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JPEXXX10.1177/0739456X15613591Journal of Planning Education and ResearchGuerra
Research-Based Article
Planning for Cars That Drive Themselves:
Metropolitan Planning Organizations,
Regional Transportation Plans, and
Autonomous Vehicles
Journal of Planning Education and Research
2016, Vol. 36(2) 210­–224
© The Author(s) 2015
Reprints and permissions:
DOI: 10.1177/0739456X15613591
Erick Guerra1
Through a review of long-range transportation plans and interviews with planners, this article examines how large metropolitan
planning organizations are preparing for autonomous vehicles. In just a few years, the prospect of commercially available selfdriving cars and trucks has gone from a futurist fantasy to a likely near-term reality. However, uncertainties about the new
technology and its relationship to daily investment decisions have kept mention of self-driving cars out of nearly all long-range
transportation plans. Nevertheless, interviewees are keeping a close watch on the new technology and actively looking to
understand and plan for future impacts.
autonomous vehicles, long-range planning, self-driving cars, regional transportation planning, metropolitan planning
Self-driving cars are no longer science fiction. In 2005, five
research teams’ self-driving vehicles completed the Defense
Advanced Research Projects Agency’s (DARPA’s) 150-mile
obstacle course designed to challenge autonomous vehicles
and spur new technological innovations. A year earlier, no team
had completed even a tenth of the course. Google, which subsequently hired engineers from several of the winning teams,
has developed semi-autonomous vehicles that have driven
more than a million miles on city streets and highways. The
company recently unveiled a fully autonomous prototype car
with no brake pedal, accelerator, or steering wheel and plans to
test the cars on its campus (Markoff 2014). Most major car
manufacturers already market and sell high-end vehicles with
features like automated braking, self-parking, lane-departure
warning, and variable-speed cruise control. Most are also racing to develop fully autonomous vehicles. Nissan announced
that it plans to mass-market cars with automated steering, braking, and acceleration by 2020 (Nissan 2014).
The freight and transit industries will be likely early
adopters of driverless technologies since the higher vehicle
costs will be offset by lower labor costs. Daimler recently
began testing an autonomous 18-wheeler prototype on public
roads in Nevada (Davies 2015). Many transit agencies and
airports already have decades of experience operating driverless trains on fixed guideways (Furman et al. 2014), and the
European Union–funded CityMobil2 has already begun testing driverless transit on public streets (CityMobil2 2015).
Within the next twenty years, fully autonomous vehicles
will likely be commercially available and driving themselves
on city streets and highways. By removing humans and
human error from the driving task, autonomous vehicles
have the potential to reduce congestion and traffic collisions
dramatically (Shladover 2000; Thrun 2010; Fagnant and
Kockelman 2014; Anderson et al. 2014; Winston and
Mannering 2014). Self-driving freight, transit, and personal
vehicles may also alter how people and goods move and
where households and firms choose to locate. The policies,
regulations, plans, and technologies adopted for autonomous
vehicles will influence the scale and perhaps even the direction of these impacts.
Despite a history of and purported focus on projecting and
planning for the future (Isserman 1985; Myers and Kitsuse
2000; Cole 2001; Couclelis 2005), the planning profession has
a somewhat poor track record of preparing for new transportation technologies. Brown, Morris, and Taylor (2009) argue
that planners’ inability to foresee the impacts of private cars at
Initial submission, November 2014; revised submission, July 2015; final
acceptance, August 2015
University of Pennsylvania, Philadelphia, PA, USA
Corresponding Author:
Erick Guerra, Department of City and Regional Planning, University of
Pennsylvania, 127 Meyerson Hall, 210 S. 34th Street, Philadelphia, PA
19104, USA.
Email: erickg@design.upenn.edu
policy, see Wagner et al. 2014 or Williams 2013). One study
surveyed state and local transportation officials to determine
which regions are best prepared to embrace and implement
new technologies like self-driving cars (Shaheen, Camel,
and Ullom 2014). No published research considers the
impacts on cities or planning in more than a cursory way.
This study is the first to investigate how a group of planners—those from large metropolitan planning organizations
(MPOs)—are preparing for what is potentially the most
transformative transportation technology since the internal
combustion engine and the mass-produced automobile. After
briefly describing autonomous vehicles, I structure the
research design, findings, and discussion around two primary questions:
Figure 1. Recent publications and projects about self-driving
or autonomous vehicles (AVs) listed in the TRID database
(Transportation Research Board 2015).
the beginning of the twentieth century contributed to the proliferation of an engineering-dominated vision of urban highways that focused almost entirely on vehicle throughput.
Instead they either ignored the car, which proceedings of the
1909 First National Conference on City Planning mentioned
just once, or embraced it as a solution for problems related to
crowding and animal-powered transportation (Foster 1979;
Brown, Morris, and Taylor 2009). When developing the interstate highway system, many planners and city boosters advocated for urban highways to stem blight and strengthen the
urban core (Mohl 2004; Hall 1996, chap. 9), though the highways almost certainly encouraged suburbanization and urban
flight (Baum-Snow 2007; Duranton and Turner 2012).
Similarly, misunderstandings about aviation technology,
the importance of the postal service, and commercial air-carriers reduced planners’ ability to influence airport locations or
surrounding land uses (Bednarek 2001, chap. 5; Barrett 1999).
Again, planners viewed the new technology primarily as a
welcome tool to alleviate the concern of the day: urban crowding. More recently, Shoup (1997, 1999, 2005) has argued that
planners helped create “perhaps the greatest [planning disaster] of all time” (2005, 218), by misunderstanding parking
supply and the effects of minimum parking requirements.
