vehicle
systems? The answer is, it’s difficult, and this legacy
stock of conventional vehicles could hamper sucha transition. We found that in order to assume thesevehicles don’t somehow result in additional travel that
isn’t otherwise needed, many would either have tobe scrapped early or simply not driven, while othervehicles are used instead. A detailed analysis of such
dynamics is beyond the scope of this project but isan interesting area for further study. We do assume
a slow but steady decline in the use of existing
conventional vehicles in the 3R scenario between
2030 and 2050, with about a 25% reduction in
annual per-vehicle travel by 2050 compared to thosevehicles in a BAU scenario.

in TNC applications are used much more intensively,
with higher passenger load factors. There is also
lower overall travel demand and higher mode sharefor non-LDV modes in this scenario (not shown inthese figures). The combined effect is that by 2050,
urban LDV stocks decline by about 70%, from about
2.1 billion in the BAU and 2R scenarios to about 500
million in the 3R scenario. This takes us back to thesales figures, which also decline substantially (byabout 40% in the U.S. example) in the 3R scenario,
since far fewer LDVs are needed to meet the travel
demand. The reduction in sales is not as significant as
the drop in stocks because each vehicle drives about
five times more per year than a private vehicle andis turned over in four to six years instead of 20 to 25
years. Sales are adjusted to meet this higher turnoverrate, but the net effect is lower global sales of cars
– on a worldwide basis, 2050 urban sales in the 3R
scenario are around 60 million – less than half of the
135 million reached in the BAU and 2R scenarios andeffectively a return to current levels (which are about
two-thirds of the 90 million, urban + non-urban, soldworldwide).

•• A second is that in the 3R scenario, total stocks ofvehicles decline dramatically since shared vehicles

Figure 10a-d. Evolution of vehicle and pkm of travel, U.S. example

24 Three Revolutions in Global Transportation

Light-duty Vehicle and Passenger Travel
The changes in LDV sales and stocks are aligned withthe passenger demand for LDV travel in each region andscenario. Looking at vehicle kilometers of travel and pkm
in those vehicles reveals a few more dynamics.
Results in terms of vehicle and passenger travel over time
are shown in Figure 10 for the 2R and 3R scenarios in
the U.S. case. Notable is the much higher vehicle travel
level in the 2R scenario, especially after 2030. This is also
higher than in the BAU case, about 15% higher by 2050.
This reflects our assumption of the travel rebound effectdue to lower out-of-pocket costs as well as much lower
time costs of travel in AVs. As expected, vehicle travel byshared vehicles in the 3R scenario is far higher than theirstock share, given the intensity of use of these vehicles.
These supply a high share of overall LDV travel in the 3R
scenario by 2050. By that year, total LDV travel in the 3R
scenario is about half that of the 2R scenario.
Finally, passenger travel is simply a reflection of vehicle
travel, with passengers per vehicle factored in. We do notassume any significant difference in passenger loadings
between the BAU and 2R scenarios. However, in the 3R
scenario shared mobility results in significant increases
in passengers per vehicle, rising over time, as reflectedin the U.S. and India examples shown in Figure 11. This
comparison reveals two very different situations. In the

United States, ride sharing is starting from relatively low
averages and will need to rise in the 3R scenario, whereas
in India it is already high and the trick will be to preserve
this high level of sharing.
In the 3R scenario many more shared trips may be takenwith two, three, or more passengers. Some such trips
occur in the other scenarios as well, naturally. But a netincrease of close to 0.5 passengers per vehicle in the
United States would be fairly dramatic. For example, itwould occur when shifting from 80% single-, 10% double-,and 10% triple-occupant vehicle shares to a 50% single-,
20% double-, and 30% triple-occupant share of these loadfactors. Such shifts could be driven by strong marketingand pricing strategies on the part of TNCs to encourage
shared trips, as well as by policies to promote this sharing.
See further discussion in Section 5, Policy Narratives.
Average load factors also rise in the 3R scenario for public
transport services, since they are better coordinated withride sharing services, and we assume people in denser
cities are more amenable to taking transit. In addition,with widespread TNC light-duty vehicle services, some of
the lowest productivity public transport routes could be
eliminated, particularly in countries like the United States
where average bus load factors are among the lowest in
the world.

