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Melissa Ruhl
May 2015
San Jose State University
Master’s Planning Report
Travel behavior changes with
self-driving cars
Driving
Future
into the
DRIVING INTO THE FUTURE:
TRAVEL BEHAVIOR CHANGES WITH SELF-DRIVING CARS
A Planning Report
Presented to
The Faculty of the Department of
Urban and Regional Planning
San José State University
In Partial Fulfillment
Of the Requirements for the Degree
Master of Urban Planning
By
Melissa Lentini Ruhl
May 2015
ii
Driving into the Future
Acknowledgments
As a young adult who, like so many of my peers, struggled to discover a career in the depth of the
Great Recession, I am both humbled and grateful to have received such dedicated support from the
faculty and students of this program. I could not have hoped for a better advisor in Professor Hilary
Nixon. She was open, eager, strategically critical, and responsive, even meeting with me over Spring
Vacation to explore project ideas with me. She is a career model for me as someone who dives in to
learn, grow, and lead.
Like Hilary, the other professors in this program always went the extra mile to encourage excellence in
their students. I am grateful to Professor Shishir Mathur for meeting with me numerous times to guide me
in statistical analysis and for teaching me to think openly and critically about the growth and devel-
opment of urban landscapes. Professors Rick Kos and Asha Agrawal expected rigor and excellence,
motivating me to always aim higher. The affiliated faculty at SJSU represent the highest caliber of pro-
fessional achievement, all with many years of theory and practice to pass on. I am especially grateful
to Laurel Prevetti, Richard Lee, Joseph Kott, Hing Wong, Charles Rivasplata, and Eduardo Serafin for
teaching both cutting edge and deeply tested best practices.
For guidance on my specific topic, I am thankful to the Mineta Transportation Institute for sponsoring
me to attend the TransOvation Conference and Workshop on autonomous vehicles and to the nu-
merous organizations across the Bay Area who offered student rates at luncheons, workshops, and
conferences.
Thank you to my peers with whom I hope to be colleagues and friends for years to come. I am grateful
especially to Adam Paranial, Audrey Shiramizu, Beth Martin, Ceci Lavelle Conley, and Mark Young for
talking with me about the future of transportation and the development of urban technology, always
keeping sustainability and progress at the forefront of conversations.
Most of all, I am grateful to my husband, Chris Lentini, for supporting me, laughing with me, digging
deep into conversations with me, relaxing with me, working with me, and believing in me. I could not
have dreamed of a more perfect partner. Cheers to yet another level up on our long journey together.
iii
Driving into the Future
Contents
List of Figures and Tables
Abstract
Introduction
The Open Road Ahead
Cooperation and Control: Defining Automation
Increased Sprawl or Shared Mobility: A Range of Possibilities
Finding Precedents for Self-Driving Cars
A New Way of Driving: Carsharing
A New Type of Vehicle: Electric Vehicles
Do People Want Automation?: Vehicle Automation Conditions Tests
Self-Driving Cars Market Adoption Survey
Distribution Methodology
Results
Analysis
Discussion
Survey Limitations
Conclusion and Recommendations
Summary
Recommendations
Appendices
A: Survey Questionnaire and Results
B: Open-ended Questions Responses
C: Coding Index for Statistical Analysis
D: Statistical Results Tables
E: Survey Distribution Avenues
Reference List
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Driving into the Future
List of Figures and Tables
Figure 1: Age
Figure 2: Ethnicity
Figure 3: Race
Figure 4: Gender
Figure 5: Employment
Figure 6: Income
Figure 7: Education
Figure 8: Political Affiliation
Figure 9: Community Type
Figure 10: Housing Status
Figure 11: Car Ownership
Figure 12: Commute Mode
Figure 13: Commute Time
Figure 14: Electric Vehicles
Figure 15: Shared Mobility
Figure 16: Car Ownership Importance
Figure 17: Advantages of Driving
Figure 18: Disadvantages of Driving
Figure 19: Familiarity
Figure 20: Willingness to Ride
Figure 21: Self-Driving Rideshare: Use Frequncy
Figure 22: Self-Driving Rideshare: Shared Rides
Figure 23: Self-Driving Rideshare: Car Onwership
Figure 24: Self-Driving Commute
Figure 25: Extending Commute Distance with Self-Driving Cars
Figure 26: Vehicle Occupants in Self-Driving Cars
Figure 27: Concerns with Self-Driving Cars
Figure 28: Anticipated Features of Self-Driving Cars
Figure 29: Familiarity and Willingness
Figure 30: What factors influence a participant’s interest in using self-driving cars to commute?
Figure 31: What factors influence a participant’s interest in living farther away from work with
self-driving cars?
Figure 32: What factors influence a participant’s interest in using self-driving rideshare services
frequently?
Figure 33: What factors influence a participant’s interest in still owning a car with
self-driving rideshare services available?
Figure 34: What factors influence a participant’s willingness to share a self-driving ride-
share ride with others for a reduced fare?
Table A: Commuting and Self-Driving Cars
Table B: Living Farther Away from Work with Self-Driving Cars
Table C: Self-Driving Rideshare Use Frequency
Table D: Self-Driving Rideshare and Vehicle Ownership
Table E: Self-Driving Rideshare and Shared Rides
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v
Driving into the Future
Abstract
Self-driving cars could completely transform travel behaviors in the United States. Though we are finally
starting to see a reduction in vehicle miles traveled and a growing popularity of multimodal transpor-
tation, self-driving cars could reenergize interest in driving. They also have the potential, however, to
make car ownership truly unnecessary. In this report, results are presented from a 334-participant online
survey of people living in the United States. The survey focused on travel behaviors with self-driving cars.
The vast majority of participants (83%) stated that they were willing to ride in a self-driving car. Partici-
pants were largely open to using a shared self-driving service frequently (43% always or often), but they
were more mixed on using self-driving cars to commute. A clear majority (69%) were uninterested in
living farther away from work with self-driving cars. In general, three categories of participants showed
interested statistical significance in their decision-making. People who valued car ownership highly
were more likely to want to live farther away from work with self-driving cars and to want to continue
owning a car with shared self-driving services available. People who had longer commutes were more
likely to want to use self-driving cars to commute, but less likely to want to live farther away from work
with self-driving cars. Third, people who had experience with or interest in current shared mobility ser-
vices were more likely to be interested in using shared self-driving services often and were more likely
to be willing to carpool in shared self-driving cars for a lower fare. Numerous policy recommendations
can be derived from these findings. Most prominently, planners should focus on shared mobility, travel
demand management, and growth management policies.
2
Driving into the Future
The automobile dominates transportation in the
United States. For a population of 308 million, we
have 254 million automobiles (BTS 2014a). Driving
has become a way of life for Americans with a
full 86% of commuters driving to work each day
(BTS 2011). From 1970 to 2010, the population
grew by 52%, but the total annual vehicle miles
traveled (VMT) exploded by 165% (FHWA 2014;
United States Census Bureau 2014). Though the
car has facilitated levels of mobility and acces-
sibility previously unknown, the disadvantages
in time, land, and environment of auto-depen-
dence have put into question the advantages.
We dedicate 74.9 minutes to drive 38.4 miles per
day, and the average annual hours spent in con-
gested traffic conditions has more than doubled
from 16 hours in 1982 to 38 hours in 2011 (Krumm
2012; Schrank et al. 2012). Beyond the impacts
of car use on our day-to-day lives, the green-
house gas (GHG) emissions created from driving
will continue to affect the environment for gen-
erations. The US is second only to China in GHG
emissions of which transportation accounts for
27% (CDIAC 2014; EPA 2013).
Yet the dominance of the automobile may have
peaked. Alternatives transportation modes, such
as bicycling, walking, and riding transit have in-
creased in popularity (BTS 2014b; McKenzie 2014).
For example, the percentage of trips made by
walking doubled from 1995 to 2009 (BTS 2014a).
Vehicle miles traveled has seen a decade-long
and likely permanent decrease (Short 2015).
Most significantly, teenagers and young adults
are showing less interest in driving or even in ob-
taining a driver’s license, an achievement that
was once seen as a rite of passage (NHTSA 2012;
Davis et al. 2012).
These trends away from the car could reverse
with the market introduction of self-driving cars.
Defined by the National Highway Traffic Safety
Administration (2013) as vehicles that can oper-
ate without direct human driver input, self-driv-
ing cars could introduce a new way of traveling
that is safer, more comfortable, and less stressful.
Such a shift in quality of car travel could have
enormous consequences for the transportation
system as a whole.
While self-driving cars could introduce an ar-
ray of travel behavior changes, two possible
extremes have been discussed in the literature
and the media. At one extreme, self-driving cars
could increase suburban sprawl beyond our
wildest imagination of limitations. If commut-
ers could sleep in their cars, get ready in their
cars, and begin working in their cars, just how far
away from their workplace would they care to
live? At the other extreme, car ownership could
fade into near non-existence as travelers prefer
to call a self-driving taxi when needed, facilitat-
ing a more compact way of living with far fewer
parking spots bracketing our communities. It is
also possible that the transformations introduced
with self-driving cars are beyond what we could
imagine at this point. The transportation technol-
ogy could be as transformative to our mobility as
the Internet has been to communication.
This report presents results from a survey on trav-
el behaviors with self-driving cars. It begins with
an overview of self-driving cars, including a de-
tailed definition and a review of research mod-
eling the possible use of the technology. Next, a
review of literature on how people use cars gives
CHAPTER 1:
Introduction
3
Driving into the Future
some precedence for the adoption of self-driv-
ing cars. Third, the report presents findings for the
334-person online survey conducted. The report
concludes with a discussion of the implications of
the survey findings, including three policy recom-
mendations.
4
Driving into the Future
Cooperation and Control:
Defining Automation
There are many words being used today to de-
scribe this type of emerging vehicle. Autono-
mous vehicles, automated cars, self-driving cars,
and driverless cars are all terms used to describe
what is often assumed to be the same thing. To
complicate the issue even further, there is often
confusion between connected and autono-
mous cars. Are these vehicles robots moving in-
dependently together? Or are they more akin to
cell phones, which derive their functionality from
their connectivity?
The basic components of today’s automated
vehicle technology include computer vision that
is supplied by radar, cameras, and lidar (a de-
vice like radar that uses light rather than elec-
tromagnetic waves to detect and reconstruct
the surrounding environment), global positioning
systems (GPS) and mapping software, and me-
chanical parts that have been integrated with
the software (Chatham 2013). None of these
technologies require the vehicle to be connect-
ed or communicative with other cars (V2V) or
with the roadway infrastructure (V2I), though
such connectedness would facilitate a safer
driving experience (Milanés et al. 2014).
The differences between automated and auton-
omous is a matter of degree. The National High-
way Traffic Safety Administration (NHTSA) defines
automotive automation as the machine coordi-
nation of or more safety critical vehicle functions,
such as throttle, braking, shifting, and steering.
Automation progresses on a Level 0-4 system
with Level 0 being no automation and Level 4
being complete automation (NHTSA 2013).
Nearly all cars on the road today are at either
Level 0 or 1 automation. Level 0 automation
means that at all times a human driver must be
in full control of all safety critical functions of
the vehicle. At Level 1 automation, the car can
control one safety critical function. Technically,
we’ve had Level 1 automation market penetra-
tion since the 1980s with the introduction of auto-
matic transmissions. But because shift control has
become so normal and even assumed, with 90%
market penetration in the US (Litman 2014), con-
versations about automation rarely include au-
tomatic transmissions. More commonly, Level 1
automation functions include control over steer-
ing, braking, or accelerating. Examples of Level
1 automation for those three functions include
lane keeping – sensors on the car detect when
the driver is veering towards lane lines and au-
tocorrects; automatic braking – the brakes acti-
vate when the human driver is not applying the
required pressure in an emergency; and cruise
control – the throttle adjusts pressure according
to a preset speed. Cruise control has been an
available vehicle enhancement since before
automatic transmissions, and many drivers today
are used to the idea that cars can handle speed
regulation in road-trip environments, a small but
important baby-step towards more fully auto-
mated vehicles. Newer Level 1 capabilities, such
as lane keeping and automatic braking, are
features on a range cars, from more economical
cars such as the Ford Fusion to luxury cars such as
the Mercedes S-Class.
CHAPTER 2:
The Open Road
Ahead
5
Driving into the Future
At Level 2 automation, two automated safe-
ty critical functions coordinate together. The
clearest Level 2 example is a combination of
lane keeping and adaptive cruise control — a
smarter cruise control in which front-facing sen-
sors detect slowed traffic. A driver in such a situ-
ation could allow the computer to control speed
and direction, but the driver would still have to
watch the road for emergencies or for direction-
al changes.
Level 3 automation is the first level of autono-
mous driving. The Level 3 vehicle can coordinate
full control of driving in specified situations. For
example, on the highway, the car could coor-
dinate speed regulation with lane keeping and
also watching for and responding to unpredict-
able changes in the state of the road. Level 3
automation is the first level at which the driver
can cede attention from driving to focus on oth-
er tasks. Once the specified situation changes,
however, the driver has to resume control. For ex-
ample, if a Level 3 vehicle were in control on the
highway, the driver would have to be in control
on highway ramps and within the city.
Level 4 is the only truly autonomous vehicle. At
this level, a “driver” becomes a supervisor or
merely an occupant. In Level 4 automation,
no occupant of a vehicle could be capable of
driving in the sense that we know it today. The
occupants could be elderly, disabled, children,
intoxicated, or highly distracted. In fact, Level 4
does not require a human to be in the vehicle at
all. There are no passenger vehicles available for
public use today that are at Level 4 automation,
but many vehicle and equipment manufactur-
ers, software companies, and startup aftermar-
ket products companies are testing automotive
automation from Levels 2 all the way to 4.
An important note regarding terminology in this
report is necessary. In the popular media, the
terms “self-driving car” and “driverless car” are
most commonly used to describe this vehicle
technology. Both terms are problematic. The
term “self-driving car” has been popularized by
the Google Self-Driving Car, even though the
autonomous technology Google uses and de-
velops is similarly being used and developed by
numerous companies. “Driverless car” is simply
inaccurate. Even with a fully autonomous vehi-
cle, the software and hardware components still
“drive” the vehicle and for the foreseeable fu-
ture, all such vehicles will have override features
to, at the very least, bring the vehicle to a full
stop (Iozzio 2014). A more accurate terminology
palette uses:
•	 “automated” to describe Levels 1, 2, or 3 ve-
hicles,
•	 “fully automated” to describe Level 4 vehi-
cles,
•	 “autonomous” to differentiate fully automat-
ed vehicles that are or are not connected,
•	 and “automated vehicle technology” to de-
scribe the field itself as well as aftermarket
hardware and software packages that can
be added to existing vehicles to provide au-
tomated features.
Nevertheless, in this report, “self-driving cars” is
the primary term used. Because “self-driving car”
is a more common and digestible term than “au-
tonomous vehicle”, it was the term “self-driving
car” and not “fully automated vehicle” or “au-
tonomous vehicle” that was used in this report’s
survey. And because “self-driving car” was the
term used in the survey, it is also primarily used in
this report describing the survey.
6
Driving into the Future
Increased Sprawl or Shared
Mobility: A Range of Possi-
bilities
The development of and hype around self-driving
cars alarms planners, urbanists, and environmen-
talists who have watched the rise of the car and
the demise of the central city. While in their first
two decades, travelers used cars as recreational
luxuries, as bonuses to add to their transportation
lives, they eventually became more affordable
and accessible to wider segments of the popu-
lation. As cars grew in popularity, so cities and
regions laid the infrastructure to support them.
But as the infrastructure grew, as more roadways
were built and expanded, communities dissipat-
ed through the long web of the highway system.
Over a few short decades, what had began as
an investment in bringing communities togeth-
er became a tool to wedge them apart. (See
Hayden 2003 or Glaeser 2011.)
The story of the urban disinvestment that fol-
lowed in the tracks of the car is familiar to trans-
portation professionals. Much of planning in the
late Twentieth and early Twenty-first Century has
focused on how to bring communities back to-
gether and, more generally, how to recover from
the car. These and other efforts have paid off as
driving is becoming slowly less popular with each
new generation. Could automated vehicle tech-
nology reverse such progress?
Automotive automation could make driving as
we know it today more appealing by removing
some of the most stressful components of driv-
ing. Automating highway driving in particular is
a low-hanging fruit for many vehicle automa-
tion developers. Tesla, for example, announced
a much-hyped new vehicle that would be 90%
autonomous (Ackerman 2014), or autonomous
for highway driving, which accounts for much
of total vehicle miles traveled. In highway con-
gestion, drivers of such vehicles could largely re-
move their attention from the road, knowing that
their car would deal with the necessary stop and
go movement. In highway driving without con-
gestion, these vehicles could also relieve drivers
of the slight and continuous throttle and steering
adjustments of highway driving. If such cars are
allowed in dedicated lanes, such as high-occu-
pancy toll lanes, highway driving could be even
easier and more free-flowing for owners of such
vehicles (Keefe 2014). Overall, it is possible that
highways could become more efficient, allow-
ing more vehicles in a more compact space and
enabling a greater number of cars to traverse
highways faster (Bierstedt et al. 2014; Shladover
2012). If highway driving becomes so easy and
stress-free, drivers could be incentivized to live
farther away from their places of work, encour-
aging the low-density development that has dis-
sipated cities and threatened arable and sensi-
tive environments.
On the other hand, the self-driving car could be
revolutionary. What Uber has done to the taxi
industry, autonomous cars could do to the car
industry as we know it today. Instead of purchas-
ing and parking cars, app-based transportation
services could usurp and nullify the vehicle own-
ership model. With such a service, a car could ar-
rive at your door each time you wished to leave
and take you to your destination, leaving for its
next customer afterwards. Rather than the car
being parked all day while its owner goes about
her or his day, the car could be servicing other
passengers. With such a service, payment could
be handled through the app, and the fee could
include fare and taxes. Payment could also be
“gamified” to encourage sustainable use. For
example, a rider could earn a bonus or fare re-
duction by sharing the ride with passengers go-
7
Driving into the Future
ing in similar directions, by being willing to deliver
freight on route, or by opting for a smaller or more
efficient vehicle. Fees could also be reduced or
nullified for individuals receiving government sub-
sidies. Cars not being used for passenger trans-
port could recharge, complete freight deliveries,
or be of use for necessary but not time-sensitive
transportation. Repair stations could service vehi-
cles that are damaged or worn down. Since the
cars would be used more continuously, the fleet
would turn over faster than it does today, pro-
viding economic incentives for manufacturers to
provide such services over the purchasing model
and also encouraging more environmentally in-
novative vehicles to be on the road sooner. The
possibilities for revolutionary change with such a
service are endless.
Literature is beginning to flourish that unfolds the
mechanics of such a system. The first and most
influential study was conducted by the Earth
Institute of Columbia University by a team of
researchers led by former General Motors R&D
director, Lawrence Burns (2013). The research-
ers modeled the total number of shared auton-
omous vehicles (SAVs) required to achieve the
same mobility of traditional, privately owned ve-
hicles. They performed this analysis in three differ-
ent scenario regions: a typical suburban-urban
commute community (Ann Arbor, Michigan), a
transit-inefficient small town (Babcock Ranch,
Florida), and a dense, transit-rich city in which
taxis are the predominant form of passenger ve-
hicle travel (New York City, New York).
The most significant time and cost efficiency
came from the model scenario of Ann Arbor.
While achieving the same mobility level, the re-
searchers found the total number of traditional,
privately owned vehicles could be reduced by
85% - from 120,000 traditional, privately owned
vehicles to 18,000 SAVs. In their modeling, the
SAVs are in use 75-80% of the time on a given
weekday. This range compares with the average
usage rate of traditional, privately owned of 5%
or less.
The modeled Babcock Ranch and New York
City saw less substantial efficiency gains with
SAVs. Babcock Ranch is a planned city in Flori-
da that has been built as a “living laboratory” of
smart and efficient technologies integrated with-
in a community. It is likely that Babcock Ranch
is more intelligently designed than most small
towns, allowing for better designed and placed
transportation systems. Nevertheless, it is a small
town based on low density development and ac-
cess to open spaces that make destinations less
accessible to each other. At full build out, Bab-
cock Ranch is expected to have a population of
50,000. If this town’s population has the average
number of vehicles per person as the US general-
ly, there will be 42,100 privately owned vehicles in
the community. As in the Ann Arbor case study,
the researchers made a series of informed as-
sumptions regarding average trip total per day,
average trip length, and average speed. They
found that 4,000 vehicles would supply mobility
to the residents of Babcock Ranch with total wait
times not exceeding a minute, but that each trip
would be somewhat more expensive at an aver-
age of $2 per trip (Burns et al. 2013).