Planners may yet again fail to influence the relationship
between cities and a new transportation technology by either
misunderstanding driverless cars or seeing them as a solution
for contemporary planning problems, such as road congestion or climate change. After 20 years of stable research into
self-driving cars, there has been a recent spike in the number
and proportion of papers and projects about autonomous
vehicles listed on the Transportation Research Board’s TRID
database (Figure 1). Most of the work focuses on technology,
regulatory policy, and the likely impacts on safety or highway capacity (Kalra, Anderson, and Wachs 2009; Anderson
et al. 2014; Fagnant and Kockelman 2014, 2015). (For a
recent summary of the literature and state of technology and
Why do MPOs not include self-driving cars in their
long-range plans or do more to plan for self-driving
How are large MPOs starting to plan for self-driving
Large MPOs have both the staff and the mandate to consider the implications of driverless cars. For example,
Philadelphia’s MPO has almost one hundred planners on
staff while Seattle’s has about seventy-five (Delaware
Valley Regional Planning Commission 2015; Puget Sound
Regional Council 2015). And federal law (U.S. Code Title
23 Chapter 1 § 134 – Metropolitan Transportation Planning
2014) requires that MPOs develop long-range regional
transportation plans (commonly referred to by the acronyms RTP or LRTP) with a minimum planning horizon of
twenty years and update them every four years for regions
with more than fifty thousand residents. Required to be
performance-driven and outcome-oriented, these plans set
regional priorities and guide regional transportation investments to achieve national goals related to safety, infrastructure condition, congestion, and system reliability. For better
or worse, autonomous vehicles will almost certainly influence these and other national goals within this minimum
planning horizon. Yet only one of the twenty-five largest
MPOs even mentions driverless, automated, or autonomous
vehicles in its most recent RTP.
In addition to providing the first systematic review of how
a group of proactive planners are considering self-driving cars,
the article documents the state of best planning practice and
illuminates some of the challenges of preparing for transformative technological shifts. It also demonstrates some of the
limitations of the RTP process and raises questions about
whether and how planners could do more to prepare for a fastapproaching and potentially transformative new technology.
What Are Self-Driving Cars?
Engineers and futurists have long envisioned the potential
for and benefits of self-driving vehicles (Beiker 2014;
Journal of Planning Education and Research 36(2)
help improve vehicle automation and have similar road
safety and capacity impacts, but do not have the same potential to transform the transportation system by replacing drivers altogether.
Research Design
Figure 2. Advertisement of family playing dominoes in an
electric-powered self-driving car (America’s Independent Electric
Light and Power Companies, art by H. Miller).
Source: Courtesy of the Everett Collection.
Shladover 1998). A 1957 advertisement showing a family
playing dominoes while their car moves itself smoothly
along the highway graces the cover of a RAND policy report
(Anderson et al. 2014) on self-driving cars (Figure 2). Only
recently, however, has the confluence of rapid improvements
in computer processing, satellite positioning, and laser sensing made this long-held dream a potential reality. Current
prototypes rely on the Global Positioning System (GPS),
cameras, and other sensors, particularly LIDAR (like
RADAR but with lasers instead of radio), to detect a vehicle’s location and the location of surrounding vehicles, people, and obstacles to remain safely in the center of a highway
lane or move around on city streets.
Between zero and full autonomy is a range of functional
controls such as adaptive center-lane cruise control, which can
take over the driving task on highways but requires the driver
to remain attentive and ready to take over the task of driving at
short notice. The U.S. Department of Transportation’s National
Highway Traffic Safety Administration (NHTSA) defines five
levels of automation ranging from Level 0 to Level 4—which
refer respectively to a human-controlled car and a fully selfdriving car (U.S. Department of Transportation 2013a). A
separate, but intimately related, technology (and one frequently discussed throughout interviews with MPO planners)
is that of connected vehicles, which communicate wirelessly
with other vehicles or infrastructure.1
Throughout this study, I refer to autonomous, automated,
driverless, and self-driving vehicles interchangeably, but
treat connected vehicles separately. Connected vehicles may
In March and April 2014, I collected and reviewed the most
recent RTPs from the twenty-five most populous metropolitan areas in the United States (as of July 1, 2013). Altogether,
130 million people—roughly 40 percent of the nation’s population—live within these regions. I searched the RTPs for
variations on phrases like “self-driving,” “connected vehicles,” “autonomous,” “automated,” and “technology.” If the
document mentioned self-driving, connected, or autonomous
vehicles, I summarized how and where this fit within the
regional plan, as well as principal priorities and other trends
and technologies mentioned in the plan.
I then contacted staff members from the twenty-five
MPOs and conducted in-depth, semistructured interviews
with representatives of fifteen of them (a 60 percent response
rate). I conducted two interviews in person, with the remaining thirteen over the phone. Another two MPOs provided
written feedback to questions (for a total response rate of 68
percent), but these did not provide the same level of detail or
nuance as the interviews and are largely excluded from the
reported findings (Appendix A).
I initially emailed any person listed as a contact on the
RTP, the head of transportation planning, and/or the head of
long-range planning to request an interview. Contacted staff
members often forwarded the request to other MPO employees, whom they thought better suited to the interview.
Interviews took place between May and September 2014,
ranged from 40 to 80 minutes, and included one to five participants. Interviewees included senior planners, senior management, and project managers in transportation planning,
long-range planning, modeling, research, and Intelligent
Transportation Systems (Appendix B).
After reviewing the RTPs and discussing the findings
with local MPO staff members, I developed interview
questions around three hypotheses about why MPO planners have not incorporated autonomous vehicles into their
Planners are either unaware of the new technologies
or do not believe their impacts will be profound;
The impacts are not yet certain enough for credible
planning efforts; and
The impacts are too far removed from day-to-day
policy and investment decisions.
I also asked planners direct, open-ended questions about
why they did or did not mention self-driving cars in the RTP
and what future plans, if any, they had for incorporating selfdriving cars into their planning efforts. Interviewees also
discussed other mechanisms through which their MPO has
considered the impacts of self-driving cars, probable MPO
responses to a series of predicted impacts of self-driving
cars, and the knowledge-gaps that must be filled for planners
to incorporate autonomous vehicles into long-range planning
efforts and investment decisions.