Figure 11. Average passengers per LDV, U.S. and India examples

Scenario Impacts on Energy and CO2 Emissions
Having established the passenger and vehicle travel
shares by region in the various scenarios, it is not difficult
to estimate the types and amounts of energy used, andCO2 emitted, in relation. In addition to the assumptions
described above for each scenario, there are a numberof key features of these scenarios that impact energy use
and CO2:•• Vehicles of all types may become more efficient
over time as technologies improve. We follow theIEA BAU projections of fuel economy improvement
given current and expected efficiency improvement
programs and regulations around the world. By 2030
conventional vehicles become roughly 30% more
efficient than in 2015, as a stock on-road average.

25 Three Revolutions in Global Transportation•• We assume significant changes in the mix ofvehicles in each scenario that affect efficiency.
For example, we assume that automation and theresulting interest in spending more time in vehicles
leads to larger vehicle sizes. Vehicles may beredesigned, for example, to make it possible for four
people to sit comfortably facing each other, or toobserve entertainment systems, all of which could
require larger vehicles. We assume that in the 2R
scenario, larger vehicles offset some of the benefitof electrification and automation. In the 3R scenario
we assume far greater right-sizing of vehicles, with a
distribution from 2-seaters to minibuses. Light-duty
vehicles are smaller on average in 3R than in the BAU
or 2R scenarios•• EVs also have a singular impact on the use of gasoline
and diesel, and on other liquid fuels such as biofuels.
The rising use of EVs in the 2R and 3R scenarios,
across all types of vehicles, drives oil use to very
low levels by 2050. In fact, from an energy pointof view, the main advantage of 3R over 2R is thatthe electricity demand in 2050 – the electric power
needed to support transportation – is far lower.
•• Finally, the shift to electric power can help strongly
decarbonize passenger vehicle travel, but only ifelectricity itself is decarbonized. Countries such as
India currently do not have a power grid mix thatwould offer much, if any, CO2 reduction from a 2R
scenario over a BAU scenario. Of course, electric
grid mixes will evolve, and given Paris Agreement
commitments, there is a reasonable chance that thesewill be strongly decarbonized over the next 35 years.
The IEA projects in its 4˚C scenario (roughly a baselineor BAU type of scenario) that the average carbon
intensity of electricity worldwide will decrease by
about 50% in 2050 relative to 2015. In a 2˚C scenario,
the average carbon intensity of electricity worldwide
will decrease by more than 95% in 2050; in a 2˚Cworld, nearly all electricity worldwide is generated
from zero-carbon sources.
The results in terms of energy use are shown in Figures
12 and 13. In Figure 12, LDVs dominate energy use in all
scenarios, although energy use declines significantly fromthe BAU to 2R to 3R cases, particularly in 2050. In the 2R
scenario, the strong uptake of EVs cuts energy use by
about 40% compared to the BAU scenario in 2050, withsome reductions already by 2030, thanks to the rapidly
rising use of EVs at that point. The 3R scenario delivers
an additional significant reduction in energy use by 2030,
and a very large reduction even compared to the 2R
scenario in 2050 (more than 50% lower), with energy
use less than one-third of the BAU case. This reflects the
combination of electrification and a strong shift towardshared mobility, higher load factors, and much lower
energy use per pkm of travel service provided around theworld.
These differences are also reflected in Figure 13, breaking
energy use into ICE vs. EV consumption rather than bymode. The dominance of electricity in the 2R and 3R
scenarios by 2050 is evident, and since EVs are much
more efficient than ICE vehicles, the overall energy use is
far lower than in the BAU scenario.

Figure 12. World energy use across all modes, by scenario and year

26 Three Revolutions in Global Transportation

Figure 13. World energy use, ICE vs. EV, by scenario and year

Breaking Down the 2R and 3R Scenarios’ Net Impacts on Energy Use
Many factors in our model affect energy use, either by affecting vehicle travel (and the share of travel bydifferent vehicle types), or the energy efficiency of that travel. Table 3 provides some key indicators, each ofwhich is affected by different factors.
Table 3. Summary of key travel and efficiency assumptions across scenarios
Factor2R Assumption

3R Assumption

Notes

Private AV travel per
vehicle15-20% higher per vehicle
than BAU in all years

Same private AV increase
as 2R (but with far lower
private AV travel share)

This includes empty
running of vehicles

Public (shared) AV travel
per vehicle

Similar to nonautonomous sharedvehicles (intensive travel
given service provision)