The third modeling the researchers conducted
was replacing taxi cabs in New York City with
SAVs. Within the five boroughs, 53,000 taxi cabs
and for-hire vehicles provide 410,000 trips per
day. They found that an average 9,000 SAVs
could provide rides with wait times under a min-
ute. They also found that imbalances between
trip origins and destinations would entail that 11%
of the average trip would be empty of passen-
gers. These SAVs could be dramatically cheaper
at an average of $1 per trip compared to the
8
Driving into the Future
current average of $7.80 per trip in a taxi cab.
As with current taxis, there would likely be a con-
siderable imbalance between cars necessary for
peak period and cars available during off peak
hours.
Despite some differences in cost savings and
relative efficiency, all three modeling scenarios
produced far superior results for a more sustain-
able future. Fewer cars would be needed, creat-
ing fewer emissions and consuming far few park-
ing spaces (Burns et al. 2013). In this first modeling
study, therefore, the promise of SAVs was found
to be great.
Other studies have continued to build off of the
Transforming Personal Mobility study. For exam-
ple, Fagnant and Kockelman (2014) similarly
modeled the possible car use changes with a
generated model of a fictional city similar to Aus-
tin, Texas. The researchers ran the model for one
hundred days to ensure greater confidence in
their results. Their modeling showed SAVs to add
an average of 10% more travel distance than
traditional cars, but that a more than 90% reduc-
tion in total passenger vehicles could provide
the same level of mobility. In other words, though
individual vehicle miles traveled (VMT) would
increase, the cumulative effect would still be a
reduction in VMT, given the fewer total number
of cars on the road. The model produced similar
benefits for parking reductions. They found that
for every SAV, eleven parking spaces throughout
the city could be eliminated. If a few thousand
SAVs could provide mobility for an entire city,
tens of thousands of parking spaces could be de-
veloped into other, more beneficial uses. Further,
SAVs could reduce emissions, not only in reduc-
ing the total number of vehicles, but also signifi-
cantly reducing the number of cold starts when
vehicles produce the most emissions. Overall,
Fagnant and Kockelman found that SAVs could
bring drastic environmental and land use gains
to a city.
The possible range of behavior changes resulting
from the development of self-driving cars could
be as great as is the range of automotive auto-
mation development is itself. While vehicles avail-
able for public use are currently only at automa-
tion Levels 0, 1, or 2, Level 3 vehicles and even
Level 4 vehicles will likely be on the market soon.
With Level 4 automation, we could see a dra-
matic range of travel behavior changes from the
unprecedented expansion of suburban sprawl
and the maximum acceptable commute to the
abandonment of the vehicle ownership model in
favor of shared autonomous vehicle fleets. Such
a range of possibilities makes planning difficult,
to make an understatement. The next chapter
provides a detailed literature review of emerg-
ing vehicle technologies and how people have
responded to those technologies. With an under-
standing of our past, we may more intelligently
plan for the future.
9
Driving into the Future
10
Driving into the Future
From one perspective, there is no precedent for
self-driving cars. This technology could effective-
ly nullify the time cost of transportation, allowing
passengers to continue their lives relatively unin-
terrupted while moving from one place to anoth-
er. The time cost of travel could drop significantly.
Yet self-driving cars are emerging from a long his-
tory of innovations in transportation. From steam
engines to motor boats and from cargo trains to
high speed rail, human societies have continu-
ally sought for greater efficiency in movement.
But just because self-driving cars may be more
efficient does not mean they will automatical-
ly be adopted into popular use. The automatic
transmission created a more efficient driving ex-
perience, for example, yet it took decades to be
widely adopted in the US and still has not gained
majority use in many European nations (Litman
2014). Technological progress and use is never a
given, especially when it concerns a technology
and intimate as transportation.
If the use of more efficient transportation tech-
nologies is not a given, how can planners and
policymakers plan for emerging trends? Even as-
suming the technical development and market
release of self-driving cars proceeds as predict-
ed (Ro 2014) — a large assumption — it is not a
given that people will be enthusiastic about the
emerging products. Autonomous cars could, for
example, fail spectacularly on the market, such
as the highly publicized Segway (Rivlin 2007).
Gauging market interest now could shine some
light on a likely range of adoption paths.
Because of the speculative nature of self-driving
car adoption, determining how best to gauge
market interest is far from clear. Three major
genres of literature shed light on how self-driv-
ing vehicle adoption may unfold. First, self-driv-
ing cars may present a shift in how travelers
view cars as transportation modes. Carsharing,
an alternative model of vehicle ownership that
has gained momentum in Europe and North
America, allows urban residents to subscribe to
a car service. How subscribers have used and
responded to carsharing may reveal changing
perceptions towards innovations in car owner-
ship. Second, the gained market share of elec-
tric vehicles may serve as a recent movement
in the automotive market that shows consumers
are willing to try new technologies, even when
issues such as range anxiety continue in the pop-
ular conversation.
The third major genre of literature that could serve
as a guide for gauging market adoption is the
collection of studies analyzing behaviors and at-
titudes towards automotive automation. Studies
are being conducted that analyze participants’
implicit trust in and response to automation by
understanding their use of automated features,
such as parallel parking assist. Simulation studies
have also allowed participants to elect to drive
manually or autonomously, noting their behav-
iors when choosing autonomous driving. Further,
a growing body of literature is analyzing individ-
uals’ explicit trust in and response to automation,
including automotive automation, through sur-
veys and interviews. Together, the two methods
for understanding use of and orientation towards
automation create a more detailed landscape
for determining potential market adoption of
CHAPTER 2:
Finding Precedents
for Self-Driving
Cars
11
Driving into the Future
self-driving cars.
A New Way of Driving: Car-
sharing
First introduced in Europe in the 1980s as an al-
ternative to privately owned cars sitting unused
most of the day, carsharing has grown into a
global movement. Most early European car-
sharing organizations started as publicly funded
services that offered vehicles on a pay-per-use
system. Beginning with two organizations in Swit-
zerland and Germany, carsharing services now
have hundreds of thousands of members across
Europe (Shaheen et al. 1998). Though carsharing
has caught on slower in North America, due in
large part to the lower density pattern of devel-
opment, it has continued to grow in populari-
ty, particularly in the last decade. In the United
States, carsharing services tend to operate on
a subscription and pay-per-use system in which
members can access either a neighborhood car
or cars from a specific company. Usually, these
cars have dedicated parking spaces where the
vehicle is both picked up and returned (Shaheen
et al. 2009). Some one-way carshare services
have burgeoned in Europe with slower adoption
in North America. In these services, shared cars
are located by app rather than parking space
and the car is only paid for while the user drives it
(Firnkorn and Müller 2011). With carsharing, drivers
can have the greater access and mobility pas-
senger vehicles afford without being burdened
by the cost and responsibility of car ownership.
The People of Carsharing
Understanding the types of people who are in-
terested in or who use carsharing services can
shed light on why carsharing fails or succeeds.
Income is by far the most studied demographic
characteristic of carsharing users with some con-
flicting results. Two surveys showed people with
higher incomes are more likely to be interested
in carsharing. An intercept survey of 840 Beijing
residents found that higher income respondents
were generally more interested in carsharing
than lower income respondents, but that the
difference was minimal (Shaheen and Martin
2006). Similarly, an online study of 1,340 current
carshare users in North America found that most
participants were middle or upper income, but
in the United States, more carshare users made
less than $10,000 a year than statistically average
for the larger population. In other words, carshar-
ing populations skewed towards higher incomes
and very low incomes. In both studies, the great-
er number of higher income individuals showing
an interest in carsharing was only slightly statisti-
cally significant.
Other studies have shown lower income individ-
uals to be generally more likely to use carshar-
ing services, though these studies are also not
without caveats to their findings. An in depth
analysis of three years worth of carsharing data
in Toronto found that members in lower income
neighborhoods are more active and active for
longer than members in higher income neigh-
borhoods (Costain et al. 2012). Though specific
income data was not available for these car-
sharing members, the difference in low income
versus high income neighborhood use data for
members showed that members more likely
to be low income used carsharing much more
actively. Likewise, an online stated preference
survey of Greek residents found that the major-
ity of individuals interested in carsharing were
lower income (Efthymiou et al. 2013). Another
stated preference survey analyzing interest in
carpooling clubs likewise found lower income re-
spondents were more interested in coordinated
carpooling (Correia and Viegas 2011). Though a
12
Driving into the Future
study of cost rather than income, an intercept
survey of 500 people in Palermo, Italy found that
as the cost per kilometer of carsharing decreas-
es, carsharing becomes more appealing to indi-
viduals driving alone, carpooling, or taking transit
(Catalano et al. 2008).
The data are not conclusive enough to state
general trends in the incomes of carsharing
members. One possible explanation for this in-
consistency could be that members of different
income groups use shared cars differently. A
study of carshare availability and reservations in
Montreal analyzed a statistically significant yet
unclear relationship between income and car-
share use. Comparing neighborhoods based on
income in which there is a carshare station, av-
erage monthly car reservation time decreased
as income increased. Specifically, every $1000
increase in average income correlated with an
average decrease in more than 30 minutes that
cars were reserved every month. Yet the incomes
reported by members in an internal carsharing
service survey tended to be higher than the me-
dian income. Even though members tended to
have higher incomes, the rates of actual use
may have been greater in lower income neigh-
borhoods (de Lorimier and El-Geneidy 2010).
More studies on the relationship between mem-
ber income and use activity is necessary to fully
understand why higher and lower income popu-
lations are differently drawn to carsharing.
Likely the most consistent demographic charac-
teristic of carshare users is that they tend to be
young adults. Burkhardt and Millard-Ball (2006)
found in an online survey of 1,340 participants
that individuals interested in carsharing tended
to be in their 20s and 30s. In Shaheen and Mar-
tin’s study (2006) of 840 Chinese residents, more
younger survey participants than older survey
participants were interested in carsharing, with
those who were both familiar with and interest-
ed in carsharing particularly likely to be younger.
Correia and Viegas (2011) found in an online sur-
vey of 996 residents in Lisbon, Spain that young-
er adults are more willing to try a carpool club
than older adults. Even in a study that specifically
focused on young people’s views of carsharing,
the younger cohort was found to be more will-
ing to carshare. Efthymiou et al. (2013) surveyed
233 Greek residents aged 18-35 and found that
though respondents aged 26-35 are less satisfied
with their travel patterns than younger respon-
dents, they are less willing to join a carsharing
service than their younger counterparts.
Household size could be another significant indi-
cator of willingness to carshare, since the lesser
consistency and predictability of a communal
car could be easier to smaller households to
handle. In particular, families may be overly in-
convenienced enough to not use carsharing.
Unfortunately, no search revealed studies that
have found whether households with young
children are more likely to carshare. Burkhardt
and Millard-Ball (2006) found in their online sur-
vey of 1,340 individuals that people living in small
households were more likely to be interested in
carsharing. This data likely indicates that these
households did not having young children living
at home, though some of the two person house-
holds could have included a child. Knowing
household size could elucidate or complicate
the issue of age. Are young adults generally more
likely to use carsharing because they do not
have young children to care for and chaperone
around town? Is there any statistical correlation
between families and those not interested in car-
sharing? If the complexities of carsharing make
the transportation mode less appealing to adults
with children, such data could explain at least in
part the continued minority status of carsharing
as a viable and popular mode of transportation.
13
Driving into the Future
The Travel Patterns of Carsharing Members
If urban planners and researchers benefit from
knowing who are likely users of carsharing, an
even more important question is how carshare
users actually use their shared cars. When and
for what purposes do members use carsharing
services? Will carshare users shift from using tran-
sit or shift from using a privately owned vehicle?
Finally, how do users perceive transportation in
general? In other words, what general percep-
tions motivate an individual to become a car-
share user? At this stage in the market adoption
of carsharing services, travel patterns reveal that
though carsharing is a progressive and increas-
ingly common idea, it is by no means replacing
the privately owned vehicle as a staple of trans-
portation.
Research indicates that carsharing tends to be
a supplemental rather than a primary mode of
transportation for most users. The 6,000 active
AutoShare users in Toronto tend to use the ser-
vice on the weekend and with diminishing fre-
quency towards the beginning of the week, indi-
cating a reliance on other modes for the majority
of weekday travel. During the week, most trips
are made during the late morning hours, which
could indicate that users have a non-tradition-
al work schedule (Costain et al. 2012). Apply-
ing carsharing data in Zurich, Switzerland to a
combined agent- and activity-based modeling
software produced the most common carshare
use scenarios to be trips from home to a leisurely
activity, trips from home to shopping, and trips
from home to work (Ciari 2010). Most significant-
ly, carshare users in North America tend to travel
with a shared car only once or twice a month
(Burkhardt and Millard-Ball 2006), revealing that
a different mode of transportation is used the
majority of the month.
The most discouraging data regarding the viabil-
ity of carsharing comes from low user retention
rates. Since most carsharing services are for-prof-
it companies, complete user and usage data is
rarely obtained. In one study, however, Toron-
to-based service AutoShare allowed research-
ers access to full membership lists and reserva-
tion transactions for nearly three years. With 200
parking locations and over 6,000 active mem-
bers, AutoShare data provides a thorough case
study for what would appear to be a successful
carshare model. But if active usage is fairly wide
(defined quite liberally as a single use or more
over the past year), low retention presents a sig-
nificant problem to the success of the service. Af-
ter the first year, 60% of AutoShare members be-
come inactive. After three years of membership,
75% of members are inactive. For the majority of
AutoShare members, therefore, carsharing is not
becoming a habit (Costain et al. 2012).
On the surface, carsharing seems to hurt rather
than help transit. Two surveys found that transit
users were more likely than drivers to be interest-
ed in carsharing (Efthymiou et al. 2013; Shaheen
and Martin 2006). What is unclear from these sur-
veys is whether these current transit riders would
use carsharing in tandem with transit — using
carsharing to connect to transit or relying on car-
sharing when transit is not available — or whether
they would use carsharing as a more affordable
or more sustainable alternative to owning a car.
In other words, while it is possible these individuals
would stop riding transit with carshare, it is also
possible they would use carsharing to support
their transit travel. In the Beijing study referenced
above (Shaheen and Martin 2006), one inter-
esting finding could provide hope to emerging
economies struggling with greater numbers of
transit riders switching to driving on already con-
gested roads. Though more Beijing transit users
than drivers were interested in carsharing, tran-
14
Driving into the Future
sit users who were interested in purchasing a car
were twice as likely to be interested in carsharing
than drivers who are interested in purchasing a
car. In other words, rather than adding private-
ly owned vehicles to the road, new drivers were
looking to shared cars as a viable alternative.
The Future of Carsharing
Even if carsharing remains a service that attracts
a fraction of the number of people as does pri-
vately owned vehicles, the attitudes that have
allowed carsharing to thrive at all could indi-
cate a changing transportation landscape. In
a survey of North American carshare users, Bur-
khardt and Millard-Ball (2006) found that partic-
ipants see transportation pragmatically. People
who opt for carsharing do not like the inconve-
niences of car ownership. Similarly, when asked
about the advantages of owning a car, Beijing
residents rated most highly travel convenience,
followed by travel comfort, and greater mobili-
ty. The disadvantages of car ownership included
parking difficulties, pollution, and cost. For those
who did not already own a car, the two great-
est deterrents to purchasing a vehicle included
cost, transit convenience, and parking difficul-
ties (Shaheen and Martin 2006). If carsharing is
affordable and supports transit in delivering the
most convenient and comfortable transporta-
tion experience possible, nations such as China
could lead the world in adopting a more sustain-
able yet economically empowering alternative
to the privately owned vehicle.
A New Type of Vehicle:
Electric Vehicles
Like carsharing, electric vehicles are a more sus-
tainable alternative to the traditional privately
owned, conventionally fueled vehicle. Also like
carsharing, electric vehicles require little policy
change or infrastructure investment from gov-
ernment, though greater public sphere attention
can help such alternative transportation options
flourish. Carsharing and electric vehicles have
clear advantages, both from societal and per-
sonal perspectives. Yet just as carsharing has
slowly entered the market due to disadvantages
in convenience and other issues, so electric ve-
hicle adoption has grown slowly due in part to
public perceptions of overriding disadvantages.
Most prominently, fears of being stranded by a
dead battery, called “range anxiety”, have dom-
inated past discussions surrounding the viability
of electric vehicles. Despite their disadvantages,
the increasing popularity of both carsharing and
electric vehicles could indicate a changing land-
scape for cars in general. Both the advantages
and the disadvantages of electric vehicles apply
to autonomous vehicles. The possible mobility,
efficiency, and land use advantages of autono-
mous vehicles may only be realized in small por-
tions due to concerns over loss of control. While
neither range anxiety nor the concern over loss
of control is unfounded, much of the innovation
of the last decade regarding electric vehicles
has been in extending range and demonstrating
the viability and reliability of electric vehicles. This
section explores nearly two decades of public
opinion and related studies concerning the intro-
duction and use of electric vehicles.
The People Who Are Interested in Electric Vehi-
cles
The types of people who are interested in elec-
tric vehicles (EVs) cannot be summarized into
clear demographic categories. One study of
more than 3000 individuals found that people
interested in EVs tend to be younger and more
15
Driving into the Future
highly educated. This study also found that in-
come was not statistically significant (Hidrue et
al. 2011). In other cases, however, income be-
came a factor by the mere expense of electric
vehicles. In 2009, BMW released an electric ver-
sion of their Mini Cooper called the Mini E. The
Mini E was available by an $850 lease to a limited
number of households in Los Angeles and New
York. BMW and the University of California at Da-
vis conducted a series of surveys with the house-
holds that leased the Mini E, including a travel
diary analysis and a series of in-depth interviews
for a selection of those households (Woodjack
et al. 2012). By nature of the $850 monthly lease,
any household but those who could afford such
an experiment could participate. It may be the
case that income likewise biases other studies of
electric vehicle adoption. In general, however,
public opinion studies of EVs tend to find few spe-
cific demographic trends.
People are interested in electric vehicles for a
variety of reasons. Two commonly assumed or
attributed factors for purchasing vehicles, envi-
ronmental and fuel economy concerns, are not
always central motivations. A series of interviews
with owners of hybrid electric vehicles found en-
vironment to be a central motivation, along with
concerns about international peace and interest
in supporting technological innovation (Heffner
et al. 2007). Other studies found that participants
valued environmental benefits of EVs in explic-
it statements of attitudes or beliefs, but did not
necessarily list environmental reasons as cen-
tral to EV purchase or interest (Ozaki and Sev-
estyanova 2011; Delang and Cheng 2012). While
fuel economy was often listed as a motivation
for purchasing an EV, study participants often
struggled to formulate or repeat their personal
transportation budget, showing a basic lack of
understanding as to how exactly fuel efficiency
saves money (Cheron and Zins 1997; Kurani and
Turrentine 2004).
Range Anxiety — A Needless Concern?
An early study of public perceptions regarding
electric vehicles first highlighted the problem
of range anxiety. Four focus groups in Montre-
al gathered urban residents together to discuss
car ownership in general as well as their partic-
ular reactions to the pros and cons of EVs. Par-
ticipants identified the most important features
of car ownership to be comfort and reliability,
specifically to protect against the difficulty of
starting vehicles during Montreal’s cold winters.
Their most dominate fears regarding general car
ownership were running out of gas, followed by
having an accident and having a mechanical
breakdown. When first asked to express their un-
derstanding of EVs, participations stated associ-
ations such as, “a more expensive car, a small-
er and less comfortable car, a slower car, a car
with limited range, a less reliable car, a less safe
car, a golf cart” (Cheron and Zins 1997, 283) —
mostly highly negative associations. Participants
were then introduced to a formal definition of
EVs after which they discussed the most promi-
nent challenges to EV adoption. Participant dis-
cussions revolved around dead batteries and
general range anxiety strongly enough that the
researchers concluded that this fear alone may
be prevent market penetration of EVs.