Interviewees indicated sensitive material throughout the
interviews and in two cases requested that I stop recording
toward the end of the session. In order to maintain anonymity
and consistency throughout the text, I opt not to present the
names of any interviewees in the body of the paper. For the
section on contemporary planning practices, the interested
reader can match the names of the organizations with the
names of the interviewees in Appendix B.
Sample Selection
Several considerations influenced the selection of MPOs in
these regions. First, MPOs are federally mandated to develop
and update long-range plans every four years. This makes
them more likely to consider long-range trends than cities or
municipalities, which are more focused on day-to-day operations and project implementation. Second, the largest metropolitan areas tend to have the largest, most technically
savvy MPO staffs that are most likely to have the resources
to incorporate self-driving cars into planning efforts. If these
MPOs are not planning for self-driving cars, smaller MPOs
are unlikely to be doing so. Third, including the top twentyfive regions gives a much more nationally representative
cross section of U.S. metropolitan areas than a smaller sample. The top twenty-five includes large coastal metropolises
as well as many of the fast-growing smaller cities of the
Sunbelt and several older, slow-growing Midwestern
regions. It also includes a far greater range of politically liberal and conservative regions than a top-ten or top-five list.
Finally, although the number of regions is small, their
importance is great. Forty percent of the country lives within
them and is directly affected by their MPOs’ plans and
investment decisions.
While this sample selection provides a convenient way to
identify best proactive planning practices with a limited
number of interviews, smaller MPOs’ reasons for not including self-driving cars in their plans and current planning practices should not be inferred from the sample. However,
sampling from the agencies that are most likely to plan does
allow for some generalizations about non-planning and the
challenges of planning for self-driving cars. Extreme or critical sample selection is often used to generalize from a single
case study (Yin 2008, chap. 2; Flyvbjerg 2006). If the interview sample (fifteen of the twenty-five MPOs contacted) is
further biased, it is likely biased in the intended direction:
Staff from MPOs that are actively planning for and considering self-driving cars were probably more likely to agree to an
interview than staff from MPOs that were not.
Self-Driving Cars and the Long-Range
None of the MPOs most likely to be planning for self-driving
cars have incorporated them into their most recent RTPs.2 Of
the twenty-five largest MPOs, only Philadelphia’s Delaware
Valley Regional Planning Commission mentions autonomous vehicles at all. In a brief sidebar, the plan identifies the
rapid advancement of new technologies like driverless cars
and uncertainty about their timing and impacts as a reason
for regular RTP updates (Delaware Valley Regional Planning
Commission 2013). Since federal law requires regular
updates to qualify for federal transportation funds, the text
reads more like a response to staff and stakeholders’ planning-fatigue than a call to prepare for a transformative, but
uncertain technology. The San Diego Association of
Governments (2011) dedicates a section of its RTP to connected vehicles (the only plan that does), but does not mention autonomous vehicles.
Table 1 summarizes five principal reasons why interviewees indicated that their MPOs have not incorporated driverless
cars into long-range plans. While uncertainty about the timing
and impacts of driverless cars plays the strongest role, the
divide between the emerging technology’s potential impacts
and the way that investment decisions are made featured
prominently in most interviews. Unawareness and skepticism
played only a weak role, though planners were less aware and
more skeptical just several years ago; a testament to the rapid
pace of technological change. The remainder of this section
describes each entry of the summary table in greater detail. It
bears repeating that the interviewees are among the planners
most likely to consider and plan for self-driving cars. Smaller
MPOs have more limited planning capacity and may be less
aware of emerging technologies and less likely to view planning for emerging trends within their mandate.
Awareness of Driverless Vehicle Technologies
Although no MPO incorporated driverless cars into its RTP,
most interviewees are well aware of the latest developments
in automated vehicle technologies, legislation, and research.
Throughout the interviews, they frequently used technical jargon like Level 0 and Level 4—referring to zero and full
autonomy—and V2V and V2I—acronyms referring to vehicle-to-vehicle and vehicle-to-infrastructure telecommunication. Interviewees also referred to recent newspaper articles,
conference sessions, web videos, and academic publications
on self-driving cars. Many not only attend but organize
national and local meetings and conference sessions on
autonomous vehicles. A few have started to develop regional
transportation models to help predict the impacts of self-driving cars. In short, many are following the technology closely
and have intimate knowledge about the current state, likely
near-future, and potential impacts of autonomous vehicles.
Journal of Planning Education and Research 36(2)
Table 1. Summary of the Reasons Interviewed MPO Planners Reported for Not Including Autonomous Vehicles in Most Recent LongRange Regional Transportation Plans.
Very weak
Very strong
Too far
One of many
Interviewees are well aware of the technological progress, regulatory environment, and
potential impacts of driverless cars. That said, many planners were less aware when they began
the RTP process as many as seven or eight years ago.
Most interviewees believe that the impacts are likely, though not certain, to be profound.
Several see the probable impacts as marginal, though still fairly uncertain.
There is a great deal of uncertainty about what technologies will prevail, how much and when
they will penetrate the market, whether regulation will hinder or support deployment, what
the direct impacts will be on capacity or safety, and how consumers will respond. Possibilities
range from a marginal improvement in the comfort and convenience of driving to a radical
transformation in car-ownership and travel patterns with potentially positive and negative
Driverless cars and their potential impacts are too far removed from decisions about whether
and how to invest in and maintain transportation infrastructure.
Vehicle automation is just one of a number of radical changes that could influence regional
transportation over the next 30 years. Staff also mentioned changes in federal transportation
funding, 3D printers, improvements in telecommunications, and the impacts of and policies to
address climate change as potential game-changers.