Same as 2R

No "induced travel" effectsince travelers pay at themargin for each trip

Non-autonomous EV
efficiency

Roughly 50% better thansimilar ICE vehicles

Same as 2R

ICE vehicles improveover time, with increasing
shares of hybridized andlighter vehicles

AV/EV efficiency

60% better (lower energy/
kilometer) than similar
conventional vehicle

Same as 2R

Autonomy provides
additional efficiency
benefits, mostly in-use,
despite possibly higher
traffic levels

LDV shared mobility

Little effect in this
scenario

Up to 33% lower LDV
energy per pkm fromsharing

Given up to 50% higher
load factors

Public transport

Major improvementsfrom electrification andautomation thoughsmaller decrease inenergy per pkm due todeclining load factors

Same as 2R except
steady or increasing loadfactors result in moreenergy production perpkm

A high share of public
transport vehicles areautomated in mostregions by 2050, similar
to LDVs

27 Three Revolutions in Global Transportation

The CO2 emissions associated with these scenarios are shown in Figure 14. Generally speaking, the ICE vehicles use
primarily gasoline or diesel fuel and continue to do so into the future; these fuels remain carbon intensive since nomajor increase in biofuels is assumed in these scenarios. Thus, CO2 emissions from ICE vehicles closely track theirenergy use. On the other hand, EVs benefit from steady, ongoing reductions in the CO2 of electricity generation. Ourbase electricity scenario (shown here) uses the IEA 4 ˚C scenario projection of electricity carbon intensity, with anaverage of 40% reduction between 2015 and 2050 worldwide. However, for a 2˚C climate target to be achieved, as
projected by IEA, electricity-related CO2 emissions must be nearly completely eliminated by 2050. Achieving that would
be reflected in this figure as removing the electricity CO2 (yellow bars) from the chart, leaving only the blue bars. In sucha case the 3R scenario nearly achieves a zero carbon urban transportation energy system in 2050. Either way, the 3R
scenario cuts CO2 more than the 2R scenario, even by 2030 and onward through 2050, due to the greater reduction inpetroleum energy use it provides via shared vehicle trips and greater public transport and non-motorized travel.

Figure 14. World CO2 emissions by scenario and year, BAU EV decarbonization cases

The Paris Agreement and Very Low Carbon Scenarios
The climate targets the world adopted in Paris in 2015 (the Paris Agreement), include well-below 2˚C or 1.5˚Climits to global temperature changes. To achieve 2˚C, a 50% reduction in CO2 by 2050 relative to 1990 levels
as an average across all energy sectors is often cited as an appropriate global target (IEA, 2016). There
are few published analyses on achieving a 1.5˚C target but it is not unreasonable to infer that emissions inall sectors must be very low by 2050, headed toward zero not long after. The exact role for transportation(and in this case, urban transportation) is uncertain, but clearly it must achieve very low CO2 emissions by
2050 to do its part to achieve the broader targets. A reasonable approximation would be more than a 50%
reduction in CO2 emissions in 2050 compared to 2015 for a 2˚C scenario, and something closer to zero CO2emissions for a 1.5˚C scenario. In the scenarios shown in Figure 14, the 2R case appears on track to achievea 2˚C target, whereas the 3R case is more consistent with a 1.5˚C target. If electricity generation CO2 drops
to zero worldwide, both scenarios may be 1.5˚C scenarios.

28 Three Revolutions in Global Transportation

The Costs of 3 Revolutions
Transportation is expensive. There are costs associatedwith constructing and operating vehicles; extracting andconverting energy to its final form as fuel; building andmaintaining roadways and vehicle fueling infrastructure;operating public transport and other systems; and paid
drivers.
In our previous High Shift studies (and in IEA’s Energy
Technology Perspectives series), most of these costs
were estimated and projected out to 2050 for countries
around the world. Here we reorganize this cost analysis
around our 2R and 3R scenarios, and add some importantelements. We continue to use average costs (as final
prices) for all cost components within each of our regional
breakouts, and adjust prices into the future.
Beyond the cost of purchasing and fueling vehicles,
one of the most important costs, it turns out, is the laborassociated with paid drivers. Nearly as important, at leastin the 3R scenario, is the cost of running companies thatmanage shared mobility networks (the TNCs of the world).
Finally, the additional cost of EVs and automated EVs overconventional ICE vehicles is not minor, though this is offset
over time by fuel cost savings.
Specifically, the costs in our estimates include:•• Purchase cost, maintenance cost, energy cost of
private vehicles (everything from bicycles to large
SUVs)•• All costs associated with operating TNC services•• All costs associated with operating public transport
services•• Costs of constructing and maintaining roads,
sidewalks, bike paths, and parking facilities
We have not attempted to quantify the costs of:•• Traffic congestion and the associated time delays•• Traffic safety, in the form of crashes, injuries, anddeaths•• Air pollution and its impacts on health•• Noise and its impacts on health•• CO2 and its impacts on the climate – though we
do make some cost estimates associated with thispollutant, in the form of cost-per-ton of CO2 reduced.