Since the late 1990s, other studies have sought to
disprove range concerns by showing evidence
that even the lower end of available range in
EVs provides sufficient travel for most daily needs.
A three-day travel diary survey of 454 house-
holds in eight metropolitan regions of California
found that most households can easily replace
one household vehicle with an EV and still satisfy
all their transportation needs. For these house-
16
Driving into the Future
holds, range is not an issue (Kurani et al. 2001). In
the study mentioned above of households that
leased the Mini E, 89% of desired destinations
were within a range of 160 miles. With a com-
paratively low range of around 100 miles a day,
some of these destinations would not be accessi-
ble with the Mini E. The farthest destinations, how-
ever, tended to be social or recreational and not
everyday activities. The majority of survey partic-
ipants did not drive more than 80 miles a day,
allowing the Mini E to satisfy their travel needs
with a complete overnight charge to reset their
range.
The Future of Electric Vehicle Adoption
Small steps have characterized electric vehicle
market growth. In Germany, a survey of 1152 indi-
viduals found only about 5% of participants were
interested in EVs. Taken alone, that seems to be
a small number, a mere fraction of the overall
interest in vehicle purchasing. However, were all
the individuals that composed the 5% to actually
purchase an EV, it would signify a marked growth
in EV adoption. In 2009, German consumers pur-
chased 162 EVs. If 5% of all purchases were EVs,
it would total 175,000 EVs sold or an increase in
over 1000 times (Lieven et al. 2011). Merely an-
alyzing the structural change in surveys from the
1990s to the 2010s shows the progress in EV un-
derstanding. In the 1990s, a definition of EV was
required and once the definition was given, sur-
vey or focus group participants showed strange
associations with them. By the 2010s, surveys
had no need to include a definition of EVs at all,
showing the significant progress in consumer un-
derstanding. After greater understanding may
also come greater adoption.
Do People Want Automa-
tion?: Vehicle Automation
Conditions Tests
Despite the clear benefits of both carsharing and
electric vehicles, their market penetration has
been minimal in comparison to the overwhelm-
ing dominance of traditionally fueled, privately
owned vehicles. Analyzing how people actually
use a new transportation technology may begin
to show why carsharing and electric cars — and
maybe eventually self-driving cars as well —
have been slow to catch on.
Implicit Response to Automation – People’s Be-
haviors
Trust in new technology can vary between a
person’s attitudes and her or his behaviors. For
example, a study of 69 college students showed
a lack of correlation between stated trust and
demonstrated trust in technology. Beginning with
a survey of the participants’ willingness to trust
technology, and automation in particular, the
second phase of the study asked the participants
to determine whether a bag passing through a
security scanner should be removed for review or
not. Participants gave an initial judgment wheth-
er to pass or review the bag, then they were
showed the judgment of an automated system
on the likeliness of the bag to contain a weapon
or some other illicit item. Participants was asked
to keep or change their initial decision. The auto-
mated recommendation was usually correct, but
not always. Following this exercise, participants
took a final survey, attaching evaluative words
to either “automation” or “humans” with the as-
sumption that quicker responses represent more
implicit associations. The researchers found that
explicit and implicit propensity to trust machines
17
Driving into the Future
did not correlate (Merritt et al. 2013). Participants
who stated a higher level of trust in automation
did not necessary show that same level of trust
when actually presented with machine auto-
mation. Likewise, participants who stated low
levels of trust, often enough showed trust in sit-
uational automation that conclusions could not
be drawn. The importance of this study for gaug-
ing the likelihood of autonomous vehicle adop-
tion is that explicit willingness to trust automation
may not translate into implicit willingness to trust,
tempering the findings of any stated preference
study.
When automation is clearly beneficial to users,
people tend to be more willing to lend the sys-
tem their trust. An experiment of trust in parallel
parking assistance found that participants were
more open to the technology after trying it. Par-
ticipants in this study were given a questionnaire
at the beginning of the experiment and after a
series of trainings took a test car out onto a down-
town Boston street to parallel park both manually
and with parallel parking assistance activated.
Participants were outfitted with heart rate moni-
toring equipment. Following twelve parking ma-
neuvers, participants returned to the lab to com-
plete a post- experiment evaluation of their stress
and their interest in parking assistance technolo-
gy. The difference in heart rate between manual
and assisted parking was statistically significant.
Heart rate even decreased during parking ma-
neuvers in assisted conditions compared to a
dramatic increase in heart rate for manual park-
ing. 76.5% of respondents stated that the assis-
tance made parking easier and 71.4% expressing
a belief that it would generally reduce parking
stress. The participants had been explicitly cho-
sen to represent a range of age categories, but
no statistical difference in demographics was
found. Older and younger participants were just
as likely to experience a reduction in stress and
just as willing to want to use the technology in
their own cars (Reimer et al. 2010).
When given the choice to use or not use auto-
mated features, drivers do not always choose
such features, even if the features increase the
safety and ease of driving. A different study of
parking assistance found that participants tend-
ed not to use cameras and auditory warnings
meant to assist them with backing out of a park-
ing space. Participants in this study conducted
sixteen parking maneuvers, including backing
out of spaces, while their pupil movements and
range of vision were measured. In the car, they
were provided with a rearview camera and an
auditory warning of any objects nearing the re-
versing car. About halfway into the testing, an
object was placed behind the vehicle without
alerting the driver to its presence. Most of the
drivers (59%) did not glance at the camera to
assist them when reversing and those who did
tended to glance less often as reversing trials
proceeded without incident. Once the object
was placed behind the car, most of the drivers
who used the cameras did not collide with the
object (87.5%), but 96.3% of the drivers who did
not look into the camera did collide with it. Fol-
lowing the collision or near collision incident, driv-
ers used the camera more often (Hurwitz et al.
2010).This study could indicate that with gradual
introduction of automation into the market, driv-
ers may become more and more accustomed
to the benefits of such systems and increasingly
opt to use them.
Studies in which participants are placed in more
fully automated driving conditions likewise re-
veal a gradual increase in use of the technology.
Participants in two multi-hour studies were given
the option to drive in manual or autonomous
conditions while in a controlled environment.
In the test vehicles, participants were offered a
18
Driving into the Future
range of entertainment options, including radio,
movies, books, and food. While both situations
were controlled, neither experiment environ-
ment was without risk. In one experiment, par-
ticipants were in a full environment simulator of
driving on a highway (Jamson et al. 2013). They
were offered the choice of using manual or au-
tonomous mode and were faced with light and
heavy traffic conditions. Participants were not
in a simulator in Llaneras et al. (2011), but were
asked to drive on General Motors testing grounds
for three hours, which protected them from most
real-world dangers while providing a real-world
feel. In both experiments, participants chose au-
tonomous driving for a majority of the test. Partic-
ipants in the simulation study tended to become
more relaxed, a finding consistent with the Re-
imer et al. (2010) finding of slowed heart rate with
assisted parking, with the percentage of time in
which their eyelids were closed more than dou-
bling. On the GM testing grounds, participants
increased the time looking away from the road
by 33%, engaging in all tasks, including high risk,
more fully disengaged tasks. If these studies can
be generalized to real driving conditions, drivers
may be ready to give up control at the very least
in conditions such as highways or other low com-
plexity roads.
Explicit Response to Automation – People’s Atti-
tudes
If implicit trust in transportation automation —
the actual behaviors people display in automat-
ed conditions — appears high enough for the
introduction of autonomous features in passen-
ger vehicles, people’s explicit trust or stated at-
titudes towards automation may also indicate
readiness or trepidation towards autonomous
transportation. As vehicle automation becomes
more of a reality, studies are increasingly be-
ing conducted to gauge public opinion of and
trust in automation. In general, people who take
surveys on autonomous vehicles are in favor of
them or express a willingness to eventually use or
purchase them (Howard and Dai 2014; Kyriakidis
et al. 2014; Schoettle and Sivak 2014; Shin et al.
2014). Demographic analyses tend to find men
and higher income individuals more interested in
autonomous vehicles (Schoettle and Sivak 2014;
Howard and Dai 2014). A study of individuals in
South Korea found that lower income people
were just as interested in smart technologies as
higher income individuals, though interestingly
they were not as interested in non-convention-
al fuel types, indicating a possible success in the
marketing of equity for technological but not
environmental efficiencies (Shin et al. 2014). An
international study of individuals in more than a
hundred countries around the world found that
despite background differences, participants
generally expect autonomous vehicles to be
available in the coming decades (Kyriakidis et
al. 2014). When asked what participants would
be interested in doing while driving, participants
chose a large array of tasks, possibly indicating
a trust that nearly any stationary task could be
accomplished in an autonomous car (Kyriakidis
et al. 2014; Schoettle and Sivak 2014). Interest in
autonomous cars for these participants seems to
be high.
While trust in automation seems to be increasing,
it is not necessarily clear what exactly people are
trusting more. Is it machines? Is it the companies
that build these machines? Or is it trust in the abil-
ity of average people to use and control these
machines? One study concluded that we may
not necessarily be trusting machines so much as
the people behind the machines. Carlson et al.
(2013) provided 737 online participants with one
of four scenarios: questions regarding automat-
ed medical diagnosis and IBM’s Watson, auto-
19
Driving into the Future
mated medical diagnosis without a brand name,
autonomous vehicles and Google’s Self-Driving
Car, and autonomous vehicles without a brand
name. Participants expressed their attitudes to-
wards automation in general and regarding the
specific type of automation under analysis. They
were then asked questions regarding the impor-
tance of brand. In the surveys with the branding,
participants provided a statistically significant
higher trust in the automated system. To con-
trol for specific opinions regarding companies
(as opposed to general trust in humans versus
machines), the surveys also provided questions
on aspects of the automated systems that the
participants trusted. For example, participants
were asked to rate how highly they weighed
the importance of system testing. For medical
diagnosis systems, participants ranked as one of
the highest factors of trust ability of the medical
professional to interpret the diagnosis system.
For autonomous cars, participants likewise rated
their own knowledge of the automated system
as critical to trusting the system. In all four survey
scenarios, people were trusting people. In order
for autonomous cars to penetrate the market,
a significant level of trust in the engineers and
companies who manufacture the software and
the vehicles will have to be well-established.
The Future of Automotive Automation
Both the implicit and explicit studies of people’s
response to automation show a need for a de-
liberate engagement with autonomous cars on
the part of planners and policymakers. In gen-
eral, people tend to want this technology, yet
they aren’t as quick to trust it when they are ac-
tually using it. Once they have used it, however,
they tend to be open to using it more. A greater
understanding of how people are interested in
using automated features, and particularly how
they imagine using autonomous cars, could have
a significant impact on the direction of planning
and policy-making.
Conclusion
This literature review included a range of studies
in transportation innovation that may provide a
glimpse into for how autonomous vehicles may
be adopted. First, carsharing members tend to
be lower income, young people who live in small
households. For these members, carsharing rep-
resents a low-cost alternative to owning a car or
a low-hassle addition to what is often a single-car
household. Especially for emerging economies in
countries such as China, carsharing offers a sus-
tainable, affordable alternative to vehicle own-
ership that could ease urban residents into a high-
er level of mobility with a lesser cost to land use
and the environment than privately owned vehi-
cles have put on higher income nations. Second,
electric vehicles also offer a more sustainable
option to the conventional vehicle that require
little additional infrastructure and public invest-
ment. Many of the concerns surrounding electric
vehicles, particularly range anxiety, have been
shown to be mostly irrelevant for everyday use, a
fact that consumers are increasingly coming to
understand. Third, studies analyzing response to
automotive automation show a greater tenden-
cy to trust machines following actual experience
of the benefits with automation. In general, pos-
itive associations remain or increase and nega-
tive associations become neutral or positive after
using the automated feature. Coupled with posi-
tive anticipation for autonomous cars, the public
may be ready to use increasingly autonomous
vehicle features as they are introduced to the
market.
For determining the willingness to adopt autono-
20
Driving into the Future
mous vehicles, stated preference surveys create
an important bottom line for evaluation of pub-
lic opinion. Importantly, the survey presented in
this report goes beyond the basic questioning of
opinions and collects demographic and travel
behavior data. Through this data, a greater un-
derstanding of the types of people and types of
lifestyles of early adopters may help inform plan-
ners and researchers to the nature of transpor-
tation changes that may begin to appear. This
survey attempts to combine the values of both
quantitative and qualitative research by asking
not only numerical but open-ended questions
that allow participants an opportunity to express
responses along prepared lines and along the
lines of their own thoughts and attitudes. In this
study, participants will not have an opportunity
to test their implicit perspectives towards auto-
mation and autonomous cars, but this research
may build the foundation for future research ef-
forts that compare both explicit and implicit re-
action to and trust in automation.
21
Driving into the Future
22
Driving into the Future
This report presents the results from an online sur-
vey of 334 participants on demographics, trav-
el behaviors, and interest in self-driving cars. The
survey was created by the author and distribut-
ed in January and February 2015. Survey partici-
pants provided basic demographic information,
including age, gender, ethnicity and race, in-
come, and housing information. In an effort to
gauge cultural predictions for likelihood to adopt
self-driving cars, the survey also included a ques-
tion on political affiliation. Questions about travel
behaviors and perspectives on driving provided
a baseline analysis for interest in self-driving cars.
These questions included information regarding
vehicle ownership, the advantages and disad-
vantages of driving, interest in new vehicle tech-
nologies or ownership models, and commute
mode and length.
The final five groups of questions focused on
self-driving cars. First, participants were asked
to select their familiarity with self-driving cars fol-
lowed immediately by a direct question on will-
ingness to ride in one. Next, to determine trends
in vehicle ownership and shared mobility, partic-
ipants were given a description of a self-driving
ridesharing service and asked to gauge their in-
terest in and use of such a service. Third, partici-
pants were asked to provide their comfort levels
with people who currently cannot drive riding
in self-driving cars. With this question, planners
can begin to understand how many additional
vehicle occupants may be added with self-driv-
ing cars. The next group of questions focused on
commute frequency and length with self-driv-
ing cars. These questions seek to address one of
the most pressing questions for planners regard-
ing self-driving cars: will this technology enable
commuters to live farther from work, potentially
inducing more low density, suburban develop-
ment? Lastly, participants were asked to list their
top concerns and top anticipated features with
self-driving cars. The final question was open-end-
ed, and about 10% of the participants provided
written responses. The full survey questionnaire
and top line results are provided in Appendix A,
and answers to open-ended questions are pro-
vided in Appendix B.
Distribution Methodology
The survey was live online via the survey service
Qualtrics for the first two months of 2015. It was
advertised to three primary markets. The first mar-
ket category included people most likely to be
familiar with self-driving technologies. These indi-
viduals were sought out because they are more
likely to be early adopters, and the ways in which
they use self-driving cars could have reverberat-
ing effects on the larger population. The second
category of targeted participants included ur-
ban planners and transportation professionals
who are creating and implementing policies
today that will impact transportation tomorrow.
The third category of participants included indi-
viduals from no particular discipline or interest.
These individuals represent a more likely sam-
pling of the general population. A full list of sur-
vey distribution sources can be found in Appen-
dix E. Unfortunately, the survey software used for
this study provides no means of determining link
referrals. In other words, it is impossible to know if
the 334 participants represent an even sampling
of individuals from all three categories.
CHAPTER 3:
Self-Driving Cars
Market Adoption
Survey
23
Driving into the Future
Results
Demographics
The survey oversampled young, white male par-
ticipants. Most participants were young adults
with only 40 participants (or 12%) stating that
they were over 40-years-old (Fig. 1). The few par-
ticipants who checked that they were under 18
were sent to the end of the survey. The relative
paucity of participants over 40 is disappointing
given the potential importance of adoption for
that age category (Reimer 2014). However, the
survey adequately sampled the most likely con-
sumer age demographic, young adults, given
the projected rate of the technological devel-
opment. There is no such silver lining to the skew
in ethnicity, race, and gender. The majority of
participants were non-Hispanic (Fig. 2) and white
(Fig. 3) with certain demographics largely absent,
such as African Americans. In relation to national
population data, the survey was most skewed by
gender. Only 27.5% or 92 of the participants were
female, compared to 51% nationwide (Fig. 4).
The survey participants represented relative
skews in employment, income, education, and
political affiliation. Though the unemployment
rate was the same in the survey population as
the United States population at 5.7%, the survey
oversampled students. Although 26% of partici-
pants were students, most likely in a 2- or 4-year
college program given the age range, only 6.5%
of the national population is enrolled in higher
education (Fig. 5). Likewise, the survey overrepre-
sented more highly educated individuals. Signifi-
cantly more participants have college degrees
than the general public, and for significantly
fewer participants a high school diploma is their
highest educational attainment (Fig. 7). Income,
Fig. 3: RACE
Source (for US data): US Census Bureau. 2014. American Community Survey
2013. Washington DC: United States Census Bureau.
Source (for US data): US Census Bureau. 2014. American Community Survey
2013. Washington DC: United States Census Bureau.
Fig. 2: ETHNICITY
Fig. 1: AGE
Source (for US data): US Census Bureau. 2014. American Community Survey
2013. Washington DC: United States Census Bureau.
44% 43%
10%
2%
16%
19%
28%
19%
18-25 26-39 40-59 60+
Survey Population United States
3%
91%
17%
83%
Hispanic Non-Hispanic
Survey Population
United States
1%
5%
1%
0%
81%
3%
3%
1%
5%
13%
1%
74%
3%
5%
American Indian or Alaskan Native
Asian American
Black or African American
Native Hawaiian or Pacific Islander
White
Two or more races
Other race
Survey Population
United States
24
Driving into the Future
though also somewhat skewed, is more repre-
sentative than some of the other demographic
categories. However, 13% of survey participants
selected the option “cannot choose / refuse to
answer,” making true understanding of skew or
accurate representation difficult (Fig. 6). Survey
participants largely identified their political affil-
iation as either Independent or Democrat with
very few participants identifying as Republican
(Fig. 8).
Community and Housing
The United States Census does tally whether
residents live in urban or rural communities, but
they do not differentiate between urban and
suburban communities. From a planning and
transportation efficiency perspective, suburban
communities tend to be auto-oriented, making
regular transportation by any other mode diffi-
cult and slow. Urban communities, on the other
hand, tend to have more efficient transit options
with more walkable neighborhoods and better
bicycle networks. From a transportation perspec-
tive, many suburban communities are closer to
rural than urban communities in their offering of
a multimodal lifestyle. For that reason, this survey
asked participants to state whether they lived in
an urban, suburban, or rural community. A slight
majority of participants defined their community
as suburban with a close second group select-
ing urban as their community. Only 8% identified
their community as rural compared to 19% na-
tionally. Although the communities in which sur-
Source (for US data): US Census Bureau. 2014. American Community Survey
2013. Washington DC: United States Census Bureau.
Fig. 4: GENDER Fig. 5: EMPLOYMENT
Source (for US data): Bureau of Transportation Statistics. 2014. Pocket
Guide to Transportation 2014. Washington DC: United States Department of
Transportation; National Center for Education Statistics. 2015. Back to School
Statistics. Washington DC: United States Department of Education; Bureau
of Labor Statistics. 2015. Household Data, Seasonally Adjusted. Washington
DC: United States Department of Labor.
Source (for US data): US Census Bureau. 2014. American Community Survey
2013. Washington DC: United States Census Bureau.
Fig. 6: INCOME
Survey Population United States
51%
49%
Female Male
28%
70%
Female Male
59%
6%
26%
6%
2%
59%
4%
7%
6%
35%
Employed
Employed, work at home
Student
Unemployed, looking for work
Unemployed, not looking for work
Survey
Population
United States
9%
6%
14%
19%
12%
14%
14%
13%
11%
24%
18%
12%
13%
10%
Under $15k
$15-24k
$25-49k
$50-74k
$75-99k
$100-149k
Over $150k
Survey Population United States
25
Driving into the Future
vey participants live reflect national trends, it is
significant to know that 60% of participants live in
largely car-dependent rural and suburban com-
munities and 38% live in more multimodal urban
communities (Fig. 9).