Despite this current level of awareness, planners update
regional transportation plans every four years, meaning that
the start of the planning process for an existing plan could
have begun as many as eight years prior to most recent official plan. Given the rapid changes in the development of selfdriving cars, levels of awareness were much lower in the
recent past. A few planners mentioned that self-driving cars
had only come onto their radar in the past couple of years.
Impacts Likely within a Long-Range Planning
Most interviewees indicated that self-driving cars will likely
have significant impacts on travel behavior, safety, car-ownership, infrastructure, land-use, and settlement patterns.
Citing the recent work of Google and major car manufacturers, one self-described technology skeptic indicated a belief
that self-driving cars would be transformative in the near
term. Even planners who expected change to be slow and
marginal agreed that driverless cars had the potential to be
rapidly transformative, particularly if encouraged by federal
policies as happened with seatbelts, air bags, fuel efficiency,
and safety glass. Most had a sense that self-driving technologies would be available and mass-marketed within the next
twenty years, but that policy, regulation, and price would all
influence market penetration and thus any effect on road
capacity or traffic safety.
Furthermore, planners were generally cautious about specific predictions. Rather, they described a range of possible
outcomes in terms of how and when automated-vehicle technologies are deployed and how the public responds. To temper statements about likely impacts, one regional planner
cautioned, “We used to predict colonies on the moon.”
Nevertheless, most MPO staff members are best described as
cautiously optimistic, rather than skeptical, that driverless
cars will eventually produce significant road-capacity and
safety improvements.
Uncertainty about Timing and Scale of Impacts
Despite this cautious optimism, the direct and indirect impacts
of self-driving cars are highly uncertain. This uncertainty featured heavily in all fifteen interviews. As one planner who has
spent time coordinating conference sessions on autonomous
vehicles summed up the planning experience, “Yes, there’s a
discussion [about incorporating autonomous vehicles into
planning efforts at our MPO]. We don’t know what the hell to
do about it. It’s like pondering the imponderable.”
In addition to a wide range of outcomes—from just
another marginal improvement in the comfort and safety of
driving to a profound shift in travel behavior and the transportation system—the direction of impacts remains uncertain. For example, self-driving cars could increase vehicle
miles traveled (VMT) by generally lowering the time-costs
of travel and parking and by giving increased mobility to
children, the elderly, the blind, and others restricted from
operating vehicles. On the other hand, driverless cars could
reduce VMT by enabling substantially more car-sharing, better transit, and a shift from paying for vehicles and insurance
in lump sums to paying for each trip or mile driven. Most
interviewees saw significantly higher and significantly lower
VMT as potential outcomes. Some, however, thought both
outcomes were somewhat overblown. One planner stated, “I
guess I’m slightly pessimistic about the transformative
impacts of [driverless cars] and I think slowly cars will be
more and more comfortable to drive.”
Some regional planners spoke at length about attracting
new tech jobs related to driverless cars, while others worried
about unemployed truck and taxi drivers. This split mirrors
the split in a recent Pew poll of experts on whether new technologies, including driverless cars, will create more jobs
than they replace (Smith and Anderson 2014).
In terms of transit, interviewees also had divided views on
the impacts of driverless vehicles. Several expressed concern
that they were investing in expensive rail projects that might
soon become obsolete if autonomous vehicles greatly influence effective road-capacity or travel behavior. One worried
that “If you basically have your own personal rapid transit
vehicle . . . it could draw [choice riders] away from transit
and back into a personal vehicle.” Others saw an opportunity
to improve bus service and last-mile transit provision with
autonomous vehicles. One planner noted that “autonomous
subways are easy, in that we’ve had them for 20 years. But if
we can have autonomous buses, then that really changes the
cost of providing service and service frequency . . . and we
can have smaller vehicles and higher frequencies.”
While planners from one agency thought that new, automated technologies would reinforce the much-reported (and
much-disputed) Millennial generation’s move away from carownership and toward transit use and urban lifestyles (for a
discussion of Millenials’ preferences, see Blumenberg et al.
2012), planners from two other agencies predicted the opposite
effect. They felt that if driverless cars provided the freedom to
operate mobile devices while in the car, then younger, wealthier transit riders would switch to using cars. Another planner
identified both of these outcomes as possible. Similarly, driverless cars could encourage sprawl by reducing the burden of
travel but could also encourage infill and dense development
by reducing the need for parking and the inconvenience of
looking for parking in dense residential neighborhoods.
Planners acknowledged that there are doubtless uncertainties that they have not considered at all. There may be
unintended consequences or totally unexpected behavioral
responses to the new technology. Wondering about how far
the future might deviate from all of the many impacts under
consideration, one planner joked: “I don’t want to hear this
[interview recording] in 20 years.” Four different interviewees brought up texting while driving as an unintended consequence of a new technology that few predicted. Several
fretted about personal privacy and the potential danger of
hacked driverless cars. Technology has already been used to
either crash or hijack military drones (Peterson and Faramarzi
2011) and cause contemporary cars to behave erratically
(Greenberg 2013). The hacking of private vehicles for theft,
mischief, or assault is a serious potential risk and a roadblock
to deploying self-driving cars.
Disconnect from Regional Investment Decisions
Partly as a result of these uncertainties, planners stated that
driverless cars remain far removed from the types of
day-to-day investments and policy decisions that long-range
planning supports and justifies. MPOs have a legal responsibility to develop fiscally constrained plans based on credible
and consistent data to meet specified targets. These plans
tend to be conservative and interviewees consistently stated
that there is insufficient evidence to justify projections of
driverless cars’ influence on congestion, collisions, or fatalities.3 In addition to the threat of losing federal funding, plans
can be the basis for lawsuits related to environmental impacts
and air quality conformity. (For an in-depth discussion about
air quality and regional transportation planning in the San
Francisco Bay Area, see Garrett and Wachs 1996.) One planner indicated that a recent lawsuit had made the agency even
more conservative and less likely to discuss the impacts of
new technologies.