one kilometer of highway, one square kilometer ofparking, or one bus, or running a bus system per pkm ofservice) are retained from our previous High Shift studies.
Three new estimates are included here:•• The cost of EVs and AVs, as noted in the assumptions
for the 2R scenario. These costs decline over time but
remain fairly expensive through 2030.
•• The cost of paid drivers. Vehicle drivers, including fortaxis, TNC vehicles, buses, and trains, can account for
up to 50% of the cost of operating these enterprises.
We did include this cost in previous studies for public
transport systems, but taxi and TNC services were
neglected. We have estimated the average cost
per driver plus overhead for OECD and non-OECD
countries taking into account typical wage rates. Thisis naturally an important element in lowering the costof operating AVs.
•• The cost of operating and managing TNC systems.
This is difficult to estimate, given the fairly early stageof operating such systems, but it appears potentiallyvery important in a world with high levels of use ofsuch vehicles. The current 20-25% overhead rate
(fee charged per unit revenue charged by drivers)
that is typical of systems today is assumed to decline
somewhat as these systems expand, but is kept close
to 20% from 2020 to 2050.
Finally, the method for estimating and tracking costs
here is based on societal costs. We look at the fuel use
over the entire 20-year operating span of a vehicle,
and allocate its capital cost over that same span. We
do not discount costs into the future, though we use
real rather than nominal cost estimates, accounting for
inflation. Of course, private actors make decisions very
differently, using short-term considerations, rapid payback
requirements and such. Our cost comparisons do notprovide such context, and thus costs that may be lower
from a long-term, societal point of view, may not appear
lower to these agents. Thus policies may (among otherthings) need to help better align private costs with societal
costs in order to achieve the scenarios we present here.

However, these are all important forms of societal costthat are affected by the different scenarios, and we
consider some of the possible impacts below.

A full set of costs across cost categories and comparing
across modes, for two example countries (the UnitedStates and India) in 2030 is shown in Figure 15. Theseare estimates for BAU scenarios, and the per unit costs
per pkm do change somewhat for other scenarios, but
generally not by a lot. These figures show some of themain differences across modes and situations worthy ofnote.

Most of the cost assumptions per unit (such as building

Modes in OECD countries with paid drivers are nearly
29 Three Revolutions in Global Transportation

double the cost per pkm of modes without drivers. Clearly,
removing the driver can increase the competitivenessof any commercial mode dramatically. This is far more
important than the increased cost of purchasing AVs, forexample. In poorer countries with lower wages, driver cost
is not as big a part of overall costs.
Public transport systems in this figure are shown withdrivers, though one can easily imagine the savings ofremoving the driver. Even with drivers, public transportsystems can provide the least expensive travel service ofany mode, though this is heavily dependent on averageload factors. In the United States, with relatively low
average bus ridership, cost per pkm is higher than rail,
while in India, with low driver wages and much higher
ridership levels, large buses clearly provide the lowestcost travel.
•• The 20% overhead cost of operating and managing
TNCs amounts to a significant expense; far higher
than the cost per pkm of operating public transportcompanies. This is not surprising since this is a very
personalized service, with higher costs all aroundgiven the small number of people included in eachtrip.
•• The cost of infrastructure construction, whenamortized across all vehicles over time, is not a major
expense; certainly not in comparison with the cost ofdrivers and, in the case of TNCs, the cost of operating
and managing the enterprise.
•• The cost per pkm for 2-wheel vehicles may appear
high – especially for bicycles – but this reflects therelatively low average use of these vehicles in a given
year. A bike ridden 3 kilometers per day accounts
for about 1,000 pkm per year, which results in higher
overall costs per pkm than public transport modes.
•• Costs change somewhat out to 2050, but the biggest
impact on overall costs is how much these different
modes are used.