Likely because of the age skew, the survey over-
represented renters and childless households. The
majority of United States residents are homeown-
ers, but only 30% of survey participants owned
their home. Sixty-seven percent of participants
either rented their home or lived in some other
Fig. 8: POLITICAL AFFILIATION
Source (for US data): Gallup. 2015. Party Affiliation. Washington DC: Gallup,
Inc. http://www.gallup.com/poll/15370/party-affiliation.aspx (accessed
March 4, 2015).
89.8%
8.1%
80.7%
19.3%
Urban Rural
Survey Population United States
89.8%
8.1%
80.7%
19.3%
Urban Rural
Survey Population United States
Fig. 9: COMMUNITY TYPEFig. 7: EDUCATION
Source (for US data): US Census Bureau. 2014. American Community Survey
2013. Washington DC: United States Census Bureau.
Urban
(38%)
Suburban
(52%)
Source (for US data): US Census Bureau. 2010. United States Census. Wash-
ington DC: United States Census Bureau.
Fig. 10: HOUSING STATUS
Source (for US data): US Census Bureau. 2014. American Community Survey
2013. Washington DC: United States Census Bureau.
situation, such as living with a parent or living (Fig.
10). The vast majority (77%) of participants lived
in households with no persons under the age of
18, compared to 67% nationally.
Transportation
The survey participants, though generally a pop-
ulation of drivers, were particularly multimodal
in their use of transportation. Most participants
owned or wanted to own a car. Significantly,
1%
6%
21%
3%
38%
28%
12%
30%
19%
9%
19%
10%
Under 12th grade (no diploma)
High school graduate
Some college, no degree
Associate's degree
Bachelor's degree
Master's degree or higher
Survey Population United States
39%
29%
7%
9%
29%
43%
25%
3%
Democrat Independent Republican Other political view
Survey Population United States Gallup Data
90%
8%
19%
81%
55%
30%
13%
35%
65%
Rent home Own home Other housing situation
Survey Population United States
26
Driving into the Future
given the auto-oriented nature of most United
States communities, 9% of participants neither
owned a car nor desired to own one (Fig. 11).
Although the largest percentage of participants
drove alone to work or school, 20% of participants
commuted via transit, five times greater than the
national average. Additionally, four times more
participants commuted by walking or bicycling
than the national average (Fig. 12). Further, they
were open to more sustainable methods of driv-
ing with the clear majority interested in hybrid
or electric vehicles (Fig. 14) and also the clear
majority interested in or experienced with shared
mobility services (Fig. 15).
Most participants considered owning a vehicle
to be a personal priority. Significantly, 31% of par-
ticipants rated car ownership as a low priority,
either “nice, but not necessary” or completely
unimportant “as long as I’m able to get where
I want to go” (Fig. 16). Such deemphasis on the
value of vehicle ownership may indicate a cul-
tural shift away from the car as status symbol.
Participant opinions on the value of driving re-
vealed general consensus on the advantages
Fig. 11: CAR OWNERSHIP
Question: Do you own a car?
of driving and a wide selection of
the disadvantages of driving. Par-
ticipants were asked to select the
top three advantages of driving
from a list of seven choices: af-
fordability, comfort, flexibility, pri-
vacy, safety, social status symbol,
and time saved. Flexibility, time
saved, and comfort were over-
whelmingly chosen as the top
advantages to driving with priva-
cy in a distant fourth. A small per-
centage of participants chose
safety, affordability, and symbol
as leading advantages. Overall,
Fig. 12: COMMUTE MODE
Fig. 13: COMMUTE TIME
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
Less than 15
minutes
15-29 minutes 30-44 minutes 45 or more
minutes
78%
12%
9%
Yes
No, but want to
No, and don't want to
47%
5%
20%
7%
10%
1%
76%
9%
5%
1%
3%
1%
Drive alone Carpool Ride transit Bicycle Walk Other
Survey Population United States
27
Driving into the Future
312
267199
138
69
62
56
Flexibility Time saved Comfort
Privacy Safety Affordability
Social status symbol
Fig. 15: SHARED MOBILITY
Question: Have you ever used a ridesharing service (Uber, Lyft, etc.) or a
carsharing service (Zipcar, car2go, etc.)?
Fig. 16: CAR OWNERSHIP IMPORTANCE
Question: How important is vehicle ownership to you?
Fig. 17: ADVANTAGES OF DRIVING
Question: In your opinion, what are the 3 greatest advantages of driving?
Flexibility1
3
2
Comfort
Time saved
Fig. 18: DISADVANTAGES OF DRIVING
Question: In your opinion, what are the 3 greatest disadvantages of driving?
251
172
171
152
149
137
93
Expense Inability to multitask Pollutions and emissions
Parking problems Stress Safety
Time wasted
251
172
171
152
149
137
93
Expense Inability to multitask Pollutions and emissio
Parking problems Stress Safety
Time wasted
Expense1
3
2
Pollutions and emissions
Inability to multitask
participants rated aspects of convenience as
the best attributes of driving (Fig. 17). Participants
were in less agreement regarding the disadvan-
tages of driving from a list of expense, inability
to multitask, parking, pollution and emissions,
safety, stress, and time wasted. Expense took the
most votes for a top disadvantage, which was
not surprising given the low selection for afford-
ability as an advantage. The next greatest dis-
advantage was spread fairly evenly among the
next five choices. Inability to multitask and pollu-
tion and emissions were neck-and-neck with 42%
and 41% of participants putting each choice in
the top three. Parking and stress were one step
Fig. 14: ELECTRIC VEHICLES
Question: Do you want to own a hybrid or electric vehicle?
312
267199
138
69
62
56
Flexibility Time saved Comfort
Privacy Safety Affordability
Social status symbol
8%
71%
10%
Currently own or use one
Would consider buying one
No, and not interested
41%
30%
21%
8%
Have used shared mobility services
Interested in shared mobility services
Not interested in using shared
mobility services
Not familiar with shared mobility
services
25%
34%
13%
18%
Among highest priorities
Important
Nice, but not necessary
Not important
28
Driving into the Future
lower with 36.5% and 32%. Safety took 29%. Time
wasted came in last with only 15% of votes (Fig.
18).
Self-Driving Cars: General
The heart of the survey asked participants to ex-
plore their opinions regarding self-driving cars. To
set the stage, participants were asked to state
their level of familiarity with self-driving cars de-
fined as “vehicles that can drive themselves with-
out human assistance, also called driverless or
autonomous cars.” Rather than providing a more
thorough definition of autonomous vehicles that
risked a participant skimming or becoming con-
fused, the survey provided a more simple defini-
tion that only defined the most capable, Level 4
vehicle. The results showed a general familiarity
with the technology among participants. Only 4%
of participants either stated that they were unfa-
miliar or they selected “cannot choose / refuse
to answer” whereas 96% stated that they either
closely follow the technological development
or they are somewhat familiar with the technol-
ogy (Fig. 19). Willingness to ride in a self-driving
car was also high with only 16% stating that they
were either not willing or not sure if they were will-
ing to ride in a self-driving car, compared to 83%
who were willing (Fig. 20). Because of this definite
familiarity with and openness to self-driving cars,
the more significant results came in the projected
use of and opinions regarding the technology.
Use of and opinions regarding self-driving cars
were divided into four sections. In the first two
sections, participants were asked questions re-
garding shared mobility and commuting prac-
tices with self-driving cars. In the third section,
participants selected their comfort level with var-
ious individuals who cannot drive riding alone in
a self-driving car. Finally, participants were asked
to select their top concerns and anticipated fea-
tures with self-driving cars, with an alternative to
write in a concern or feature not provided.
Self-Driving Cars: Travel Patterns
Two potential travel pattern changes with
self-driving cars were highlighted in the survey.
Participants were asked to express their interest
in a self-driving rideshare service, and they were
asked several questions regarding commuting
patterns. For the self-driving rideshare questions,
Question: Have you heard of self-driving cars (vehicles that can drive
themselves without human assistance, also called driverless or autonomous
cars)?
Fig. 19: FAMILIARITY Fig. 20: WILLINGNESS TO RIDE
Question: Would you be willing to ride in a self-driving car?
37.1%
59%
2.7%
0% 0%
Very familiar Somewhat
familiar
Not familiar,
but interested
Not familiar,
not interested
Doesn't sound
possible
83%
6%
10%
Yes No Uncertain
29
Driving into the Future
11%
60%
25%
Would continue to own a car
and not use such a service
Would continue to own a car
and also use such a service
Would not own a car with
such a service available
participants were asked to consider this service:
“Using an app or text messaging service, you re-
quest a self-driving car to pick you up within 5-15
minutes and take you where you need to go.
After dropping you off, the car serves other cus-
tomers. When you are ready to leave, you make
another car request.” Three questions followed
this text, gauging participant interest in such a
service, their interest in sharing a self-driving ride-
share car with another person, and their interest
in owning a car if such a service were available.
Participants were generally yet cautiously open
Question: How often would you use such a self-driving rideshare service?
Fig. 21: SELF-DRIVING RIDESHARE:
USE FREQUENCY
Fig. 22: SELF-DRIVING RIDESHARE:
SHARED RIDES
Question: Would you be willing to ride in a self-driving rideshare car with
other passengers for a reduced fare?
Question: Would you still own a car if such a self-driving rideshare service
were available?
+ own car, - use service
+ own car, + use service
- own car, + use service
Fig. 23: SELF-DRIVING RIDESHARE:
CAR OWNERSHIP
to a self-driving rideshare service. The largest por-
tion of participants stated that they would often
or sometimes use a self-driving rideshare service.
Comparatively few expressed absolute opinions
with only 12% stating that they would always use
such a service and 16.5% preferring to never use
the service (Fig. 21).
To reduce total cars on the roadways, carpool-
ing and decreased car ownership may be key.
When asked if they would be willing to carpool
in a self-driving rideshare vehicle for a reduced
fare, the overwhelming majority of participants
were willing. Only 14% were uninterested in shar-
ing a ride, compared to 41% who were willing
and 43% who were potentially willing (Fig. 22).
Unfortunately, the majority of participants stat-
ed that they would still own a vehicle with such
a self-driving rideshare service available. A full
quarter of participants, however, would not own
a car in favor of such a shared system (Fig. 23).
The implications of such a system could be sig-
nificant for transportation networks in all types of
communities. While it could change car owner-
ship trends, it could also threaten public transpor-
tation. Understanding the likelihood that such a
system would be used, therefore, has many impli-
12%
31%
32%
17%
5%
Always Often Sometimes Rarely Never
41%
43%
14%
Yes
Maybe
No
30
Driving into the Future
cations for the future of transportation planning.
It is possible that self-driving cars may have rela-
tively little impact on commuting practices. Al-
though almost half of all participants would only
occasionally use self-driving cars to commute, a
quarter of commuters would always use the tech-
nology to get to work. A relatively high 18% stat-
ed they would never use self-driving cars to com-
mute (Fig. 24). Further, most participants (62%)
were uninterested in living farther away from
work with self-driving cars (Fig. 25). Of the partic-
ipants willing to live farther away from work with
self-driving cars, it is impossible to know if these
participants would be interested in living in a low-
er or higher density environment. In other words,
would these individuals push for further suburban
or rural sprawl, or would they move to the city to
travel to a rural or suburban job? Although it may
be difficult to know their intentions the possibility
of increased sprawl is real with self-driving cars.
Self-Driving Cars: Increased Mobility
One of the great possibilities with self-driving cars
is the increased mobility gained for populations of
people who currently should not or cannot drive.
For example, drunk drivers should never control
the wheel, yet they do at an alarming rate. Some
disabled populations, such as the blind or those
bound to wheelchairs, cannot currently drive be-
cause of the requirements that drivers watch the
road and control the throttle and brake. Self-driv-
ing cars could facilitate mobility for these and
other populations.
This question of increased mobility is important for
four primary reasons. First, streets could become
safer with drunk drivers, distracted drivers, and
some teenage and elderly drivers ceding control
of their vehicles. Second and third, this increased
mobility with cars could facilitate greater equity
in transportation access while also threatening
the transit services that currently exist to provide
such mobility. The fourth consideration is that by
welcoming additional individuals into privately
occupied vehicles, more cars could be added
to already congested, polluted roadways. For all
these reasons, understanding the possible use of
self-driving cars by such populations is critical to
understanding the future of driving.
To gauge such an increase in mobility, partici-
pants were asked to rate their degree of comfort
Fig. 24: SELF-DRIVING COMMUTE
Question: Would you use self-driving cars to get to work or school?
Question: Using self-driving cars, would you live farther away from work or
school?
Fig. 25: EXTENDING COMMUTE
DISTANCE WITH
SELF-DRIVING CARS
24%
31%
17%
18%
Always
Sometimes
Rarely
Never
25%
69%
6%
Yes
No
Other (not possible,
cannot choose)
31
Driving into the Future
Fig. 26: VEHICLE OCCUPANTS IN
SELF-DRIVING CARS
Preferably Under certain circumstances
Only with a capable driver present Never
Preferably Under certain circumstances
Only with a capable driver present Never
Highly Distracted
Persons
Elderly Persons
Teenagers
Children2
1
Disabled Persons3
4
5
Question: Should the following individuals be passengers in self-driving cars
without an able, human driver?
with different types of people riding in a self-driv-
ing car. Specifically, they were asked to answer
this question: “Should the following individuals be
passengers in self-driving cars without an able,
human driver?” They were presented with a chart
containing a list of six population types: children,
teenagers, elderly persons, disabled persons, in-
toxicated or high persons, and highly distracted
persons. Participants were given the choice to
answer the question with one of four degrees of
comfort: “Yes, preferably,” “Yes, under certain
conditions,” “Only with a capable human driver
present,” and “Never” (Fig. 26).
In general, participants expressed a high de-
gree of comfort with the listed populations riding
in self-driving cars. The population most partici-
pants thought could ride alone were highly dis-
tracted drivers, perhaps with the assumption that
they would be most capable to taking control
in event of an emergency. The next population
for which participants selected a high degree of
comfort using self-driving cars was elderly per-
sons. Intoxicated or high persons came in third
place. It is hard to know what the 24% of indi-
viduals who selected “Yes, under certain circum-
stances” intended for these vehicle occupants,
but perhaps they assumed that occupants un-
der the influence may sometimes not be able
to provide navigation instructions to a car com-
puter. In those cases in which they are not ca-
pable of providing accurate instructions, riding
alone in a self-driving car could be dangerous.
For disabled persons, the next highest category,
47% of participants selected the highest degree
of comfort, perhaps indicating uncertainty on
whether disabled persons could independent-
ly maneuver in and out of a self-driving car. The
teenagers category was more evenly distributed
with nearly equal numbers of participants select-
ing the top three categories of comfort. Partic-
ipants were the least comfortable with children
61%
20%
13%
3%
36%
32%
24%
5%
54%
26%
15%
3%
20%
27%
34%
15%
47%
28%
19%
3%
32
Driving into the Future
being alone in self-driving cars. The largest per-
centage of participants only felt comfortable
with children riding in a car along with an adult.
Overall, the category with “never” selected most
frequently was the children category.
With the sole exception of the children catego-
ry, most participants selected the highest degree
of comfort for people who cannot drive current-
ly riding in a self-driving car in the future. While
stating level of comfort and actually enacting
comfort may not be the same thing, as shown
in the literature review with explicit versus implic-
it trust in automation, this data shows a strong
likelihood that self-driving cars may increase the
number of possible vehicle occupants on the
roadways. While this speculation does not have
to mean that the total number of vehicles will
also increase, given the possibility of decreased
vehicles on the road with shared autonomous
vehicles, it is nevertheless an important possibility.
Self-Driving Cars: Concerns and Anticipated Fea-
tures
Four concerns were most prominent among the
seven offered. As other surveys have shown, li-
ability is a persistent concern (Howard and Dai
2014; Kyriakidis et al. 2014; Schoettle and Sivak
2014). In case of a traffic incident, who is respon-
sible? The occupant? The hardware manufac-
turer? The software engineers? It may be new
legal territory. The uncertainties around how it
will work out worry the public more than many
vehicle automation issues. (For thorough discus-
sions of legal liability with autonomous cars, see
Villasenor 2014 or the blog of Bryant Walker Smith
at cyberlaw.stanford.edu.) In showing liability to
be a prominent concern, this survey was no ex-
ception. Liability received the highest number of
selections as a top concern. Usually, however,
control is also a top concern, but control came
in fourth place in this survey. In second place was
expense, which was no surprise given the weight
placed on expense as a disadvantage to driv-
ing earlier in the survey. The third highest con-
cern was data privacy, with control closely be-
hind. Safety received 21.5% of the votes as a top
concern, which is significant given that greater
safety is a highly publicized anticipated feature
of self-driving cars. Closely behind safety, partici-
pants worried that self-driving cars will be bad for
the environment, potentially through promoting
177
145
121
113
72
65
64
54
29
Liability Expense Data privacy Control Safety
Environment Nothing Other Congestion
Question: What are your top 3 concerns with self-driving cars?
Fig. 27: CONCERNS WITH
SELF-DRIVING CARS
Liability1
3
2
Data privacy
Expense
177
145
121
113
72
65
64
54
29
Liability Expense Data privacy Control Safety
Environment Nothing Other Congestion
Question: In your opinion, what are the 3 greatest advantages of driving?
Convenience1
3
2
Multitasking
Safety
Fig. 28: ANTICIPATED FEATURES OF
SELF-DRIVING CARS
196
190
185
157
136
45
35
7
Convenience Safety Multitasking Mobility
Congestion Environment Speed Other
196
190
185
157
136
45
35
7
Convenience Safety Multitasking Mobility
Congestion Environment Speed Other
33
Driving into the Future
for drinking without having to bother with desig-
nating transportation, one comment brought the
human component back into transportation: “I
will worry less for my alcoholic girlfriend.” A dif-
ferent kind of increased mobility was highlighted
by one commenter who wrote, “I’m legally blind
and, to quote the Oatmeal, ‘I, for one, welcome
our new adorable Skynet Marshmallow Bumper
Bot Overlords!’” (From Inman 2014).
The most common comment revolved around
the pleasure of driving. Several commenters
generally expressed their preference for driving,
while others specifically noted that self-driving
cars will remove a valuable part of American cul-
ture. The below comment reflects the sentiments
in many similar comments:
Comments expressing general discomfort (such
as: “It’s TOO SPOOKY.”) were rare. A full list of
comments can be viewed in Appendix B.
more driving. The smallest concern was conges-
tion (Fig. 27).
Not surprisingly, convenience topped the list of
anticipated features. Because the survey partic-
ipants rated convenience features so highly for
driving in general and because self-driving cars
will likely increase convenience, this feature will
be highly marketable to the populations this sur-
vey represent. Closely behind convenience are
safety and ability to multitask. It is significant that
safety is seen as both a concern and a feature,
perhaps because of the complexity of safety.
Whereas human-error-caused incidents may de-
crease, computer-caused and other unforeseen
issues may create a different kind of danger. The
importance of multitasking begins to confirm the
emerging belief that driving is now the distrac-
tion. Mobility and congestion were a close fourth
and fifth place with environment and speed only
gaining 13% and 10% of the votes as top features
(Fig. 28).
Participants provided a wide variety of com-
ments in the concerns, features, and open-end-
ed final question. Several comments revolved
around safety and the increased dangers with
self-driving cars. Commenters highlighted insecu-
rities with hacking, failure to respond creatively
or appropriately in an emergency, and problems
with mixing human drivers with self-driving cars on
the roads. One commenter summed up some of
these feelings by stating, “The vehicle lacks com-
passion.” Other comments focused on the econ-
omy, either worrying about lost jobs or praising
the efficiencies gained. A few comments high-
lighted issues in land use and mobility, expressing
concerns over increased suburban sprawl or an-
ticipating the walkability benefits to more pedes-
trian-responsive streets. Alcohol was a common
theme in additional anticipated features. While
most such comments discussed the advantages
I don’t drive a car because it’s an ap-
pliance that moves me from point A to
point B. I drive a car because I like it. I like
personalizing and customizing and turn-
ing it into both a status symbol and a re-
flection of who I am. It’s my community,
my hobby, I meet friends through it, it’s a
sport, it’s an art. All of this is lost in a SDC.
34
Driving into the Future
Analysis Fig. 29: Familiarity and Willingness
How does a person’s familiarity with self-driving car research and
development influence her or her willingness to ride in a self-driv-
ing car?