Interviewees routinely described the long-range planning
process as reactive and, as one planner lamented, they tend to
plan in the rear-view mirror. Another stated, “A lot of times
our planning processes are more reactionary [sic] than anticipatory.” However, one planner embraced this reactive nature,
stating that the job of long-range planning is not to predict
the market, but to enable it: “My job is to explore the planning and policy capabilities so that when the private sector’s
ready, we’re an enabler and not an inhibitor.” In any case,
unless an MPO sets aside money for specific projects related
to driverless cars, planners will only tend to address driverless cars in general terms as a potentially impactful technology. This tension between predicting the future and planning
in the present is by no means new to the planning field nor
exclusive to discussions about uncertain new technologies
(see, e.g., Altshuler 1965; Myers and Kitsuse 2000; Quay
2010; Hamin, Gurran, and Emlinger 2014).
Furthermore, planners do not wish to make bad investment decisions based on uncertain technologies. Spending
millions of dollars on smart infrastructure is potentially
wasteful, and no government agency wants to risk betting on
a technology that flops. One planner expressed that a shift to
in-vehicle technologies (like sensors and short-range vehicle-to-vehicle wireless communications) has been a blessing
because it has insulated public agencies from the risks associated with investing in new infrastructure like fiber-optic
cables along highways. Nevertheless, planners recognize
that there are risks associated with continuing to conduct
business as usual. In particular, many worry that planned
highway and rail investments could be obsolete by the time
that they open, if autonomous vehicles greatly influence
effective road-capacity or travel behavior. One said that “the
last [thing] you want to have is 10, 15, 20 years down the
road is highway investment projects that become increasingly less effective or irrelevant.”
Changing the portfolio of planned investments, however,
is politically difficult. As many interviewees noted, planned
projects have constituents who are loath to see them abandoned. One planner described the local planning process as
conformity-driven, rather than planning- or needs-driven:
Journal of Planning Education and Research 36(2)
“Traditionally, one of the things that we’ve done here in this
region with our RTPs is that we’ve really treated them more
as vehicles for achieving conformity so that the money can
continue to flow.” The state and local actors develop a list of
investments and the MPO produces plans in accordance. This
is almost certainly the case for smaller MPOs, where the RTP
is more about federal compliance than regional planning.
Even in regions with more empowered MPOs, planners
stated that it can be extremely difficult to remove a planned
project from the existing pipeline. The MPO’s role is generally to compare and evaluate investment proposals from
stakeholders, not to make proposals. One interviewee likened MPO staff to a “lunchroom monitor,” who makes sure
that cities and towns play nice and work together to agree on
regional priorities and spending plans. Discussing regional
transportation investment decisions, another noted that
“there is a political economy that in many cases appears to
override questions of efficiency or responsible stewardship
of tax dollars.”
and national meetings and conferences about vehicle automation, they also organize them. Several interviewees moderated and attended recent sessions like the Automated
Transportation and Impacts to Planning session at the 2014
meeting of the Association of Metropolitan Planning
Organizations in Atlanta and a breakout session on planning
at the 2015 Automated Vehicles Symposium in Ann Arbor.
This section documents how a subset of regional planners
are beginning to consider self-driving cars by developing
planning scenarios, modeling travel behavior, testing new
transportation technologies (though not yet autonomous
vehicles), and considering changes to regional investment
priorities. In addition to demonstrating the state of best metropolitan planning practice, this section illustrates some of
the challenges of preparing for driverless cars. These challenges almost certainly extend to smaller MPOs, which have
smaller staffs and more constrained planning activities.
One of Many Potentially Impactful Changes
Given the uncertainties around the impacts of driverless cars,
several agencies have opted to incorporate the technology
into scenario planning efforts. For example, the Philadelphia
MPO looked at autonomous vehicles as one of six driving
forces of regional change and considered a number of potential impacts on capacity, safety, and travel behavior (Delaware
Valley Regional Planning Commission 2014). Interviewees
also indicated that they are examining the potential impacts
of self-driving cars with regional SWOT (strengths, weaknesses, opportunities, and threats) analyses, regional operations plans, or organized brainstorming sessions with local
universities, industry leaders, and government officials.
Planners indicated that they tend to consider autonomous
vehicles in concert with other changing technologies like
connected vehicles, smart infrastructure, and improved teleconferencing. They also indicated that new technologies are
just one planning consideration among others like climate
change impacts and decreased federal transportation funding. While these efforts have helped agencies to consider a
range of potential outcomes, they have—as one planner put
it—led to more questions than answers about the future
impacts of self-driving cars.
Finally, in addition to having uncertain impacts that are disconnected from daily investment decisions, self-driving cars
are just one of many technologies and political changes that
may disrupt regional transportation systems over the next
several decades. Interviewees mentioned changes in federal
funding for regional transportation, climate change, natural
disasters, and other new technologies as having uncertain
and potentially significant impacts on regional transportation
systems. Developments in high-quality video conferencing,
for example, could radically reduce the need for business
travel. Drone-delivered freight is looking more and more
possible. One interviewee described how 3D printing could
alter global freight flows, potentially affecting a recent
regional port investment.
One planner summarized the relative importance of
autonomous vehicles: “Right now, it’s one of several forces
that we think are going to be a potential game-changer that
we need to take a closer look at. We’re just not at the point
now where we can start saying, okay these are the four most
important.” In the Philadelphia region, where MPO planners
are developing a set of scenarios to consider potentially highimpact changes in the coming years, automated vehicles are
just one of 32 under consideration (Delaware Valley Regional
Planning Commission 2014).