Figure 15. Cost per passenger kilometer by mode, 2030, U.S. and India examples(Note: y axes are not aligned; Indian costs are far lower than U.S. costs)

30 Three Revolutions in Global Transportation

Multiplying the per-pkm costs by total pkm in each scenario, the total costs of the scenario in a given year can beobtained. These totals are shown in Figure 16, by scenario, worldwide to 2050. In 2030 the scenarios are remarkably
close together, reflecting in part that the revolutions are not that far along. It also reflects the fact that the significant
increase in shared trips in the 3R scenario occurs mainly with paid drivers, and does not provide a significant costadvantage compared to private vehicle driving. However, by 2050, with widespread, lower cost automation and EVs,
and an ongoing increase in load factors across modes, the 3R scenario is far cheaper than the 2R scenario.

Figure 16. Total global cost of scenarios by year and mode
Again, these cost results do not include things like air pollution or traffic congestion impacts. They also ignore the valueof time, and the disutility of travel that may change considerably due to AVs or, for that matter, higher quality public
transport systems. But they do give a rough idea of the out-of-pocket costs to travelers and taxpayers associated withbuilding and operating, and using urban transportation systems. This also raises an issue that is beyond the scopeof this study: who pays? Some costs, such as the purchase and use of private vehicles or the fares paid for public
transport and on-demand mobility services, are borne by travelers. But other costs are paid by governments (and thus
taxpayers). These costs include highway infrastructure and often public transportation infrastructure. Thus savings ineach of these areas accrues to different types of agents; there would likely be winners and losers. All of these details
are beyond the scope of this analysis but would be areas for additional research.

31 Three Revolutions in Global Transportation

5. Policy Narratives
The three scenarios we describe represent very different
futures. The course of events that cities and urban
transportation take will be highly dependent on thepolicies that city and national governments implement.

losses. In many non-OECD countries, low labor costs
dampen demand for vehicle automation for many years.
When demand increases, restrictions are enacted once
AVs challenge labor markets.

Here we describe a set of policies that seems likely tobe needed in order to realize each scenario, and present
the scenarios roughly in order of the level of policy
intervention required. The BAU scenario, while perhaps
increasingly unlikely due to its increases in travel, energy
use and CO2 emissions, represents no significant change
in policy trajectory (and relatively limited success of the
new technologies in question); the 2R scenario would
require important policies to enable automation andencourage electrification; and the 3R scenario would likely
require the broadest array of policy interventions andchange as it affects fundamental mobility systems andpatterns around the world.

BAU: ElectrificationBAU Scenario Policy Narrative
The BAU scenario includes no major changes to thecourse of current policies affecting urban transportor land use. In the more advanced countries (UnitedStates, Europe, China, Japan), this typically includes
continuing existing policies to support new technologies(electrification, sharing, and automation), urban
development and transportation planning. In mostdeveloping countries, the BAU scenario includes almost
no support for new technologies, limited capacity for
urban policy implementation, and generally continued
policy support of (or failure to prevent) strong increases
in private motorized travel, with transport infrastructure
provision continuing to lag far behind urban growth.
We describe the policy assumptions for BAU in somedetail here as these are likely to be just as important indetermining future directions as the new policy initiatives
we discuss for the other scenarios.

BAU: Automation
In OECD countries, most existing regulatory obstacles
to automation remain in place for many years. Even if
automation technologies improve and mature, legal
liability, privacy, and ethics concerns are resolved only
slowly over the course of many years, and existing
restrictions on AVs are only removed after these issues
are resolved. In some places, new restrictions on AVs
are enacted in response to growing concerns about job

In the BAU scenario, electrification does not receive
additional government support to encourage widespread
adoption, beyond what is already in place and planned. In
most OECD countries, this includes some modest support
for EVs in the form of purchase incentives, new charging
infrastructure, and some movement toward decarbonizing
electricity generation. These policies, however, have only
a limited impact and are not enough to help EVs break outof the 1-2% sales share position they are currently in. In
most non-OECD countries little or no support is given toeither the electrification of vehicles or the decarbonizationof electricity production.