Very familiar
Somewhat or not
familiar
Willing Not willing
36%
15%
2%
47%
In general, participants who were very familiar
with self-driving car research and development
were willing to ride in a self-driving car. Even par-
ticipants who were somewhat or less familiar with
the technology were generally willing to expe-
rience a self-driving car ride (Fig. 29). Because
of this general openness to self-driving cars, the
more dynamic and interesting analysis comes
from the different ways in which participants
were interested in integrating the transportation
technology into their lives. In this section, statis-
tical analysis revolves around commuting and
shared mobility travel behaviors as primary pillars
of personal transportation. The coding index for
statistical analysis and full statistical results tables
can be found in Appendix C and D, respectively.
Commuting with Self-Driving Cars
Because commuting is central to travel behav-
ior, determining interest in using self-driving cars
to commute is important to gauging the future
of transportation. Given how willing participants
were to ride in a self-driving car in general, it is a
bit surprising that they showed more reservation
when expressing their interest in commuting with
self-driving cars. Only a quarter of participants
were interested in always using self-driving cars
to commute with slightly more interested in only
sometimes using them to commute. The largest
percentage were either rarely or never interest-
ed in letting cars drive them to work.
Using binary logistic regression with confidence
set at 95%, several variables were found to
be statistically significant with interest in using
self-driving cars to commute. Not surprisingly,
people who drive to work were nearly ten times
more likely and people who have long com-
mutes were about six times more likely to be in-
terested in having self-driving cars taking over
their commute. For the drivers, self-driving cars
present a fairly seamless mode transition, and
for people with long commutes, self-driving cars
could provide some relief. Also not unexpected,
people who would consider living farther away
from work with self-driving cars tended to want
to use self-driving cars for that longer commute.
Significantly, participants who showed an inter-
est in using self-driving rideshare services were
more likely to be interested in self-driving com-
muting, indicating a possible future of carshar-
ing being used for commuting, which is current-
ly an underutilized use of carsharing (Costain et
al. 2012; Ciari 2010). Non-white participants (of
which there were 64) were also more likely to be
interested in self-driving commuting, a trend that
requires further study. (See Fig. 30.)
Living Farther Away From Work with Self-Driving
Cars
One of the most central concerns with self-driv-
ing cars from an urban planning perspective is
the possibility that the technology could encour-
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Thesis - Driving into the Future

  • 1. Melissa Ruhl May 2015 San Jose State University Master’s Planning Report Travel behavior changes with self-driving cars Driving Future into the
  • 2. DRIVING INTO THE FUTURE: TRAVEL BEHAVIOR CHANGES WITH SELF-DRIVING CARS A Planning Report Presented to The Faculty of the Department of Urban and Regional Planning San José State University In Partial Fulfillment Of the Requirements for the Degree Master of Urban Planning By Melissa Lentini Ruhl May 2015
  • 3. ii Driving into the Future Acknowledgments As a young adult who, like so many of my peers, struggled to discover a career in the depth of the Great Recession, I am both humbled and grateful to have received such dedicated support from the faculty and students of this program. I could not have hoped for a better advisor in Professor Hilary Nixon. She was open, eager, strategically critical, and responsive, even meeting with me over Spring Vacation to explore project ideas with me. She is a career model for me as someone who dives in to learn, grow, and lead. Like Hilary, the other professors in this program always went the extra mile to encourage excellence in their students. I am grateful to Professor Shishir Mathur for meeting with me numerous times to guide me in statistical analysis and for teaching me to think openly and critically about the growth and devel- opment of urban landscapes. Professors Rick Kos and Asha Agrawal expected rigor and excellence, motivating me to always aim higher. The affiliated faculty at SJSU represent the highest caliber of pro- fessional achievement, all with many years of theory and practice to pass on. I am especially grateful to Laurel Prevetti, Richard Lee, Joseph Kott, Hing Wong, Charles Rivasplata, and Eduardo Serafin for teaching both cutting edge and deeply tested best practices. For guidance on my specific topic, I am thankful to the Mineta Transportation Institute for sponsoring me to attend the TransOvation Conference and Workshop on autonomous vehicles and to the nu- merous organizations across the Bay Area who offered student rates at luncheons, workshops, and conferences. Thank you to my peers with whom I hope to be colleagues and friends for years to come. I am grateful especially to Adam Paranial, Audrey Shiramizu, Beth Martin, Ceci Lavelle Conley, and Mark Young for talking with me about the future of transportation and the development of urban technology, always keeping sustainability and progress at the forefront of conversations. Most of all, I am grateful to my husband, Chris Lentini, for supporting me, laughing with me, digging deep into conversations with me, relaxing with me, working with me, and believing in me. I could not have dreamed of a more perfect partner. Cheers to yet another level up on our long journey together.
  • 4. iii Driving into the Future Contents List of Figures and Tables Abstract Introduction The Open Road Ahead Cooperation and Control: Defining Automation Increased Sprawl or Shared Mobility: A Range of Possibilities Finding Precedents for Self-Driving Cars A New Way of Driving: Carsharing A New Type of Vehicle: Electric Vehicles Do People Want Automation?: Vehicle Automation Conditions Tests Self-Driving Cars Market Adoption Survey Distribution Methodology Results Analysis Discussion Survey Limitations Conclusion and Recommendations Summary Recommendations Appendices A: Survey Questionnaire and Results B: Open-ended Questions Responses C: Coding Index for Statistical Analysis D: Statistical Results Tables E: Survey Distribution Avenues Reference List iv v 2 4 4 6 10 11 14 16 22 22 23 34 39 40 42 42 42 44 60 66 76 84 88
  • 5. iv Driving into the Future List of Figures and Tables Figure 1: Age Figure 2: Ethnicity Figure 3: Race Figure 4: Gender Figure 5: Employment Figure 6: Income Figure 7: Education Figure 8: Political Affiliation Figure 9: Community Type Figure 10: Housing Status Figure 11: Car Ownership Figure 12: Commute Mode Figure 13: Commute Time Figure 14: Electric Vehicles Figure 15: Shared Mobility Figure 16: Car Ownership Importance Figure 17: Advantages of Driving Figure 18: Disadvantages of Driving Figure 19: Familiarity Figure 20: Willingness to Ride Figure 21: Self-Driving Rideshare: Use Frequncy Figure 22: Self-Driving Rideshare: Shared Rides Figure 23: Self-Driving Rideshare: Car Onwership Figure 24: Self-Driving Commute Figure 25: Extending Commute Distance with Self-Driving Cars Figure 26: Vehicle Occupants in Self-Driving Cars Figure 27: Concerns with Self-Driving Cars Figure 28: Anticipated Features of Self-Driving Cars Figure 29: Familiarity and Willingness Figure 30: What factors influence a participant’s interest in using self-driving cars to commute? Figure 31: What factors influence a participant’s interest in living farther away from work with self-driving cars? Figure 32: What factors influence a participant’s interest in using self-driving rideshare services frequently? Figure 33: What factors influence a participant’s interest in still owning a car with self-driving rideshare services available? Figure 34: What factors influence a participant’s willingness to share a self-driving ride- share ride with others for a reduced fare? Table A: Commuting and Self-Driving Cars Table B: Living Farther Away from Work with Self-Driving Cars Table C: Self-Driving Rideshare Use Frequency Table D: Self-Driving Rideshare and Vehicle Ownership Table E: Self-Driving Rideshare and Shared Rides 23 23 23 24 24 24 25 25 25 25 26 26 26 27 27 27 27 27 28 28 29 29 29 30 30 31 32 33 34 35 36 37 38 76 78 79 81 82 38
  • 6. v Driving into the Future Abstract Self-driving cars could completely transform travel behaviors in the United States. Though we are finally starting to see a reduction in vehicle miles traveled and a growing popularity of multimodal transpor- tation, self-driving cars could reenergize interest in driving. They also have the potential, however, to make car ownership truly unnecessary. In this report, results are presented from a 334-participant online survey of people living in the United States. The survey focused on travel behaviors with self-driving cars. The vast majority of participants (83%) stated that they were willing to ride in a self-driving car. Partici- pants were largely open to using a shared self-driving service frequently (43% always or often), but they were more mixed on using self-driving cars to commute. A clear majority (69%) were uninterested in living farther away from work with self-driving cars. In general, three categories of participants showed interested statistical significance in their decision-making. People who valued car ownership highly were more likely to want to live farther away from work with self-driving cars and to want to continue owning a car with shared self-driving services available. People who had longer commutes were more likely to want to use self-driving cars to commute, but less likely to want to live farther away from work with self-driving cars. Third, people who had experience with or interest in current shared mobility ser- vices were more likely to be interested in using shared self-driving services often and were more likely to be willing to carpool in shared self-driving cars for a lower fare. Numerous policy recommendations can be derived from these findings. Most prominently, planners should focus on shared mobility, travel demand management, and growth management policies.
  • 7. 2 Driving into the Future The automobile dominates transportation in the United States. For a population of 308 million, we have 254 million automobiles (BTS 2014a). Driving has become a way of life for Americans with a full 86% of commuters driving to work each day (BTS 2011). From 1970 to 2010, the population grew by 52%, but the total annual vehicle miles traveled (VMT) exploded by 165% (FHWA 2014; United States Census Bureau 2014). Though the car has facilitated levels of mobility and acces- sibility previously unknown, the disadvantages in time, land, and environment of auto-depen- dence have put into question the advantages. We dedicate 74.9 minutes to drive 38.4 miles per day, and the average annual hours spent in con- gested traffic conditions has more than doubled from 16 hours in 1982 to 38 hours in 2011 (Krumm 2012; Schrank et al. 2012). Beyond the impacts of car use on our day-to-day lives, the green- house gas (GHG) emissions created from driving will continue to affect the environment for gen- erations. The US is second only to China in GHG emissions of which transportation accounts for 27% (CDIAC 2014; EPA 2013). Yet the dominance of the automobile may have peaked. Alternatives transportation modes, such as bicycling, walking, and riding transit have in- creased in popularity (BTS 2014b; McKenzie 2014). For example, the percentage of trips made by walking doubled from 1995 to 2009 (BTS 2014a). Vehicle miles traveled has seen a decade-long and likely permanent decrease (Short 2015). Most significantly, teenagers and young adults are showing less interest in driving or even in ob- taining a driver’s license, an achievement that was once seen as a rite of passage (NHTSA 2012; Davis et al. 2012). These trends away from the car could reverse with the market introduction of self-driving cars. Defined by the National Highway Traffic Safety Administration (2013) as vehicles that can oper- ate without direct human driver input, self-driv- ing cars could introduce a new way of traveling that is safer, more comfortable, and less stressful. Such a shift in quality of car travel could have enormous consequences for the transportation system as a whole. While self-driving cars could introduce an ar- ray of travel behavior changes, two possible extremes have been discussed in the literature and the media. At one extreme, self-driving cars could increase suburban sprawl beyond our wildest imagination of limitations. If commut- ers could sleep in their cars, get ready in their cars, and begin working in their cars, just how far away from their workplace would they care to live? At the other extreme, car ownership could fade into near non-existence as travelers prefer to call a self-driving taxi when needed, facilitat- ing a more compact way of living with far fewer parking spots bracketing our communities. It is also possible that the transformations introduced with self-driving cars are beyond what we could imagine at this point. The transportation technol- ogy could be as transformative to our mobility as the Internet has been to communication. This report presents results from a survey on trav- el behaviors with self-driving cars. It begins with an overview of self-driving cars, including a de- tailed definition and a review of research mod- eling the possible use of the technology. Next, a review of literature on how people use cars gives CHAPTER 1: Introduction
  • 8. 3 Driving into the Future some precedence for the adoption of self-driv- ing cars. Third, the report presents findings for the 334-person online survey conducted. The report concludes with a discussion of the implications of the survey findings, including three policy recom- mendations.
  • 9. 4 Driving into the Future Cooperation and Control: Defining Automation There are many words being used today to de- scribe this type of emerging vehicle. Autono- mous vehicles, automated cars, self-driving cars, and driverless cars are all terms used to describe what is often assumed to be the same thing. To complicate the issue even further, there is often confusion between connected and autono- mous cars. Are these vehicles robots moving in- dependently together? Or are they more akin to cell phones, which derive their functionality from their connectivity? The basic components of today’s automated vehicle technology include computer vision that is supplied by radar, cameras, and lidar (a de- vice like radar that uses light rather than elec- tromagnetic waves to detect and reconstruct the surrounding environment), global positioning systems (GPS) and mapping software, and me- chanical parts that have been integrated with the software (Chatham 2013). None of these technologies require the vehicle to be connect- ed or communicative with other cars (V2V) or with the roadway infrastructure (V2I), though such connectedness would facilitate a safer driving experience (Milanés et al. 2014). The differences between automated and auton- omous is a matter of degree. The National High- way Traffic Safety Administration (NHTSA) defines automotive automation as the machine coordi- nation of or more safety critical vehicle functions, such as throttle, braking, shifting, and steering. Automation progresses on a Level 0-4 system with Level 0 being no automation and Level 4 being complete automation (NHTSA 2013). Nearly all cars on the road today are at either Level 0 or 1 automation. Level 0 automation means that at all times a human driver must be in full control of all safety critical functions of the vehicle. At Level 1 automation, the car can control one safety critical function. Technically, we’ve had Level 1 automation market penetra- tion since the 1980s with the introduction of auto- matic transmissions. But because shift control has become so normal and even assumed, with 90% market penetration in the US (Litman 2014), con- versations about automation rarely include au- tomatic transmissions. More commonly, Level 1 automation functions include control over steer- ing, braking, or accelerating. Examples of Level 1 automation for those three functions include lane keeping – sensors on the car detect when the driver is veering towards lane lines and au- tocorrects; automatic braking – the brakes acti- vate when the human driver is not applying the required pressure in an emergency; and cruise control – the throttle adjusts pressure according to a preset speed. Cruise control has been an available vehicle enhancement since before automatic transmissions, and many drivers today are used to the idea that cars can handle speed regulation in road-trip environments, a small but important baby-step towards more fully auto- mated vehicles. Newer Level 1 capabilities, such as lane keeping and automatic braking, are features on a range cars, from more economical cars such as the Ford Fusion to luxury cars such as the Mercedes S-Class. CHAPTER 2: The Open Road Ahead
  • 10. 5 Driving into the Future At Level 2 automation, two automated safe- ty critical functions coordinate together. The clearest Level 2 example is a combination of lane keeping and adaptive cruise control — a smarter cruise control in which front-facing sen- sors detect slowed traffic. A driver in such a situ- ation could allow the computer to control speed and direction, but the driver would still have to watch the road for emergencies or for direction- al changes. Level 3 automation is the first level of autono- mous driving. The Level 3 vehicle can coordinate full control of driving in specified situations. For example, on the highway, the car could coor- dinate speed regulation with lane keeping and also watching for and responding to unpredict- able changes in the state of the road. Level 3 automation is the first level at which the driver can cede attention from driving to focus on oth- er tasks. Once the specified situation changes, however, the driver has to resume control. For ex- ample, if a Level 3 vehicle were in control on the highway, the driver would have to be in control on highway ramps and within the city. Level 4 is the only truly autonomous vehicle. At this level, a “driver” becomes a supervisor or merely an occupant. In Level 4 automation, no occupant of a vehicle could be capable of driving in the sense that we know it today. The occupants could be elderly, disabled, children, intoxicated, or highly distracted. In fact, Level 4 does not require a human to be in the vehicle at all. There are no passenger vehicles available for public use today that are at Level 4 automation, but many vehicle and equipment manufactur- ers, software companies, and startup aftermar- ket products companies are testing automotive automation from Levels 2 all the way to 4. An important note regarding terminology in this report is necessary. In the popular media, the terms “self-driving car” and “driverless car” are most commonly used to describe this vehicle technology. Both terms are problematic. The term “self-driving car” has been popularized by the Google Self-Driving Car, even though the autonomous technology Google uses and de- velops is similarly being used and developed by numerous companies. “Driverless car” is simply inaccurate. Even with a fully autonomous vehi- cle, the software and hardware components still “drive” the vehicle and for the foreseeable fu- ture, all such vehicles will have override features to, at the very least, bring the vehicle to a full stop (Iozzio 2014). A more accurate terminology palette uses: • “automated” to describe Levels 1, 2, or 3 ve- hicles, • “fully automated” to describe Level 4 vehi- cles, • “autonomous” to differentiate fully automat- ed vehicles that are or are not connected, • and “automated vehicle technology” to de- scribe the field itself as well as aftermarket hardware and software packages that can be added to existing vehicles to provide au- tomated features. Nevertheless, in this report, “self-driving cars” is the primary term used. Because “self-driving car” is a more common and digestible term than “au- tonomous vehicle”, it was the term “self-driving car” and not “fully automated vehicle” or “au- tonomous vehicle” that was used in this report’s survey. And because “self-driving car” was the term used in the survey, it is also primarily used in this report describing the survey.