Planning for Self-Driving Cars
Although only one of the twenty-five largest MPOs even
mentions driverless car technologies in its most recent RTP,
all but a couple of interviewees described one or more planning-related activities, such as focus groups or meetings with
experts, to discuss the technological progress and implications of self-driving cars. Interviewees not only attend local
Developing Scenarios
Modeling Impacts
Modeling regional travel behavior is one of the primary roles
and responsibilities of MPOs. Most interviewees indicated that
the behavioral responses to autonomous vehicles would be
extremely difficult to predict. As one put it, “One of the bigger
challenges we have is [to predict] how will autonomous vehicles change travel behavior, and I have no idea.” Nevertheless,
the San Francisco, Seattle, and Atlanta regions have begun to
test different scenarios involving driverless cars with their
regional activity-based travel models. In a recent conference
paper, the Puget Sound Regional Council’s modelers
Table 2. Summary of Autonomous Vehicle Modeling Scenarios from Interviewed MPOs.
In-vehicle Time Costs
Road Capacity
VMT Change
50% of car
50% of car
50% of car
Same as car
High-quality rail
50% of car
Same as car
65% of car
65% of car
+10% to +100%
+10% to +100%
+0% to +100%
+4% to +5.2%
+6.7% to +7.9%
+13.2% to +14.5%
San Francisco
Key Assumptions
Reduced operating costs
Reduced operating costs and free parking
All scenarios: driver present, though
interventions rare; no intercity travel; same
car ownership levels and urban form
Owned by high-income households only
50% parking cost reduction
No car ownership. Cost is $1.65 per mile.
Sources: Childress et al. (2015); Michael Gucwa (2014); Kim et al. (2015).
Note: MPO = metropolitan planning organization; VMT = vehicle miles traveled.
summarize the current state of modeling: “These scenarios
clearly stretch current model capabilities, and depend on highly
uncertain inputs. However, it is still useful to test the existing
models in order to start a conversation with planners and decision-makers, as well as to highlight shortcomings in our existing methods to modelers” (Childress et al. 2015, 2).
Most modeling scenarios from the three regions have produced somewhat similar predictions, with 5 to 20 percent
increases in regional VMT and associated decreases in nonmotorized modes and public transportation. Table 2 summarizes the scenarios from each region and their predicted
influence on regional VMT. In most scenarios, autonomous
vehicles influence VMT by making it less burdensome to
drive (since passengers can sleep or work in the vehicle),
increasing road capacity, and reducing parking costs.
The only scenario that predicts lower VMT includes a
per-mile vehicle charge of $1.65 (the equivalent of $33 at the
pump for a car that gets 20 miles per gallon and close to the
price of a taxi) and zero car-ownership. Given the low current taxi mode share, this alternative scenario seems unlikely.
Nevertheless, it is an attempt to capture a future scenario in
which autonomous vehicles lead to a revolution in car ownership, decreased driving, and increased walking and transit
ridership using an existing model. Furthermore, the predicted
effects of increased road capacity on VMT are also much
lower than might be expected. It is unlikely that doubling
effective road capacity would only increase VMT by 2 to 12
percent.4 Existing empirical studies suggest that a 1 percent
increase in highway capacity leads to an increase in VMT
somewhere between 0.6 and 1 percent (Cervero and Hansen
2002; Cervero 2003; Duranton and Turner 2011).
However, travel models rely on observed trends and are
conservative by nature. As one MPO modeler explained, “If
the taxi mode share is zero and [autonomous taxis] save on
labor, my first approximation of the automated taxi mode
share would also be zero.” Without radical changes to the
modeling assumptions, existing models are unlikely to
predict radical responses to driverless cars. This may reflect
a behavioral reality, but probably also a limitation of ongoing
attempts to predict behavioral responses to self-driving cars.
Testing New Technologies
At present, MPO planners are not significantly involved in
automated vehicle testing, which happens on closed facilities or unsupervised public rights-of-way in the states that
allow testing.5 At least two regions have test beds where
they are starting to analyze the impacts of other new transportation technologies, such as vehicle-to-infrastructure
communications. In Pittsburgh, MPO planners are working
with the State DOT and researchers from Carnegie Mellon
to install and test vehicle sensors and a high-tech traffic
signal system that will provide additional information about
the potential benefits of automating more transportation
functions. The San Diego Association of Governments,
which unlike most other MPOs builds and operates regional
Intelligent Transportation Systems (a somewhat catch-all
term for information and communications technologies
applied to transportation), has used managed lanes to test
wireless vehicle communications since 1998 and is continuing to test vehicle-to-vehicle and vehicle-to-infrastructure technologies.
Only one regional planner discussed testing autonomous
vehicle technologies at any length. In Dallas, the MPO’s
Director of Transportation expects that private sector firms,
particularly trucking companies, and local universities will
begin to submit requests to test driverless technologies on a
new barrier-separated thirty-mile test corridor that runs down
the middle of I-30 between Dallas and Arlington. He described
the MPO’s role as being able to support and facilitate these
types of requests, rather than develop its own tests or pilot
projects. MPOs will only likely get significantly involved in
testing driverless cars if testing involves a large regional infrastructure investment or uses an existing regional test facility.
Changing Spending Priorities
To date, the prospect of autonomous vehicles has had little to
no influence on MPOs’ investment decisions. Nevertheless,
one MPO used the potential capacity improvements from
autonomous vehicles to help justify the demotion of a
regional road expansion from a funding priority to a regionally desired project. This may seem like a subtle semantic
difference, but it likely prevents the project from ever being
built. However, interviewees said that this same decision
would likely have been made without considering self-driving cars. At the request of a stakeholder city, another MPO is
trying to analyze whether the advent of self-driving cars
means that the region should continue with plans to build
managed lanes. Several interviewees worried that a number
of currently planned investments might be unnecessary if
driverless cars increase effective roadway capacity.
As research, pilot projects, and modeling efforts provide
more information on the probable impacts of self-driving cars,
interviewees indicated that investment portfolios will likely
begin to change at the margin. Staff from several MPOs used
the same analogy of moving a large ship by a few degrees.