BAU: Sharing
New technologies permitting the sharing of trips continue
to receive some ongoing support in OECD countries.
A few cities and countries continue to experiment withmobility-as-a-service platforms that better link public,
private, and shared mobility services into a cohesive
network. However, these efforts are limited and do notreach a large scale. Few governments actively encourage
the sharing of trips in any meaningful way beyond current
policies supporting carpooling and public transport. In
non-OECD countries, even less is done, and some policies
to actively restrict the sharing of trips via new technology
continue.

BAU: Urban Planning
Around the world, existing urban planning practices
continue into the future. In OECD countries in western
Europe, and east Asia, existing policies that support
dense urban settlement tied to a strong network of
public transport, cycling, and walking continue. In more
car-centric OECD countries, such as those in North
America and Australia, policies supporting low-density
urban development tied primarily to automobile travel
will continue, maintaining high mode shares of singleoccupant car travel over increasingly long distances.
In non-OECD countries, current urban planning trends
such as spending that is primarily focused on new roads,
32 Three Revolutions in Global Transportation

flyovers, and limited access highways continues. Auto-centric and poorly-coordinated land use planning also continues
in many areas, leading to more low-density, vehicle-centric settlement patterns that follow large arterials as they
radiate out of cities. The speed of urban growth in non-OECD countries will continue to surpass transport infrastructure
provision. This, coupled with growing wealth and ability to purchase private vehicles, results in a large increase incongestion and decrease in access. While a few countries will continue to invest in public transport and maintaincompact cities, most will not.

BAU Scenario: Assumed Policy Summary•• Vehicle automation* Maintain or slowly relax existing barriers to automation* New restrictions added in some places to protect jobs•• Vehicle electrification and decarbonization of electricity production* Limited support for electrification and decarbonization in OECD countries* Very low support for electrification and decarbonization in non-OECD countries•• Shared mobility services* Limited ongoing support for shared mobility, in select cities in OECD countries* Low support for new vehicle sharing in non-OECD countries* Continued support for compact cities, public transport, cycling, and walking in OECD countries where
this is already a focus* Continued low-density car-centric development in the rest of the world

2R Scenario Policy Narrative
The 2R scenario is focused on achieving rapid global
adoption of EVs from 2020 onward, and a breakout andrapid growth trajectory for AVs beginning around 2025.
This scenario is otherwise similar to the BAU scenario in
the continuation of existing trends for vehicle sharing,
public transport use, and urban planning. Many of themeasures described here would logically be undertaken
at a national level; however some (such as restrictions on
ICE vehicles, coordination of EV charging infrastructure,etc.) would more logically be implemented by cities
themselves.

Electrification
In both OECD and non-OECD countries, the short term
push for electrification takes the form of policies that
reduce barriers to electrification and actively, often
directly, support the decarbonization of electricity
production. This includes government support and direct
provision of EV and clean power infrastructure. It also
includes taxes and other market-driven schemes that
restrict more polluting vehicles and power sources.

Countries like Norway, with very high incentives topurchase EVs, have shown that it is possible to achieve
high market shares (EV sales shares there reached 30%
in 2016 (Lutsey, 2017). In major markets, ongoing strong
purchase incentives must be accompanied by significant
use advantages (such as preferential parking) and an
ongoing increase in public and workplace charging.
These types of incentives must continue until EVs becomemarket dominant and the costs of the incentive systems
become unworkably high. Hopefully by that point thetipping point toward a rapid increase in sales will bereached. Much of this cost of maintaining incentive
systems can be funded through differential taxation
systems, for example, feebates or French style bonusmalus policies that tax the highest-emitting vehicles andsubsidize the lowest-emitting vehicles, which would
include EVs (at least in countries with clean electricity).
In the longer term, completely decarbonizing electricity
generation may require active closure of all carbonemitting power plants and their replacement withrenewable power sources. Transitioning to a 100% EV
fleet may require banning ICEs and government buyouts
of existing vehicle fleets.
33 Three Revolutions in Global Transportation

Automation
As mentioned earlier, the potential cost savings fromvehicle automation makes it an attractive technology forbusinesses operating transportation services, especiallyin high wage environments, such as in OECD countries.
Thus the most crucial policies for automation to become
widespread are those that remove restrictions to vehicle
automation.
The scenario envisions OECD countries leading thecreation of a policy environment that supports automation.
In addition to removing barriers to automation,
governments might also support research and testingof automation technologies and set legal and safety
frameworks to allay public fears about personal data
privacy and liability in the event of crashes. As labor prices
rise and technology prices fall in non-OECD countries,