  • 11. 6 Driving into the Future Increased Sprawl or Shared Mobility: A Range of Possi- bilities The development of and hype around self-driving cars alarms planners, urbanists, and environmen- talists who have watched the rise of the car and the demise of the central city. While in their first two decades, travelers used cars as recreational luxuries, as bonuses to add to their transportation lives, they eventually became more affordable and accessible to wider segments of the popu- lation. As cars grew in popularity, so cities and regions laid the infrastructure to support them. But as the infrastructure grew, as more roadways were built and expanded, communities dissipat- ed through the long web of the highway system. Over a few short decades, what had began as an investment in bringing communities togeth- er became a tool to wedge them apart. (See Hayden 2003 or Glaeser 2011.) The story of the urban disinvestment that fol- lowed in the tracks of the car is familiar to trans- portation professionals. Much of planning in the late Twentieth and early Twenty-first Century has focused on how to bring communities back to- gether and, more generally, how to recover from the car. These and other efforts have paid off as driving is becoming slowly less popular with each new generation. Could automated vehicle tech- nology reverse such progress? Automotive automation could make driving as we know it today more appealing by removing some of the most stressful components of driv- ing. Automating highway driving in particular is a low-hanging fruit for many vehicle automa- tion developers. Tesla, for example, announced a much-hyped new vehicle that would be 90% autonomous (Ackerman 2014), or autonomous for highway driving, which accounts for much of total vehicle miles traveled. In highway con- gestion, drivers of such vehicles could largely re- move their attention from the road, knowing that their car would deal with the necessary stop and go movement. In highway driving without con- gestion, these vehicles could also relieve drivers of the slight and continuous throttle and steering adjustments of highway driving. If such cars are allowed in dedicated lanes, such as high-occu- pancy toll lanes, highway driving could be even easier and more free-flowing for owners of such vehicles (Keefe 2014). Overall, it is possible that highways could become more efficient, allow- ing more vehicles in a more compact space and enabling a greater number of cars to traverse highways faster (Bierstedt et al. 2014; Shladover 2012). If highway driving becomes so easy and stress-free, drivers could be incentivized to live farther away from their places of work, encour- aging the low-density development that has dis- sipated cities and threatened arable and sensi- tive environments. On the other hand, the self-driving car could be revolutionary. What Uber has done to the taxi industry, autonomous cars could do to the car industry as we know it today. Instead of purchas- ing and parking cars, app-based transportation services could usurp and nullify the vehicle own- ership model. With such a service, a car could ar- rive at your door each time you wished to leave and take you to your destination, leaving for its next customer afterwards. Rather than the car being parked all day while its owner goes about her or his day, the car could be servicing other passengers. With such a service, payment could be handled through the app, and the fee could include fare and taxes. Payment could also be “gamified” to encourage sustainable use. For example, a rider could earn a bonus or fare re- duction by sharing the ride with passengers go-
  • 12. 7 Driving into the Future ing in similar directions, by being willing to deliver freight on route, or by opting for a smaller or more efficient vehicle. Fees could also be reduced or nullified for individuals receiving government sub- sidies. Cars not being used for passenger trans- port could recharge, complete freight deliveries, or be of use for necessary but not time-sensitive transportation. Repair stations could service vehi- cles that are damaged or worn down. Since the cars would be used more continuously, the fleet would turn over faster than it does today, pro- viding economic incentives for manufacturers to provide such services over the purchasing model and also encouraging more environmentally in- novative vehicles to be on the road sooner. The possibilities for revolutionary change with such a service are endless. Literature is beginning to flourish that unfolds the mechanics of such a system. The first and most influential study was conducted by the Earth Institute of Columbia University by a team of researchers led by former General Motors R&D director, Lawrence Burns (2013). The research- ers modeled the total number of shared auton- omous vehicles (SAVs) required to achieve the same mobility of traditional, privately owned ve- hicles. They performed this analysis in three differ- ent scenario regions: a typical suburban-urban commute community (Ann Arbor, Michigan), a transit-inefficient small town (Babcock Ranch, Florida), and a dense, transit-rich city in which taxis are the predominant form of passenger ve- hicle travel (New York City, New York). The most significant time and cost efficiency came from the model scenario of Ann Arbor. While achieving the same mobility level, the re- searchers found the total number of traditional, privately owned vehicles could be reduced by 85% - from 120,000 traditional, privately owned vehicles to 18,000 SAVs. In their modeling, the SAVs are in use 75-80% of the time on a given weekday. This range compares with the average usage rate of traditional, privately owned of 5% or less. The modeled Babcock Ranch and New York City saw less substantial efficiency gains with SAVs. Babcock Ranch is a planned city in Flori- da that has been built as a “living laboratory” of smart and efficient technologies integrated with- in a community. It is likely that Babcock Ranch is more intelligently designed than most small towns, allowing for better designed and placed transportation systems. Nevertheless, it is a small town based on low density development and ac- cess to open spaces that make destinations less accessible to each other. At full build out, Bab- cock Ranch is expected to have a population of 50,000. If this town’s population has the average number of vehicles per person as the US general- ly, there will be 42,100 privately owned vehicles in the community. As in the Ann Arbor case study, the researchers made a series of informed as- sumptions regarding average trip total per day, average trip length, and average speed. They found that 4,000 vehicles would supply mobility to the residents of Babcock Ranch with total wait times not exceeding a minute, but that each trip would be somewhat more expensive at an aver- age of $2 per trip (Burns et al. 2013). The third modeling the researchers conducted was replacing taxi cabs in New York City with SAVs. Within the five boroughs, 53,000 taxi cabs and for-hire vehicles provide 410,000 trips per day. They found that an average 9,000 SAVs could provide rides with wait times under a min- ute. They also found that imbalances between trip origins and destinations would entail that 11% of the average trip would be empty of passen- gers. These SAVs could be dramatically cheaper at an average of $1 per trip compared to the
  • 13. 8 Driving into the Future current average of $7.80 per trip in a taxi cab. As with current taxis, there would likely be a con- siderable imbalance between cars necessary for peak period and cars available during off peak hours. Despite some differences in cost savings and relative efficiency, all three modeling scenarios produced far superior results for a more sustain- able future. Fewer cars would be needed, creat- ing fewer emissions and consuming far few park- ing spaces (Burns et al. 2013). In this first modeling study, therefore, the promise of SAVs was found to be great. Other studies have continued to build off of the Transforming Personal Mobility study. For exam- ple, Fagnant and Kockelman (2014) similarly modeled the possible car use changes with a generated model of a fictional city similar to Aus- tin, Texas. The researchers ran the model for one hundred days to ensure greater confidence in their results. Their modeling showed SAVs to add an average of 10% more travel distance than traditional cars, but that a more than 90% reduc- tion in total passenger vehicles could provide the same level of mobility. In other words, though individual vehicle miles traveled (VMT) would increase, the cumulative effect would still be a reduction in VMT, given the fewer total number of cars on the road. The model produced similar benefits for parking reductions. They found that for every SAV, eleven parking spaces throughout the city could be eliminated. If a few thousand SAVs could provide mobility for an entire city, tens of thousands of parking spaces could be de- veloped into other, more beneficial uses. Further, SAVs could reduce emissions, not only in reduc- ing the total number of vehicles, but also signifi- cantly reducing the number of cold starts when vehicles produce the most emissions. Overall, Fagnant and Kockelman found that SAVs could bring drastic environmental and land use gains to a city. The possible range of behavior changes resulting from the development of self-driving cars could be as great as is the range of automotive auto- mation development is itself. While vehicles avail- able for public use are currently only at automa- tion Levels 0, 1, or 2, Level 3 vehicles and even Level 4 vehicles will likely be on the market soon. With Level 4 automation, we could see a dra- matic range of travel behavior changes from the unprecedented expansion of suburban sprawl and the maximum acceptable commute to the abandonment of the vehicle ownership model in favor of shared autonomous vehicle fleets. Such a range of possibilities makes planning difficult, to make an understatement. The next chapter provides a detailed literature review of emerg- ing vehicle technologies and how people have responded to those technologies. With an under- standing of our past, we may more intelligently plan for the future.
  • 15. 10 Driving into the Future From one perspective, there is no precedent for self-driving cars. This technology could effective- ly nullify the time cost of transportation, allowing passengers to continue their lives relatively unin- terrupted while moving from one place to anoth- er. The time cost of travel could drop significantly. Yet self-driving cars are emerging from a long his- tory of innovations in transportation. From steam engines to motor boats and from cargo trains to high speed rail, human societies have continu- ally sought for greater efficiency in movement. But just because self-driving cars may be more efficient does not mean they will automatical- ly be adopted into popular use. The automatic transmission created a more efficient driving ex- perience, for example, yet it took decades to be widely adopted in the US and still has not gained majority use in many European nations (Litman 2014). Technological progress and use is never a given, especially when it concerns a technology and intimate as transportation. If the use of more efficient transportation tech- nologies is not a given, how can planners and policymakers plan for emerging trends? Even as- suming the technical development and market release of self-driving cars proceeds as predict- ed (Ro 2014) — a large assumption — it is not a given that people will be enthusiastic about the emerging products. Autonomous cars could, for example, fail spectacularly on the market, such as the highly publicized Segway (Rivlin 2007). Gauging market interest now could shine some light on a likely range of adoption paths. Because of the speculative nature of self-driving car adoption, determining how best to gauge market interest is far from clear. Three major genres of literature shed light on how self-driv- ing vehicle adoption may unfold. First, self-driv- ing cars may present a shift in how travelers view cars as transportation modes. Carsharing, an alternative model of vehicle ownership that has gained momentum in Europe and North America, allows urban residents to subscribe to a car service. How subscribers have used and responded to carsharing may reveal changing perceptions towards innovations in car owner- ship. Second, the gained market share of elec- tric vehicles may serve as a recent movement in the automotive market that shows consumers are willing to try new technologies, even when issues such as range anxiety continue in the pop- ular conversation. The third major genre of literature that could serve as a guide for gauging market adoption is the collection of studies analyzing behaviors and at- titudes towards automotive automation. Studies are being conducted that analyze participants’ implicit trust in and response to automation by understanding their use of automated features, such as parallel parking assist. Simulation studies have also allowed participants to elect to drive manually or autonomously, noting their behav- iors when choosing autonomous driving. Further, a growing body of literature is analyzing individ- uals’ explicit trust in and response to automation, including automotive automation, through sur- veys and interviews. Together, the two methods for understanding use of and orientation towards automation create a more detailed landscape for determining potential market adoption of CHAPTER 2: Finding Precedents for Self-Driving Cars
  • 16. 11 Driving into the Future self-driving cars. A New Way of Driving: Car- sharing First introduced in Europe in the 1980s as an al- ternative to privately owned cars sitting unused most of the day, carsharing has grown into a global movement. Most early European car- sharing organizations started as publicly funded services that offered vehicles on a pay-per-use system. Beginning with two organizations in Swit- zerland and Germany, carsharing services now have hundreds of thousands of members across Europe (Shaheen et al. 1998). Though carsharing has caught on slower in North America, due in large part to the lower density pattern of devel- opment, it has continued to grow in populari- ty, particularly in the last decade. In the United States, carsharing services tend to operate on a subscription and pay-per-use system in which members can access either a neighborhood car or cars from a specific company. Usually, these cars have dedicated parking spaces where the vehicle is both picked up and returned (Shaheen et al. 2009). Some one-way carshare services have burgeoned in Europe with slower adoption in North America. In these services, shared cars are located by app rather than parking space and the car is only paid for while the user drives it (Firnkorn and Müller 2011). With carsharing, drivers can have the greater access and mobility pas- senger vehicles afford without being burdened by the cost and responsibility of car ownership. The People of Carsharing Understanding the types of people who are in- terested in or who use carsharing services can shed light on why carsharing fails or succeeds. Income is by far the most studied demographic characteristic of carsharing users with some con- flicting results. Two surveys showed people with higher incomes are more likely to be interested in carsharing. An intercept survey of 840 Beijing residents found that higher income respondents were generally more interested in carsharing than lower income respondents, but that the difference was minimal (Shaheen and Martin 2006). Similarly, an online study of 1,340 current carshare users in North America found that most participants were middle or upper income, but in the United States, more carshare users made less than $10,000 a year than statistically average for the larger population. In other words, carshar- ing populations skewed towards higher incomes and very low incomes. In both studies, the great- er number of higher income individuals showing an interest in carsharing was only slightly statisti- cally significant. Other studies have shown lower income individ- uals to be generally more likely to use carshar- ing services, though these studies are also not without caveats to their findings. An in depth analysis of three years worth of carsharing data in Toronto found that members in lower income neighborhoods are more active and active for longer than members in higher income neigh- borhoods (Costain et al. 2012). Though specific income data was not available for these car- sharing members, the difference in low income versus high income neighborhood use data for members showed that members more likely to be low income used carsharing much more actively. Likewise, an online stated preference survey of Greek residents found that the major- ity of individuals interested in carsharing were lower income (Efthymiou et al. 2013). Another stated preference survey analyzing interest in carpooling clubs likewise found lower income re- spondents were more interested in coordinated carpooling (Correia and Viegas 2011). Though a
  • 17. 12 Driving into the Future study of cost rather than income, an intercept survey of 500 people in Palermo, Italy found that as the cost per kilometer of carsharing decreas- es, carsharing becomes more appealing to indi- viduals driving alone, carpooling, or taking transit (Catalano et al. 2008). The data are not conclusive enough to state general trends in the incomes of carsharing members. One possible explanation for this in- consistency could be that members of different income groups use shared cars differently. A study of carshare availability and reservations in Montreal analyzed a statistically significant yet unclear relationship between income and car- share use. Comparing neighborhoods based on income in which there is a carshare station, av- erage monthly car reservation time decreased as income increased. Specifically, every $1000 increase in average income correlated with an average decrease in more than 30 minutes that cars were reserved every month. Yet the incomes reported by members in an internal carsharing service survey tended to be higher than the me- dian income. Even though members tended to have higher incomes, the rates of actual use may have been greater in lower income neigh- borhoods (de Lorimier and El-Geneidy 2010). More studies on the relationship between mem- ber income and use activity is necessary to fully understand why higher and lower income popu- lations are differently drawn to carsharing. Likely the most consistent demographic charac- teristic of carshare users is that they tend to be young adults. Burkhardt and Millard-Ball (2006) found in an online survey of 1,340 participants that individuals interested in carsharing tended to be in their 20s and 30s. In Shaheen and Mar- tin’s study (2006) of 840 Chinese residents, more younger survey participants than older survey participants were interested in carsharing, with those who were both familiar with and interest- ed in carsharing particularly likely to be younger. Correia and Viegas (2011) found in an online sur- vey of 996 residents in Lisbon, Spain that young- er adults are more willing to try a carpool club than older adults. Even in a study that specifically focused on young people’s views of carsharing, the younger cohort was found to be more will- ing to carshare. Efthymiou et al. (2013) surveyed 233 Greek residents aged 18-35 and found that though respondents aged 26-35 are less satisfied with their travel patterns than younger respon- dents, they are less willing to join a carsharing service than their younger counterparts. Household size could be another significant indi- cator of willingness to carshare, since the lesser consistency and predictability of a communal car could be easier to smaller households to handle. In particular, families may be overly in- convenienced enough to not use carsharing. Unfortunately, no search revealed studies that have found whether households with young children are more likely to carshare. Burkhardt and Millard-Ball (2006) found in their online sur- vey of 1,340 individuals that people living in small households were more likely to be interested in carsharing. This data likely indicates that these households did not having young children living at home, though some of the two person house- holds could have included a child. Knowing household size could elucidate or complicate the issue of age. Are young adults generally more likely to use carsharing because they do not have young children to care for and chaperone around town? Is there any statistical correlation between families and those not interested in car- sharing? If the complexities of carsharing make the transportation mode less appealing to adults with children, such data could explain at least in part the continued minority status of carsharing as a viable and popular mode of transportation.
  • 18. 13 Driving into the Future The Travel Patterns of Carsharing Members If urban planners and researchers benefit from knowing who are likely users of carsharing, an even more important question is how carshare users actually use their shared cars. When and for what purposes do members use carsharing services? Will carshare users shift from using tran- sit or shift from using a privately owned vehicle? Finally, how do users perceive transportation in general? In other words, what general percep- tions motivate an individual to become a car- share user? At this stage in the market adoption of carsharing services, travel patterns reveal that though carsharing is a progressive and increas- ingly common idea, it is by no means replacing the privately owned vehicle as a staple of trans- portation. Research indicates that carsharing tends to be a supplemental rather than a primary mode of transportation for most users. The 6,000 active AutoShare users in Toronto tend to use the ser- vice on the weekend and with diminishing fre- quency towards the beginning of the week, indi- cating a reliance on other modes for the majority of weekday travel. During the week, most trips are made during the late morning hours, which could indicate that users have a non-tradition- al work schedule (Costain et al. 2012). Apply- ing carsharing data in Zurich, Switzerland to a combined agent- and activity-based modeling software produced the most common carshare use scenarios to be trips from home to a leisurely activity, trips from home to shopping, and trips from home to work (Ciari 2010). Most significant- ly, carshare users in North America tend to travel with a shared car only once or twice a month (Burkhardt and Millard-Ball 2006), revealing that a different mode of transportation is used the majority of the month. The most discouraging data regarding the viabil- ity of carsharing comes from low user retention rates. Since most carsharing services are for-prof- it companies, complete user and usage data is rarely obtained. In one study, however, Toron- to-based service AutoShare allowed research- ers access to full membership lists and reserva- tion transactions for nearly three years. With 200 parking locations and over 6,000 active mem- bers, AutoShare data provides a thorough case study for what would appear to be a successful carshare model. But if active usage is fairly wide (defined quite liberally as a single use or more over the past year), low retention presents a sig- nificant problem to the success of the service. Af- ter the first year, 60% of AutoShare members be- come inactive. After three years of membership, 75% of members are inactive. For the majority of AutoShare members, therefore, carsharing is not becoming a habit (Costain et al. 2012). On the surface, carsharing seems to hurt rather than help transit. Two surveys found that transit users were more likely than drivers to be interest- ed in carsharing (Efthymiou et al. 2013; Shaheen and Martin 2006). What is unclear from these sur- veys is whether these current transit riders would use carsharing in tandem with transit — using carsharing to connect to transit or relying on car- sharing when transit is not available — or whether they would use carsharing as a more affordable or more sustainable alternative to owning a car. In other words, while it is possible these individuals would stop riding transit with carshare, it is also possible they would use carsharing to support their transit travel. In the Beijing study referenced above (Shaheen and Martin 2006), one inter- esting finding could provide hope to emerging economies struggling with greater numbers of transit riders switching to driving on already con- gested roads. Though more Beijing transit users than drivers were interested in carsharing, tran-
  • 19. 14 Driving into the Future sit users who were interested in purchasing a car were twice as likely to be interested in carsharing than drivers who are interested in purchasing a car. In other words, rather than adding private- ly owned vehicles to the road, new drivers were looking to shared cars as a viable alternative. The Future of Carsharing Even if carsharing remains a service that attracts a fraction of the number of people as does pri- vately owned vehicles, the attitudes that have allowed carsharing to thrive at all could indi- cate a changing transportation landscape. In a survey of North American carshare users, Bur- khardt and Millard-Ball (2006) found that partic- ipants see transportation pragmatically. People who opt for carsharing do not like the inconve- niences of car ownership. Similarly, when asked about the advantages of owning a car, Beijing residents rated most highly travel convenience, followed by travel comfort, and greater mobili- ty. The disadvantages of car ownership included parking difficulties, pollution, and cost. For those who did not already own a car, the two great- est deterrents to purchasing a vehicle included cost, transit convenience, and parking difficul- ties (Shaheen and Martin 2006). If carsharing is affordable and supports transit in delivering the most convenient and comfortable transporta- tion experience possible, nations such as China could lead the world in adopting a more sustain- able yet economically empowering alternative to the privately owned vehicle. A New Type of Vehicle: Electric Vehicles Like carsharing, electric vehicles are a more sus- tainable alternative to the traditional privately owned, conventionally fueled vehicle. Also like carsharing, electric vehicles require little policy change or infrastructure investment from gov- ernment, though greater public sphere attention can help such alternative transportation options flourish. Carsharing and electric vehicles have clear advantages, both from societal and per- sonal perspectives. Yet just as carsharing has slowly entered the market due to disadvantages in convenience and other issues, so electric ve- hicle adoption has grown slowly due in part to public perceptions of overriding disadvantages. Most prominently, fears of being stranded by a dead battery, called “range anxiety”, have dom- inated past discussions surrounding the viability of electric vehicles. Despite their disadvantages, the increasing popularity of both carsharing and electric vehicles could indicate a changing land- scape for cars in general. Both the advantages and the disadvantages of electric vehicles apply to autonomous vehicles. The possible mobility, efficiency, and land use advantages of autono- mous vehicles may only be realized in small por- tions due to concerns over loss of control. While neither range anxiety nor the concern over loss of control is unfounded, much of the innovation of the last decade regarding electric vehicles has been in extending range and demonstrating the viability and reliability of electric vehicles. This section explores nearly two decades of public opinion and related studies concerning the intro- duction and use of electric vehicles. The People Who Are Interested in Electric Vehi- cles The types of people who are interested in elec- tric vehicles (EVs) cannot be summarized into clear demographic categories. One study of more than 3000 individuals found that people interested in EVs tend to be younger and more