Most thought that by increasing effective road capacity, driverless cars would encourage a shift away from spending on
regional highway expansion. Many also hoped that driverless
cars would help them reduce capital expenditures in an
increasingly funding-constrained planning environment.
In terms of the safety impacts of driverless cars, planners
indicated that automation might become an important component of setting and meeting required safety targets.
Improving safety is an increasingly important component of
national transportation policy and MPOs are responsible for
setting and meeting regional safety objectives. As with seatbelts and airbags, certain automated or connected-vehicle
features will almost certainly be required, if safety benefits
are significant and the technology is modestly priced. To the
extent that these features are vehicle-based, MPOs will have
a limited role in implementing or requiring them. If, by contrast, they require significant infrastructure investments,
MPOs will take a lead role in planning and investing in new
safety-related technologies.6
Summary and Policy Recommendations
I began this article with an assertion that the planning profession has a history of failing to prepare for new transportation
technologies because of misunderstanding the technologies
and focusing on the planning issues of the day rather than the
planning issues of the future. A review of the RTPs of the
nation’s twenty-five largest metropolitan areas finds that none
has incorporated self-driving cars and only one mentions the
new technology. This is almost certainly not because the planners are unaware or skeptical about self-driving cars. Instead,
uncertainty about autonomous vehicles’ impacts and timing
and the gap between potential impacts and day-to-day
Journal of Planning Education and Research 36(2)
investment decisions are the principal reasons that self-driving
cars do not feature in RTPs. Since these twenty-five MPOs are
among the most likely to be planning for self-driving cars,
smaller MPOs are unlikely to have done more. This section
summarizes how well prepared planners are for self-driving
cars, briefly discusses how future plans are likely to address
them, and concludes with a question about the value of planning for self-driving cars and three policy recommendations.
Planners from some (though certainly not all) of the largest MPOs are at the forefront of planning for self-driving
cars. They attend, host, and organize conferences and focus
groups on new transportation technologies and their implications for cities, regions, and transportation systems. Three
regions—Atlanta, San Francisco, and Seattle—have produced some of the first and most advanced models to predict
how autonomous vehicles might affect travel behavior and
the regional transportation system. Others, like Pittsburgh
and San Diego, are involved in testing new transportation
technologies, though the focus has been on connected, rather
than autonomous, vehicles.
Despite these thoughtful planning efforts, large MPO
planners (and by extension most other planners) are unsure
how to plan for self-driving cars. Interviewees generally felt
that existing research does not yet provide sufficient, actionable information to direct investments or planning priorities.
Asked what type of knowledge is required, most expressed a
range of needs including knowledge about the impacts on
capacity, traffic safety, land use, and travel behavior. In short,
although planners follow existing research and develop modeling and other scenarios, most do not see the findings as
sufficiently concrete or certain. Several described a need for
robust pilot projects before new technologies can start to
have an impact on regional investments. Unfortunately, the
extent and direction self-driving cars’ impacts, particularly if
transformative, are unlikely to be fully understood until they
have already started to happen.
Nevertheless, MPOs are also more likely to discuss selfdriving cars in their next plans than the previous ones. Instead
of making concrete projections or investment decisions, the
RTPs will include autonomous vehicles as one of a number
of potentially transformative technological and social
changes that will have uncertain impacts. To quote one planner, “My guess is that if you did a word search [on the next
RTP], you would find [driverless cars], but more than likely
it would be used as an example for setting the framework for
changes in technology that infrastructure development need
to be aware of.”
As more information becomes available about the likely
timing and impacts of self-driving cars, MPOs are likely to
respond by reducing investments in highway expansion and
shifting more of their budgets from capital investments to
operations. However, three factors will likely temper the
influence of self-driving cars—even if transformative—on
roadway investments in large metropolitan regions. First,
interviewees indicated that they already spend the majority
of their budgets on operations; in older regions this is as high
as 90 percent of the budget. Second, most interviewees stated
that their organizations are spending an increasing share of
remaining investment dollars on transit and nonmotorized
modes instead of highways. None expected self-driving cars
to change this trajectory. Third and perhaps most importantly, planning and analysis play a constrained role in project selection and once an investment has been selected, it is
difficult to remove from the pipeline of regional projects.
The first two reasons likely do not extend to smaller
MPOs outside of the interview sample. Nationally, the total
and federal share of transportation expenditures on highway
capital investments increased modestly from 1995 to 2012
(Bureau of Transportation Statistics 2014, n. Table 21-A &
22-A). And interviewees indicated that driverless cars have a
much greater potential to change investment priorities in
smaller, faster-growing regions than older ones. An interviewee from one such region stated, “We’re still rapidly
growing and we’ve put a lot of roadway infrastructure down
in the last 25 years. If you look forward over the next 25
years with the prospect of autonomous vehicles . . . it’s gonna
reduce the need for additional capacity in the future.”
Another, from a larger but still rapidly growing Sunbelt
region, stated, “We have a million people coming every
decade for the last four decades and for the next three, so
[increases in capacity] would reduce the size, ugliness, and
the number of corridors [on our highway system].” Even in
older regions, many highways are reaching the end of their
service life and will need major repairs or replacement.
Improvements in effective road-capacity due to autonomous
vehicles could influence how these regions choose to rebuild.
Policy Recommendations
Should planners be doing more to prepare for self-driving
cars? On the one hand, the timing, scale, and direction of the
impacts are uncertain and the opportunities to influence
investment decisions are limited. Furthermore, planners will
likely have time to adjust and modify plans as the impacts of
autonomous vehicles become more reliably estimable—new
technologies are expensive and the average passenger car is
more than 11 years old (U.S. Department of Transportation
2013b). Even if transformative, the fleet will take at least a
decade to incorporate significant numbers of driverless cars.
On the other hand, the new technology will almost certainly
impact regional travel behavior and transportation systems
within the time frame of contemporary long-range plans.