AVs become more cost competitive. When this happens,these countries follow the lead of OECD countries in thedevelopment of policies that enable and support vehicle
automation.
An important question is whether driverless cars are firstand most significantly deployed in commercial fleets, taxi
companies or TNCs, or in households. In this scenario,
we assume that restrictions are removed for all potential
purchasers in an even manner and that, while there
may be some early uptake commercially (given the highcommercial advantages of driverless cars), households
are also quick to begin purchasing such vehicles. Thusby 2030 there is not a major difference in use patterns,
except that intensively used commercial vehicles areturned over much sooner, giving both more purchase
opportunities to shift to AVs, and a faster penetration
through the stock.

2R Scenario: Assumed Policy Summary•• Continuation of existing policies on sharing, public transport, urban planning (see BAU scenario)•• No particular policy preference to increase EV or AV uptake by commercial operations over households•• New policies supporting vehicle electrification and decarbonization of electricity production* Carbon tax and heavy investment in very low carbon (e.g. renewable) electricity generation* Subsidies to offset EV purchase costs; could be generated via cap-and-trade, feebates or othermarket-driven schemes* Elimination of subsidies for fossil fuels, making EVs more cost-competitive* Require automated vehicles to be electric drive* Support for public EV charging infrastructure* Policies dedicating space for EV charging in cities* Encourage “smart charging” of private and commercial EVs, at off peak times or otherwise in better
concert with electricity grid management systems* Low emission zones and other policies to encourage EV use and/or restrict operation of ICE vehicles
in cities or their central business districts, or even broader bans on ICE vehicles* Government research support for battery development and other new technologies such as 		
contactless charging to reduce charge time and increase driving range* Government buyouts of polluting vehicles and subsidies for vehicle replacement•• New policies supporting vehicle automation* Remove major legal restrictions to AV use* Develop comprehensive safety and liability regulations as consistently as possible acrossjurisdictions* Develop data policies to protect private travel data
* Support research into automation technology* Set framework for AV testing and type approval

3R Scenario Policy Narrative
The 3R scenario builds on the 2R scenario, with policy support for both electrification and automation, but also
substantial policy support for shared-use mobility and urban planning that supports shorter trip lengths and high levels
34 Three Revolutions in Global Transportation

of walking, cycling, and public transport use, even in a future where vehicular travel is significantly less expensive.
Without strong policy support for compact cities, even a scenario with fairly high levels of vehicle sharing in smaller
vehicles could result in significantly higher vehicle kilometers and lower levels of access.
TNCs must be encouraged to prioritize ride sharing over single-occupant rides, and to promote sharing as a preferred
option. Much of the 3R scenario outcomes, particularly in car-dominant countries, depend on the higher occupancy
rates of these cars. As cars become automated, and the per-trip costs drop, pricing incentives will likely become evenmore important to encourage people to share trips. Zero-occupant trips by AVs (TNCs or private) must also be heavily
discouraged, probably through pricing, though the mechanisms for achieving this are not fully clear at this point.
Preference for AVs in public (shared mobility) rather than private hands is also likely to be important in this regard, toavoid the higher driving levels of the 2R scenario.
Finally, policies will be needed to ensure that TNC vehicles work in concert with public transport and other highly
efficient modes, rather than compete with them. Policy options could involve possible restrictions on operations withincertain corridors, along with incentives to serve stations. Support for small bus and van programs that can provide ondemand and at least near door-to-door service may also hold major potential for improving system efficiency.