  • 20. 15 Driving into the Future highly educated. This study also found that in- come was not statistically significant (Hidrue et al. 2011). In other cases, however, income be- came a factor by the mere expense of electric vehicles. In 2009, BMW released an electric ver- sion of their Mini Cooper called the Mini E. The Mini E was available by an $850 lease to a limited number of households in Los Angeles and New York. BMW and the University of California at Da- vis conducted a series of surveys with the house- holds that leased the Mini E, including a travel diary analysis and a series of in-depth interviews for a selection of those households (Woodjack et al. 2012). By nature of the $850 monthly lease, any household but those who could afford such an experiment could participate. It may be the case that income likewise biases other studies of electric vehicle adoption. In general, however, public opinion studies of EVs tend to find few spe- cific demographic trends. People are interested in electric vehicles for a variety of reasons. Two commonly assumed or attributed factors for purchasing vehicles, envi- ronmental and fuel economy concerns, are not always central motivations. A series of interviews with owners of hybrid electric vehicles found en- vironment to be a central motivation, along with concerns about international peace and interest in supporting technological innovation (Heffner et al. 2007). Other studies found that participants valued environmental benefits of EVs in explic- it statements of attitudes or beliefs, but did not necessarily list environmental reasons as cen- tral to EV purchase or interest (Ozaki and Sev- estyanova 2011; Delang and Cheng 2012). While fuel economy was often listed as a motivation for purchasing an EV, study participants often struggled to formulate or repeat their personal transportation budget, showing a basic lack of understanding as to how exactly fuel efficiency saves money (Cheron and Zins 1997; Kurani and Turrentine 2004). Range Anxiety — A Needless Concern? An early study of public perceptions regarding electric vehicles first highlighted the problem of range anxiety. Four focus groups in Montre- al gathered urban residents together to discuss car ownership in general as well as their partic- ular reactions to the pros and cons of EVs. Par- ticipants identified the most important features of car ownership to be comfort and reliability, specifically to protect against the difficulty of starting vehicles during Montreal’s cold winters. Their most dominate fears regarding general car ownership were running out of gas, followed by having an accident and having a mechanical breakdown. When first asked to express their un- derstanding of EVs, participations stated associ- ations such as, “a more expensive car, a small- er and less comfortable car, a slower car, a car with limited range, a less reliable car, a less safe car, a golf cart” (Cheron and Zins 1997, 283) — mostly highly negative associations. Participants were then introduced to a formal definition of EVs after which they discussed the most promi- nent challenges to EV adoption. Participant dis- cussions revolved around dead batteries and general range anxiety strongly enough that the researchers concluded that this fear alone may be prevent market penetration of EVs. Since the late 1990s, other studies have sought to disprove range concerns by showing evidence that even the lower end of available range in EVs provides sufficient travel for most daily needs. A three-day travel diary survey of 454 house- holds in eight metropolitan regions of California found that most households can easily replace one household vehicle with an EV and still satisfy all their transportation needs. For these house-
  • 21. 16 Driving into the Future holds, range is not an issue (Kurani et al. 2001). In the study mentioned above of households that leased the Mini E, 89% of desired destinations were within a range of 160 miles. With a com- paratively low range of around 100 miles a day, some of these destinations would not be accessi- ble with the Mini E. The farthest destinations, how- ever, tended to be social or recreational and not everyday activities. The majority of survey partic- ipants did not drive more than 80 miles a day, allowing the Mini E to satisfy their travel needs with a complete overnight charge to reset their range. The Future of Electric Vehicle Adoption Small steps have characterized electric vehicle market growth. In Germany, a survey of 1152 indi- viduals found only about 5% of participants were interested in EVs. Taken alone, that seems to be a small number, a mere fraction of the overall interest in vehicle purchasing. However, were all the individuals that composed the 5% to actually purchase an EV, it would signify a marked growth in EV adoption. In 2009, German consumers pur- chased 162 EVs. If 5% of all purchases were EVs, it would total 175,000 EVs sold or an increase in over 1000 times (Lieven et al. 2011). Merely an- alyzing the structural change in surveys from the 1990s to the 2010s shows the progress in EV un- derstanding. In the 1990s, a definition of EV was required and once the definition was given, sur- vey or focus group participants showed strange associations with them. By the 2010s, surveys had no need to include a definition of EVs at all, showing the significant progress in consumer un- derstanding. After greater understanding may also come greater adoption. Do People Want Automa- tion?: Vehicle Automation Conditions Tests Despite the clear benefits of both carsharing and electric vehicles, their market penetration has been minimal in comparison to the overwhelm- ing dominance of traditionally fueled, privately owned vehicles. Analyzing how people actually use a new transportation technology may begin to show why carsharing and electric cars — and maybe eventually self-driving cars as well — have been slow to catch on. Implicit Response to Automation – People’s Be- haviors Trust in new technology can vary between a person’s attitudes and her or his behaviors. For example, a study of 69 college students showed a lack of correlation between stated trust and demonstrated trust in technology. Beginning with a survey of the participants’ willingness to trust technology, and automation in particular, the second phase of the study asked the participants to determine whether a bag passing through a security scanner should be removed for review or not. Participants gave an initial judgment wheth- er to pass or review the bag, then they were showed the judgment of an automated system on the likeliness of the bag to contain a weapon or some other illicit item. Participants was asked to keep or change their initial decision. The auto- mated recommendation was usually correct, but not always. Following this exercise, participants took a final survey, attaching evaluative words to either “automation” or “humans” with the as- sumption that quicker responses represent more implicit associations. The researchers found that explicit and implicit propensity to trust machines
  • 22. 17 Driving into the Future did not correlate (Merritt et al. 2013). Participants who stated a higher level of trust in automation did not necessary show that same level of trust when actually presented with machine auto- mation. Likewise, participants who stated low levels of trust, often enough showed trust in sit- uational automation that conclusions could not be drawn. The importance of this study for gaug- ing the likelihood of autonomous vehicle adop- tion is that explicit willingness to trust automation may not translate into implicit willingness to trust, tempering the findings of any stated preference study. When automation is clearly beneficial to users, people tend to be more willing to lend the sys- tem their trust. An experiment of trust in parallel parking assistance found that participants were more open to the technology after trying it. Par- ticipants in this study were given a questionnaire at the beginning of the experiment and after a series of trainings took a test car out onto a down- town Boston street to parallel park both manually and with parallel parking assistance activated. Participants were outfitted with heart rate moni- toring equipment. Following twelve parking ma- neuvers, participants returned to the lab to com- plete a post- experiment evaluation of their stress and their interest in parking assistance technolo- gy. The difference in heart rate between manual and assisted parking was statistically significant. Heart rate even decreased during parking ma- neuvers in assisted conditions compared to a dramatic increase in heart rate for manual park- ing. 76.5% of respondents stated that the assis- tance made parking easier and 71.4% expressing a belief that it would generally reduce parking stress. The participants had been explicitly cho- sen to represent a range of age categories, but no statistical difference in demographics was found. Older and younger participants were just as likely to experience a reduction in stress and just as willing to want to use the technology in their own cars (Reimer et al. 2010). When given the choice to use or not use auto- mated features, drivers do not always choose such features, even if the features increase the safety and ease of driving. A different study of parking assistance found that participants tend- ed not to use cameras and auditory warnings meant to assist them with backing out of a park- ing space. Participants in this study conducted sixteen parking maneuvers, including backing out of spaces, while their pupil movements and range of vision were measured. In the car, they were provided with a rearview camera and an auditory warning of any objects nearing the re- versing car. About halfway into the testing, an object was placed behind the vehicle without alerting the driver to its presence. Most of the drivers (59%) did not glance at the camera to assist them when reversing and those who did tended to glance less often as reversing trials proceeded without incident. Once the object was placed behind the car, most of the drivers who used the cameras did not collide with the object (87.5%), but 96.3% of the drivers who did not look into the camera did collide with it. Fol- lowing the collision or near collision incident, driv- ers used the camera more often (Hurwitz et al. 2010).This study could indicate that with gradual introduction of automation into the market, driv- ers may become more and more accustomed to the benefits of such systems and increasingly opt to use them. Studies in which participants are placed in more fully automated driving conditions likewise re- veal a gradual increase in use of the technology. Participants in two multi-hour studies were given the option to drive in manual or autonomous conditions while in a controlled environment. In the test vehicles, participants were offered a
  • 23. 18 Driving into the Future range of entertainment options, including radio, movies, books, and food. While both situations were controlled, neither experiment environ- ment was without risk. In one experiment, par- ticipants were in a full environment simulator of driving on a highway (Jamson et al. 2013). They were offered the choice of using manual or au- tonomous mode and were faced with light and heavy traffic conditions. Participants were not in a simulator in Llaneras et al. (2011), but were asked to drive on General Motors testing grounds for three hours, which protected them from most real-world dangers while providing a real-world feel. In both experiments, participants chose au- tonomous driving for a majority of the test. Partic- ipants in the simulation study tended to become more relaxed, a finding consistent with the Re- imer et al. (2010) finding of slowed heart rate with assisted parking, with the percentage of time in which their eyelids were closed more than dou- bling. On the GM testing grounds, participants increased the time looking away from the road by 33%, engaging in all tasks, including high risk, more fully disengaged tasks. If these studies can be generalized to real driving conditions, drivers may be ready to give up control at the very least in conditions such as highways or other low com- plexity roads. Explicit Response to Automation – People’s Atti- tudes If implicit trust in transportation automation — the actual behaviors people display in automat- ed conditions — appears high enough for the introduction of autonomous features in passen- ger vehicles, people’s explicit trust or stated at- titudes towards automation may also indicate readiness or trepidation towards autonomous transportation. As vehicle automation becomes more of a reality, studies are increasingly be- ing conducted to gauge public opinion of and trust in automation. In general, people who take surveys on autonomous vehicles are in favor of them or express a willingness to eventually use or purchase them (Howard and Dai 2014; Kyriakidis et al. 2014; Schoettle and Sivak 2014; Shin et al. 2014). Demographic analyses tend to find men and higher income individuals more interested in autonomous vehicles (Schoettle and Sivak 2014; Howard and Dai 2014). A study of individuals in South Korea found that lower income people were just as interested in smart technologies as higher income individuals, though interestingly they were not as interested in non-convention- al fuel types, indicating a possible success in the marketing of equity for technological but not environmental efficiencies (Shin et al. 2014). An international study of individuals in more than a hundred countries around the world found that despite background differences, participants generally expect autonomous vehicles to be available in the coming decades (Kyriakidis et al. 2014). When asked what participants would be interested in doing while driving, participants chose a large array of tasks, possibly indicating a trust that nearly any stationary task could be accomplished in an autonomous car (Kyriakidis et al. 2014; Schoettle and Sivak 2014). Interest in autonomous cars for these participants seems to be high. While trust in automation seems to be increasing, it is not necessarily clear what exactly people are trusting more. Is it machines? Is it the companies that build these machines? Or is it trust in the abil- ity of average people to use and control these machines? One study concluded that we may not necessarily be trusting machines so much as the people behind the machines. Carlson et al. (2013) provided 737 online participants with one of four scenarios: questions regarding automat- ed medical diagnosis and IBM’s Watson, auto-
  • 24. 19 Driving into the Future mated medical diagnosis without a brand name, autonomous vehicles and Google’s Self-Driving Car, and autonomous vehicles without a brand name. Participants expressed their attitudes to- wards automation in general and regarding the specific type of automation under analysis. They were then asked questions regarding the impor- tance of brand. In the surveys with the branding, participants provided a statistically significant higher trust in the automated system. To con- trol for specific opinions regarding companies (as opposed to general trust in humans versus machines), the surveys also provided questions on aspects of the automated systems that the participants trusted. For example, participants were asked to rate how highly they weighed the importance of system testing. For medical diagnosis systems, participants ranked as one of the highest factors of trust ability of the medical professional to interpret the diagnosis system. For autonomous cars, participants likewise rated their own knowledge of the automated system as critical to trusting the system. In all four survey scenarios, people were trusting people. In order for autonomous cars to penetrate the market, a significant level of trust in the engineers and companies who manufacture the software and the vehicles will have to be well-established. The Future of Automotive Automation Both the implicit and explicit studies of people’s response to automation show a need for a de- liberate engagement with autonomous cars on the part of planners and policymakers. In gen- eral, people tend to want this technology, yet they aren’t as quick to trust it when they are ac- tually using it. Once they have used it, however, they tend to be open to using it more. A greater understanding of how people are interested in using automated features, and particularly how they imagine using autonomous cars, could have a significant impact on the direction of planning and policy-making. Conclusion This literature review included a range of studies in transportation innovation that may provide a glimpse into for how autonomous vehicles may be adopted. First, carsharing members tend to be lower income, young people who live in small households. For these members, carsharing rep- resents a low-cost alternative to owning a car or a low-hassle addition to what is often a single-car household. Especially for emerging economies in countries such as China, carsharing offers a sus- tainable, affordable alternative to vehicle own- ership that could ease urban residents into a high- er level of mobility with a lesser cost to land use and the environment than privately owned vehi- cles have put on higher income nations. Second, electric vehicles also offer a more sustainable option to the conventional vehicle that require little additional infrastructure and public invest- ment. Many of the concerns surrounding electric vehicles, particularly range anxiety, have been shown to be mostly irrelevant for everyday use, a fact that consumers are increasingly coming to understand. Third, studies analyzing response to automotive automation show a greater tenden- cy to trust machines following actual experience of the benefits with automation. In general, pos- itive associations remain or increase and nega- tive associations become neutral or positive after using the automated feature. Coupled with posi- tive anticipation for autonomous cars, the public may be ready to use increasingly autonomous vehicle features as they are introduced to the market. For determining the willingness to adopt autono-
  • 25. 20 Driving into the Future mous vehicles, stated preference surveys create an important bottom line for evaluation of pub- lic opinion. Importantly, the survey presented in this report goes beyond the basic questioning of opinions and collects demographic and travel behavior data. Through this data, a greater un- derstanding of the types of people and types of lifestyles of early adopters may help inform plan- ners and researchers to the nature of transpor- tation changes that may begin to appear. This survey attempts to combine the values of both quantitative and qualitative research by asking not only numerical but open-ended questions that allow participants an opportunity to express responses along prepared lines and along the lines of their own thoughts and attitudes. In this study, participants will not have an opportunity to test their implicit perspectives towards auto- mation and autonomous cars, but this research may build the foundation for future research ef- forts that compare both explicit and implicit re- action to and trust in automation.
  • 27. 22 Driving into the Future This report presents the results from an online sur- vey of 334 participants on demographics, trav- el behaviors, and interest in self-driving cars. The survey was created by the author and distribut- ed in January and February 2015. Survey partici- pants provided basic demographic information, including age, gender, ethnicity and race, in- come, and housing information. In an effort to gauge cultural predictions for likelihood to adopt self-driving cars, the survey also included a ques- tion on political affiliation. Questions about travel behaviors and perspectives on driving provided a baseline analysis for interest in self-driving cars. These questions included information regarding vehicle ownership, the advantages and disad- vantages of driving, interest in new vehicle tech- nologies or ownership models, and commute mode and length. The final five groups of questions focused on self-driving cars. First, participants were asked to select their familiarity with self-driving cars fol- lowed immediately by a direct question on will- ingness to ride in one. Next, to determine trends in vehicle ownership and shared mobility, partic- ipants were given a description of a self-driving ridesharing service and asked to gauge their in- terest in and use of such a service. Third, partici- pants were asked to provide their comfort levels with people who currently cannot drive riding in self-driving cars. With this question, planners can begin to understand how many additional vehicle occupants may be added with self-driv- ing cars. The next group of questions focused on commute frequency and length with self-driv- ing cars. These questions seek to address one of the most pressing questions for planners regard- ing self-driving cars: will this technology enable commuters to live farther from work, potentially inducing more low density, suburban develop- ment? Lastly, participants were asked to list their top concerns and top anticipated features with self-driving cars. The final question was open-end- ed, and about 10% of the participants provided written responses. The full survey questionnaire and top line results are provided in Appendix A, and answers to open-ended questions are pro- vided in Appendix B. Distribution Methodology The survey was live online via the survey service Qualtrics for the first two months of 2015. It was advertised to three primary markets. The first mar- ket category included people most likely to be familiar with self-driving technologies. These indi- viduals were sought out because they are more likely to be early adopters, and the ways in which they use self-driving cars could have reverberat- ing effects on the larger population. The second category of targeted participants included ur- ban planners and transportation professionals who are creating and implementing policies today that will impact transportation tomorrow. The third category of participants included indi- viduals from no particular discipline or interest. These individuals represent a more likely sam- pling of the general population. A full list of sur- vey distribution sources can be found in Appen- dix E. Unfortunately, the survey software used for this study provides no means of determining link referrals. In other words, it is impossible to know if the 334 participants represent an even sampling of individuals from all three categories. CHAPTER 3: Self-Driving Cars Market Adoption Survey
  • 28. 23 Driving into the Future Results Demographics The survey oversampled young, white male par- ticipants. Most participants were young adults with only 40 participants (or 12%) stating that they were over 40-years-old (Fig. 1). The few par- ticipants who checked that they were under 18 were sent to the end of the survey. The relative paucity of participants over 40 is disappointing given the potential importance of adoption for that age category (Reimer 2014). However, the survey adequately sampled the most likely con- sumer age demographic, young adults, given the projected rate of the technological devel- opment. There is no such silver lining to the skew in ethnicity, race, and gender. The majority of participants were non-Hispanic (Fig. 2) and white (Fig. 3) with certain demographics largely absent, such as African Americans. In relation to national population data, the survey was most skewed by gender. Only 27.5% or 92 of the participants were female, compared to 51% nationwide (Fig. 4). The survey participants represented relative skews in employment, income, education, and political affiliation. Though the unemployment rate was the same in the survey population as the United States population at 5.7%, the survey oversampled students. Although 26% of partici- pants were students, most likely in a 2- or 4-year college program given the age range, only 6.5% of the national population is enrolled in higher education (Fig. 5). Likewise, the survey overrepre- sented more highly educated individuals. Signifi- cantly more participants have college degrees than the general public, and for significantly fewer participants a high school diploma is their highest educational attainment (Fig. 7). Income, Fig. 3: RACE Source (for US data): US Census Bureau. 2014. American Community Survey 2013. Washington DC: United States Census Bureau. Source (for US data): US Census Bureau. 2014. American Community Survey 2013. Washington DC: United States Census Bureau. Fig. 2: ETHNICITY Fig. 1: AGE Source (for US data): US Census Bureau. 2014. American Community Survey 2013. Washington DC: United States Census Bureau. 44% 43% 10% 2% 16% 19% 28% 19% 18-25 26-39 40-59 60+ Survey Population United States 3% 91% 17% 83% Hispanic Non-Hispanic Survey Population United States 1% 5% 1% 0% 81% 3% 3% 1% 5% 13% 1% 74% 3% 5% American Indian or Alaskan Native Asian American Black or African American Native Hawaiian or Pacific Islander White Two or more races Other race Survey Population United States
  • 29. 24 Driving into the Future though also somewhat skewed, is more repre- sentative than some of the other demographic categories. However, 13% of survey participants selected the option “cannot choose / refuse to answer,” making true understanding of skew or accurate representation difficult (Fig. 6). Survey participants largely identified their political affil- iation as either Independent or Democrat with very few participants identifying as Republican (Fig. 8). Community and Housing The United States Census does tally whether residents live in urban or rural communities, but they do not differentiate between urban and suburban communities. From a planning and transportation efficiency perspective, suburban communities tend to be auto-oriented, making regular transportation by any other mode diffi- cult and slow. Urban communities, on the other hand, tend to have more efficient transit options with more walkable neighborhoods and better bicycle networks. From a transportation perspec- tive, many suburban communities are closer to rural than urban communities in their offering of a multimodal lifestyle. For that reason, this survey asked participants to state whether they lived in an urban, suburban, or rural community. A slight majority of participants defined their community as suburban with a close second group select- ing urban as their community. Only 8% identified their community as rural compared to 19% na- tionally. Although the communities in which sur- Source (for US data): US Census Bureau. 2014. American Community Survey 2013. Washington DC: United States Census Bureau. Fig. 4: GENDER Fig. 5: EMPLOYMENT Source (for US data): Bureau of Transportation Statistics. 2014. Pocket Guide to Transportation 2014. Washington DC: United States Department of Transportation; National Center for Education Statistics. 2015. Back to School Statistics. Washington DC: United States Department of Education; Bureau of Labor Statistics. 2015. Household Data, Seasonally Adjusted. Washington DC: United States Department of Labor. Source (for US data): US Census Bureau. 2014. American Community Survey 2013. Washington DC: United States Census Bureau. Fig. 6: INCOME Survey Population United States 51% 49% Female Male 28% 70% Female Male 59% 6% 26% 6% 2% 59% 4% 7% 6% 35% Employed Employed, work at home Student Unemployed, looking for work Unemployed, not looking for work Survey Population United States 9% 6% 14% 19% 12% 14% 14% 13% 11% 24% 18% 12% 13% 10% Under $15k $15-24k $25-49k $50-74k $75-99k $100-149k Over $150k Survey Population United States
  • 30. 25 Driving into the Future vey participants live reflect national trends, it is significant to know that 60% of participants live in largely car-dependent rural and suburban com- munities and 38% live in more multimodal urban communities (Fig. 9). Likely because of the age skew, the survey over- represented renters and childless households. The majority of United States residents are homeown- ers, but only 30% of survey participants owned their home. Sixty-seven percent of participants either rented their home or lived in some other Fig. 8: POLITICAL AFFILIATION Source (for US data): Gallup. 2015. Party Affiliation. Washington DC: Gallup, Inc. http://www.gallup.com/poll/15370/party-affiliation.aspx (accessed March 4, 2015). 89.8% 8.1% 80.7% 19.3% Urban Rural Survey Population United States 89.8% 8.1% 80.7% 19.3% Urban Rural Survey Population United States Fig. 9: COMMUNITY TYPEFig. 7: EDUCATION Source (for US data): US Census Bureau. 2014. American Community Survey 2013. Washington DC: United States Census Bureau. Urban (38%) Suburban (52%) Source (for US data): US Census Bureau. 2010. United States Census. Wash- ington DC: United States Census Bureau. Fig. 10: HOUSING STATUS Source (for US data): US Census Bureau. 2014. American Community Survey 2013. Washington DC: United States Census Bureau. situation, such as living with a parent or living (Fig. 10). The vast majority (77%) of participants lived in households with no persons under the age of 18, compared to 67% nationally. Transportation The survey participants, though generally a pop- ulation of drivers, were particularly multimodal in their use of transportation. Most participants owned or wanted to own a car. Significantly, 1% 6% 21% 3% 38% 28% 12% 30% 19% 9% 19% 10% Under 12th grade (no diploma) High school graduate Some college, no degree Associate's degree Bachelor's degree Master's degree or higher Survey Population United States 39% 29% 7% 9% 29% 43% 25% 3% Democrat Independent Republican Other political view Survey Population United States Gallup Data 90% 8% 19% 81% 55% 30% 13% 35% 65% Rent home Own home Other housing situation Survey Population United States
  • 31. 26 Driving into the Future given the auto-oriented nature of most United States communities, 9% of participants neither owned a car nor desired to own one (Fig. 11). Although the largest percentage of participants drove alone to work or school, 20% of participants commuted via transit, five times greater than the national average. Additionally, four times more participants commuted by walking or bicycling than the national average (Fig. 12). Further, they were open to more sustainable methods of driv- ing with the clear majority interested in hybrid or electric vehicles (Fig. 14) and also the clear majority interested in or experienced with shared mobility services (Fig. 15). Most participants considered owning a vehicle to be a personal priority. Significantly, 31% of par- ticipants rated car ownership as a low priority, either “nice, but not necessary” or completely unimportant “as long as I’m able to get where I want to go” (Fig. 16). Such deemphasis on the value of vehicle ownership may indicate a cul- tural shift away from the car as status symbol. Participant opinions on the value of driving re- vealed general consensus on the advantages Fig. 11: CAR OWNERSHIP Question: Do you own a car? of driving and a wide selection of the disadvantages of driving. Par- ticipants were asked to select the top three advantages of driving from a list of seven choices: af- fordability, comfort, flexibility, pri- vacy, safety, social status symbol, and time saved. Flexibility, time saved, and comfort were over- whelmingly chosen as the top advantages to driving with priva- cy in a distant fourth. A small per- centage of participants chose safety, affordability, and symbol as leading advantages. Overall, Fig. 12: COMMUTE MODE Fig. 13: COMMUTE TIME 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% Less than 15 minutes 15-29 minutes 30-44 minutes 45 or more minutes 78% 12% 9% Yes No, but want to No, and don't want to 47% 5% 20% 7% 10% 1% 76% 9% 5% 1% 3% 1% Drive alone Carpool Ride transit Bicycle Walk Other Survey Population United States
  • 32. 27 Driving into the Future 312 267199 138 69 62 56 Flexibility Time saved Comfort Privacy Safety Affordability Social status symbol Fig. 15: SHARED MOBILITY Question: Have you ever used a ridesharing service (Uber, Lyft, etc.) or a carsharing service (Zipcar, car2go, etc.)? Fig. 16: CAR OWNERSHIP IMPORTANCE Question: How important is vehicle ownership to you? Fig. 17: ADVANTAGES OF DRIVING Question: In your opinion, what are the 3 greatest advantages of driving? Flexibility1 3 2 Comfort Time saved Fig. 18: DISADVANTAGES OF DRIVING Question: In your opinion, what are the 3 greatest disadvantages of driving? 251 172 171 152 149 137 93 Expense Inability to multitask Pollutions and emissions Parking problems Stress Safety Time wasted 251 172 171 152 149 137 93 Expense Inability to multitask Pollutions and emissio Parking problems Stress Safety Time wasted Expense1 3 2 Pollutions and emissions Inability to multitask participants rated aspects of convenience as the best attributes of driving (Fig. 17). Participants were in less agreement regarding the disadvan- tages of driving from a list of expense, inability to multitask, parking, pollution and emissions, safety, stress, and time wasted. Expense took the most votes for a top disadvantage, which was not surprising given the low selection for afford- ability as an advantage. The next greatest dis- advantage was spread fairly evenly among the next five choices. Inability to multitask and pollu- tion and emissions were neck-and-neck with 42% and 41% of participants putting each choice in the top three. Parking and stress were one step Fig. 14: ELECTRIC VEHICLES Question: Do you want to own a hybrid or electric vehicle? 312 267199 138 69 62 56 Flexibility Time saved Comfort Privacy Safety Affordability Social status symbol 8% 71% 10% Currently own or use one Would consider buying one No, and not interested 41% 30% 21% 8% Have used shared mobility services Interested in shared mobility services Not interested in using shared mobility services Not familiar with shared mobility services 25% 34% 13% 18% Among highest priorities Important Nice, but not necessary Not important
  • 33. 28 Driving into the Future lower with 36.5% and 32%. Safety took 29%. Time wasted came in last with only 15% of votes (Fig. 18). Self-Driving Cars: General The heart of the survey asked participants to ex- plore their opinions regarding self-driving cars. To set the stage, participants were asked to state their level of familiarity with self-driving cars de- fined as “vehicles that can drive themselves with- out human assistance, also called driverless or autonomous cars.” Rather than providing a more thorough definition of autonomous vehicles that risked a participant skimming or becoming con- fused, the survey provided a more simple defini- tion that only defined the most capable, Level 4 vehicle. The results showed a general familiarity with the technology among participants. Only 4% of participants either stated that they were unfa- miliar or they selected “cannot choose / refuse to answer” whereas 96% stated that they either closely follow the technological development or they are somewhat familiar with the technol- ogy (Fig. 19). Willingness to ride in a self-driving car was also high with only 16% stating that they were either not willing or not sure if they were will- ing to ride in a self-driving car, compared to 83% who were willing (Fig. 20). Because of this definite familiarity with and openness to self-driving cars, the more significant results came in the projected use of and opinions regarding the technology. Use of and opinions regarding self-driving cars were divided into four sections. In the first two sections, participants were asked questions re- garding shared mobility and commuting prac- tices with self-driving cars. In the third section, participants selected their comfort level with var- ious individuals who cannot drive riding alone in a self-driving car. Finally, participants were asked to select their top concerns and anticipated fea- tures with self-driving cars, with an alternative to write in a concern or feature not provided. Self-Driving Cars: Travel Patterns Two potential travel pattern changes with self-driving cars were highlighted in the survey. Participants were asked to express their interest in a self-driving rideshare service, and they were asked several questions regarding commuting patterns. For the self-driving rideshare questions, Question: Have you heard of self-driving cars (vehicles that can drive themselves without human assistance, also called driverless or autonomous cars)? Fig. 19: FAMILIARITY Fig. 20: WILLINGNESS TO RIDE Question: Would you be willing to ride in a self-driving car? 37.1% 59% 2.7% 0% 0% Very familiar Somewhat familiar Not familiar, but interested Not familiar, not interested Doesn't sound possible 83% 6% 10% Yes No Uncertain
  • 34. 29 Driving into the Future 11% 60% 25% Would continue to own a car and not use such a service Would continue to own a car and also use such a service Would not own a car with such a service available participants were asked to consider this service: “Using an app or text messaging service, you re- quest a self-driving car to pick you up within 5-15 minutes and take you where you need to go. After dropping you off, the car serves other cus- tomers. When you are ready to leave, you make another car request.” Three questions followed this text, gauging participant interest in such a service, their interest in sharing a self-driving ride- share car with another person, and their interest in owning a car if such a service were available. Participants were generally yet cautiously open Question: How often would you use such a self-driving rideshare service? Fig. 21: SELF-DRIVING RIDESHARE: USE FREQUENCY Fig. 22: SELF-DRIVING RIDESHARE: SHARED RIDES Question: Would you be willing to ride in a self-driving rideshare car with other passengers for a reduced fare? Question: Would you still own a car if such a self-driving rideshare service were available? + own car, - use service + own car, + use service - own car, + use service Fig. 23: SELF-DRIVING RIDESHARE: CAR OWNERSHIP to a self-driving rideshare service. The largest por- tion of participants stated that they would often or sometimes use a self-driving rideshare service. Comparatively few expressed absolute opinions with only 12% stating that they would always use such a service and 16.5% preferring to never use the service (Fig. 21). To reduce total cars on the roadways, carpool- ing and decreased car ownership may be key. When asked if they would be willing to carpool in a self-driving rideshare vehicle for a reduced fare, the overwhelming majority of participants were willing. Only 14% were uninterested in shar- ing a ride, compared to 41% who were willing and 43% who were potentially willing (Fig. 22). Unfortunately, the majority of participants stat- ed that they would still own a vehicle with such a self-driving rideshare service available. A full quarter of participants, however, would not own a car in favor of such a shared system (Fig. 23). The implications of such a system could be sig- nificant for transportation networks in all types of communities. While it could change car owner- ship trends, it could also threaten public transpor- tation. Understanding the likelihood that such a system would be used, therefore, has many impli- 12% 31% 32% 17% 5% Always Often Sometimes Rarely Never 41% 43% 14% Yes Maybe No
  • 35. 30 Driving into the Future cations for the future of transportation planning. It is possible that self-driving cars may have rela- tively little impact on commuting practices. Al- though almost half of all participants would only occasionally use self-driving cars to commute, a quarter of commuters would always use the tech- nology to get to work. A relatively high 18% stat- ed they would never use self-driving cars to com- mute (Fig. 24). Further, most participants (62%) were uninterested in living farther away from work with self-driving cars (Fig. 25). Of the partic- ipants willing to live farther away from work with self-driving cars, it is impossible to know if these participants would be interested in living in a low- er or higher density environment. In other words, would these individuals push for further suburban or rural sprawl, or would they move to the city to travel to a rural or suburban job? Although it may be difficult to know their intentions the possibility of increased sprawl is real with self-driving cars. Self-Driving Cars: Increased Mobility One of the great possibilities with self-driving cars is the increased mobility gained for populations of people who currently should not or cannot drive. For example, drunk drivers should never control the wheel, yet they do at an alarming rate. Some disabled populations, such as the blind or those bound to wheelchairs, cannot currently drive be- cause of the requirements that drivers watch the road and control the throttle and brake. Self-driv- ing cars could facilitate mobility for these and other populations. This question of increased mobility is important for four primary reasons. First, streets could become safer with drunk drivers, distracted drivers, and some teenage and elderly drivers ceding control of their vehicles. Second and third, this increased mobility with cars could facilitate greater equity in transportation access while also threatening the transit services that currently exist to provide such mobility. The fourth consideration is that by welcoming additional individuals into privately occupied vehicles, more cars could be added to already congested, polluted roadways. For all these reasons, understanding the possible use of self-driving cars by such populations is critical to understanding the future of driving. To gauge such an increase in mobility, partici- pants were asked to rate their degree of comfort Fig. 24: SELF-DRIVING COMMUTE Question: Would you use self-driving cars to get to work or school? Question: Using self-driving cars, would you live farther away from work or school? Fig. 25: EXTENDING COMMUTE DISTANCE WITH SELF-DRIVING CARS 24% 31% 17% 18% Always Sometimes Rarely Never 25% 69% 6% Yes No Other (not possible, cannot choose)
  • 36. 31 Driving into the Future Fig. 26: VEHICLE OCCUPANTS IN SELF-DRIVING CARS Preferably Under certain circumstances Only with a capable driver present Never Preferably Under certain circumstances Only with a capable driver present Never Highly Distracted Persons Elderly Persons Teenagers Children2 1 Disabled Persons3 4 5 Question: Should the following individuals be passengers in self-driving cars without an able, human driver? with different types of people riding in a self-driv- ing car. Specifically, they were asked to answer this question: “Should the following individuals be passengers in self-driving cars without an able, human driver?” They were presented with a chart containing a list of six population types: children, teenagers, elderly persons, disabled persons, in- toxicated or high persons, and highly distracted persons. Participants were given the choice to answer the question with one of four degrees of comfort: “Yes, preferably,” “Yes, under certain conditions,” “Only with a capable human driver present,” and “Never” (Fig. 26). In general, participants expressed a high de- gree of comfort with the listed populations riding in self-driving cars. The population most partici- pants thought could ride alone were highly dis- tracted drivers, perhaps with the assumption that they would be most capable to taking control in event of an emergency. The next population for which participants selected a high degree of comfort using self-driving cars was elderly per- sons. Intoxicated or high persons came in third place. It is hard to know what the 24% of indi- viduals who selected “Yes, under certain circum- stances” intended for these vehicle occupants, but perhaps they assumed that occupants un- der the influence may sometimes not be able to provide navigation instructions to a car com- puter. In those cases in which they are not ca- pable of providing accurate instructions, riding alone in a self-driving car could be dangerous. For disabled persons, the next highest category, 47% of participants selected the highest degree of comfort, perhaps indicating uncertainty on whether disabled persons could independent- ly maneuver in and out of a self-driving car. The teenagers category was more evenly distributed with nearly equal numbers of participants select- ing the top three categories of comfort. Partic- ipants were the least comfortable with children 61% 20% 13% 3% 36% 32% 24% 5% 54% 26% 15% 3% 20% 27% 34% 15% 47% 28% 19% 3%
  • 37. 32 Driving into the Future being alone in self-driving cars. The largest per- centage of participants only felt comfortable with children riding in a car along with an adult. Overall, the category with “never” selected most frequently was the children category. With the sole exception of the children catego- ry, most participants selected the highest degree of comfort for people who cannot drive current- ly riding in a self-driving car in the future. While stating level of comfort and actually enacting comfort may not be the same thing, as shown in the literature review with explicit versus implic- it trust in automation, this data shows a strong likelihood that self-driving cars may increase the number of possible vehicle occupants on the roadways. While this speculation does not have to mean that the total number of vehicles will also increase, given the possibility of decreased vehicles on the road with shared autonomous vehicles, it is nevertheless an important possibility. Self-Driving Cars: Concerns and Anticipated Fea- tures Four concerns were most prominent among the seven offered. As other surveys have shown, li- ability is a persistent concern (Howard and Dai 2014; Kyriakidis et al. 2014; Schoettle and Sivak 2014). In case of a traffic incident, who is respon- sible? The occupant? The hardware manufac- turer? The software engineers? It may be new legal territory. The uncertainties around how it will work out worry the public more than many vehicle automation issues. (For thorough discus- sions of legal liability with autonomous cars, see Villasenor 2014 or the blog of Bryant Walker Smith at cyberlaw.stanford.edu.) In showing liability to be a prominent concern, this survey was no ex- ception. Liability received the highest number of selections as a top concern. Usually, however, control is also a top concern, but control came in fourth place in this survey. In second place was expense, which was no surprise given the weight placed on expense as a disadvantage to driv- ing earlier in the survey. The third highest con- cern was data privacy, with control closely be- hind. Safety received 21.5% of the votes as a top concern, which is significant given that greater safety is a highly publicized anticipated feature of self-driving cars. Closely behind safety, partici- pants worried that self-driving cars will be bad for the environment, potentially through promoting 177 145 121 113 72 65 64 54 29 Liability Expense Data privacy Control Safety Environment Nothing Other Congestion Question: What are your top 3 concerns with self-driving cars? Fig. 27: CONCERNS WITH SELF-DRIVING CARS Liability1 3 2 Data privacy Expense 177 145 121 113 72 65 64 54 29 Liability Expense Data privacy Control Safety Environment Nothing Other Congestion Question: In your opinion, what are the 3 greatest advantages of driving? Convenience1 3 2 Multitasking Safety Fig. 28: ANTICIPATED FEATURES OF SELF-DRIVING CARS 196 190 185 157 136 45 35 7 Convenience Safety Multitasking Mobility Congestion Environment Speed Other 196 190 185 157 136 45 35 7 Convenience Safety Multitasking Mobility Congestion Environment Speed Other
  • 38. 33 Driving into the Future for drinking without having to bother with desig- nating transportation, one comment brought the human component back into transportation: “I will worry less for my alcoholic girlfriend.” A dif- ferent kind of increased mobility was highlighted by one commenter who wrote, “I’m legally blind and, to quote the Oatmeal, ‘I, for one, welcome our new adorable Skynet Marshmallow Bumper Bot Overlords!’” (From Inman 2014). The most common comment revolved around the pleasure of driving. Several commenters generally expressed their preference for driving, while others specifically noted that self-driving cars will remove a valuable part of American cul- ture. The below comment reflects the sentiments in many similar comments: Comments expressing general discomfort (such as: “It’s TOO SPOOKY.”) were rare. A full list of comments can be viewed in Appendix B. more driving. The smallest concern was conges- tion (Fig. 27). Not surprisingly, convenience topped the list of anticipated features. Because the survey partic- ipants rated convenience features so highly for driving in general and because self-driving cars will likely increase convenience, this feature will be highly marketable to the populations this sur- vey represent. Closely behind convenience are safety and ability to multitask. It is significant that safety is seen as both a concern and a feature, perhaps because of the complexity of safety. Whereas human-error-caused incidents may de- crease, computer-caused and other unforeseen issues may create a different kind of danger. The importance of multitasking begins to confirm the emerging belief that driving is now the distrac- tion. Mobility and congestion were a close fourth and fifth place with environment and speed only gaining 13% and 10% of the votes as top features (Fig. 28). Participants provided a wide variety of com- ments in the concerns, features, and open-end- ed final question. Several comments revolved around safety and the increased dangers with self-driving cars. Commenters highlighted insecu- rities with hacking, failure to respond creatively or appropriately in an emergency, and problems with mixing human drivers with self-driving cars on the roads. One commenter summed up some of these feelings by stating, “The vehicle lacks com- passion.” Other comments focused on the econ- omy, either worrying about lost jobs or praising the efficiencies gained. A few comments high- lighted issues in land use and mobility, expressing concerns over increased suburban sprawl or an- ticipating the walkability benefits to more pedes- trian-responsive streets. Alcohol was a common theme in additional anticipated features. While most such comments discussed the advantages I don’t drive a car because it’s an ap- pliance that moves me from point A to point B. I drive a car because I like it. I like personalizing and customizing and turn- ing it into both a status symbol and a re- flection of who I am. It’s my community, my hobby, I meet friends through it, it’s a sport, it’s an art. All of this is lost in a SDC.
  • 39. 34 Driving into the Future Analysis Fig. 29: Familiarity and Willingness How does a person’s familiarity with self-driving car research and development influence her or her willingness to ride in a self-driv- ing car? Very familiar Somewhat or not familiar Willing Not willing 36% 15% 2% 47% In general, participants who were very familiar with self-driving car research and development were willing to ride in a self-driving car. Even par- ticipants who were somewhat or less familiar with the technology were generally willing to expe- rience a self-driving car ride (Fig. 29). Because of this general openness to self-driving cars, the more dynamic and interesting analysis comes from the different ways in which participants were interested in integrating the transportation technology into their lives. In this section, statis- tical analysis revolves around commuting and shared mobility travel behaviors as primary pillars of personal transportation. The coding index for statistical analysis and full statistical results tables can be found in Appendix C and D, respectively. Commuting with Self-Driving Cars Because commuting is central to travel behav- ior, determining interest in using self-driving cars to commute is important to gauging the future of transportation. Given how willing participants were to ride in a self-driving car in general, it is a bit surprising that they showed more reservation when expressing their interest in commuting with self-driving cars. Only a quarter of participants were interested in always using self-driving cars to commute with slightly more interested in only sometimes using them to commute. The largest percentage were either rarely or never interest- ed in letting cars drive them to work. Using binary logistic regression with confidence set at 95%, several variables were found to be statistically significant with interest in using self-driving cars to commute. Not surprisingly, people who drive to work were nearly ten times more likely and people who have long com- mutes were about six times more likely to be in- terested in having self-driving cars taking over their commute. For the drivers, self-driving cars present a fairly seamless mode transition, and for people with long commutes, self-driving cars could provide some relief. Also not unexpected, people who would consider living farther away from work with self-driving cars tended to want to use self-driving cars for that longer commute. Significantly, participants who showed an inter- est in using self-driving rideshare services were more likely to be interested in self-driving com- muting, indicating a possible future of carshar- ing being used for commuting, which is current- ly an underutilized use of carsharing (Costain et al. 2012; Ciari 2010). Non-white participants (of which there were 64) were also more likely to be interested in self-driving commuting, a trend that requires further study. (See Fig. 30.) Living Farther Away From Work with Self-Driving Cars One of the most central concerns with self-driv- ing cars from an urban planning perspective is the possibility that the technology could encour-