3R Scenario: Assumed Policy Summary•• Continuation of policies that support vehicle electrification and automation, as in the 2R scenario•• New policies on EVs and AVs* Discourage or restrict the operation of zero-occupant vehicles* Discourage or restrict private ownership of AVs•• Strong support for trip sharing, public and active transport* Fees added for vehicular travel, or vehicle kilometers traveled, potentially variable to achieve thedesired level of movement, and with higher fees charged for vehicles with lower occupancies andhigher negative environmental and traffic impacts* Conversely, vehicle kilometer subsidies could be applied to very high-occupancy vehicles (buses
and trains), particularly during high-congestion times on more congested routes* Support and incentives for public transport operators to better match passenger demand withvehicle size, through smaller automated electric vans and shared taxi-like vehicles* Government support for driverless buses and rail, dramatically reducing the operating costs andfares, while improving frequency and reliability for these shared modes* As the nature of transit services changes, ensure mobility opportunities remain available andaffordable for traditional transit customers and for those with disabilities, older adults, and lowincome passengers* Close attention to the labor and equity impacts of automation and shifts to shared mobility; ongoingtracking and research into minimizing negative societal impacts of these revolutions•• Policies on urban planning* Mixed use, transit-oriented planning to encourage shorter, less car-dependent trips* Better metropolitan area coordination of regional land-use and transportation decisions* Increased, ongoing investments in walking, cycling, and public transport infrastructure and systems* Improved safety as well as legal protection for walking, cycling, and public transit users* Implementation of bike and e-bike sharing programs in urban areas with sufficient density* Elimination of policies that increase motor vehicle use, such as minimum parking requirements, freeparking on public streets, and fuel subsidies* Government coordination of mobility-as-a-service, linking many transportation options into aseamless network of trip planning and payment via a single interface* Increased use of local development impact fees; e.g. charges that account for car dependence andother negative externalities, and these fees fund investment in sustainable transport* Global institutions, such as development banks, change lending practices to shift investment fromurban roads toward public transport, walking, cycling, and other more sustainable modes

35 Three Revolutions in Global Transportation

6. Conclusions, Uncertainties, Next Steps
The scenarios presented in this report are one way toconstruct alternative possible futures for urban passenger
transport. There are many other possible approaches.
This study attempts to identify how we can envision thethree revolutions in order to:• Understand whether combining electrification with
automation can deliver a low CO2 and otherwise
sustainable world cost-effectively• Understand whether further revolutionizing the mannerin which we travel, rather than just the technologies
on vehicles, can contribute significantly to achieving
our environmental goals.
Ultimately the findings shown here are not that surprising.
While automation may produce some efficiencies androad congestion benefits, it may also trigger increases
in travel, and by itself does not seem likely to result in alow-energy future. But when paired with electrification,
the combination does seem capable of providing deepenergy and CO2 cuts (and thus nearly all of these cuts, onbalance, can be attributed to electrification). Even then,
the net effects of electrification depend on decarbonizing
electric power.

would have large safety benefits, though these have notbeen quantified in this study.
To achieve the 3R scenario, pricing policies will likely
need to play an important role. Such policies can help
avoid widespread use of zero-occupant vehicles andeven single-occupant vehicles, and encourage enoughon-demand mobility to get people around the world toshift away from private car ownership. The cost savings
associated with the 3R scenario are due mostly tofactors that are not apparent and don’t feature in muchdecision-making today. For example, lower car ownershipmeans people save enormous amounts on buying andmaintaining vehicles that are unused at least 90% of thetime.
Similarly, savings from fewer roads and parking lots may
only become apparent as savings opportunities once theworld is strongly on a 3R path.
Overall, while our pathway to a revolutionary futureappears to have low direct costs and likely high societal
benefits, it may require aggressive, visionary policymaking
to achieve policymaking to achieve.

We also find that there are fairly enormous efficiencies
associated with shared mobility, including ride sharing
in LDVs along with the use of small and large buses andurban rail systems. Shared mobility in ride-hailing vehicles
must achieve relatively high average occupancy (loadfactor) levels in order to really contribute to reductions
in traffic and energy use; for their part, public transportmodes must provide efficient, comfortable service forthis 3R scenario to succeed. There is evidence that suchhigh quality public transport is achievable, but there areno guarantees and considerable policy pressure andinvestments will be needed.
If successfully achieved, a 3R scenario with its ecosystemof shared vehicle trips, public transport, and active travel
use can provide high quality and sufficient urban mobility(indicated in this study as pkm) with far fewer vehicles on
the road, and even fewer vehicles parked, compared to
our BAU or 2R scenarios. Energy and CO2 emissions in
the 3R scenario are about half those of the 2R scenario,
and costs – perhaps surprisingly – are far lower as well.
The 3R scenario also seems very likely to provide thebiggest benefits in cutting traffic congestion and likely
36 Three Revolutions in Global Transportation

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