Even with modest market penetration, these impacts could
be significant. The freight, taxi, and transit industries, in particular, could change rapidly and radically, with significant
metropolitan impacts.
Large MPO planners recognize these challenges and—
although they would generally like to do more to plan for
self-driving cars—are cautious about making unsubstantiated projections or premature investment decisions. Not only
is it difficult to predict when and how the technology will
arrive and what its impacts will be, planners have a limited
role in directing investment priorities. Furthermore, there are
many other regional priorities and changing technological,
environmental, and social trends to consider. Despite some
uncertainty about whether and when to plan in earnest for
autonomous vehicles, I conclude this paper with three recommendations for and based on contemporary long-range
planning efforts for self-driving cars.
1. Beware the rosy future. Like the mass-produced automobile or limited-access highway, driverless cars will almost
certainly create, as well as resolve, a number of planning
problems. Planners should be careful not to expect self-driving cars to solve congestion, traffic accidents, or pollution
problems. Early models predict that self-driving cars are
likely to increase vehicle travel modestly. If unoccupied
vehicles circle indefinitely for free parking and run errands,
any safety and congestion benefits may be substantially or
entirely offset. Some planners worry about large shifts from
walking, biking, and transit to self-driving cars. There are
also significant potential land use and equity implications.
While most interviewees emphasized the importance of
considering negative impacts, several painted an overly rosy
picture of the future of self-driving cars. These interviewees
tended to conflate driverless cars with electric vehicles,
slower more fuel-efficient travel speeds, and a form of ubiquitous, personal rapid transit. Most, however, advised caution and conservativism when predicting self-driving cars’
ability to improve air quality, congestion, or traffic collisions. One interviewee stressed the importance of asking,
“What possible things could this bring about that we aren’t
considering and are going to be the planning problem of
tomorrow?” Another emphasized that “There are always
unintended consequences.” This level of caution is appropriate, given the technological, regulatory, and behavioral
uncertainties related to autonomous vehicles.
2. Plan outside the RTP (or other rigid planning processes). The RTP
process is too constrained to plan for an uncertain, but potentially transformative, technology like self-driving cars. All
MPOs, regardless of size or geography, need to develop and
regularly update the RTP, based on a set of regional targets and
quantifiable and observable trends. Failure to meet the requirements of the RTP can result in the withholding of federal transportation funds. This type of planning may have worked well
during the interstate highway program, when VMT rose nationally at a fairly steady rate, but is not well suited to changing
trends or radical transformations. Several interviewees indicated that these requirements made plans conformity-driven and
more about the recent past than the future.
In order to plan for self-driving cars, planners need to go
beyond conformity-driven processes like the RTP, to test and
evaluate a range of potential outcomes. This will not be an issue
for the largest MPOs, which already engage in wide-ranging
Journal of Planning Education and Research 36(2)
planning activities (mostly unrelated to autonomous vehicles)
that go well beyond the requirements of the RTP. Smaller
MPOs, however, will struggle to develop robust models or scenarios. Many only have a single transportation planner or engineer on staff and focus on conformity by necessity.
If planning for driverless cars is indeed a desirable goal,
then smaller MPOs will need federal guidance and some
easy-to-implement rules of thumb. Early modeling and scenario-planning efforts of the largest MPOs suggest that selfdriving cars will not fit neatly into long-range trend
projections. Instead, a range of outcomes should be considered. Given limited resources, planners interested in considering the impacts of self-driving cars could consider two
potential outcomes: one where driving rates, effective road
capacity, and travel increase substantially and another where
shared autonomous taxis replace private cars and public
buses, particularly in more urban areas. While neither scenario will necessarily make it into the RTP, plans that consider both scenarios will be more robust to many of the
uncertain impacts of self-driving cars.
3. Consider investment risks. Given the state of existing
knowledge and uncertainty about impacts, considerations of
vehicle automation should play a limited and complementary
role in decision making. At this stage, it simply does not
make sense to fund or not fund investments based on an
uncertain future technology that will have uncertain, though
potentially transformative, impacts. As two planners concluded, finishing each other’s sentences: “It’s not entirely
actionable at this point. We can shift direction a few degrees
to the left or right, but we can’t start to really turn the boat
yet, and certainly not stake specific investments on [autonomous vehicles], and [deciding between investments], at the
end of the day, is certainly what we’re trying to do with the
[long-range] plan.” Similar to planning for the uncertainties
of climate change, planners could nevertheless consider the
robustness of investment decisions to a range of potential
autonomous vehicle scenarios.
For example, new investments in suburban rail or highway expansion will tend to perform less well when
considering the range of impacts from driverless cars than
when not considering them. Capacity expansions tend to
take a long time to plan and build; and the longer the project
horizon, the more likely that driverless cars will have altered
travel behavior and the transportation system. Furthermore,
driverless cars will probably have the biggest and earliest
impacts on freeway capacity, where the task of automation
is far simpler, and lower volume transit, where automated
features could help buses provide more rail-like features,
such as better on-time performance, higher frequencies, and
faster average speeds.
By contrast, uncertainty about the timing and impacts of
self-driving cars has almost no influence on whether to repair
a road bridge. Bridge repairs will likely be completed in a
shorter time frame than a capacity expansion. More importantly, the value of a repaired bridge is far less dependent on
whether automation leads to a revolution in travel behavior
or provides just another marginal improvement in the safety,
comfort, and convenience of driving. Investments in bicycle
and pedestrian infrastructure are also somewhat insulated
from the risks associated with driverless car technologies. If
the technology leads to a shared revolution in urban mobility,
the nonmotorized mode share will likely increase and vehicles will need less space. If the technology leads to a marginal increase in the safety and comfort of driving, however,
none of the original motivations to invest in bicycle or pedestrian facilities will have gone away.
Whatever the actual future impacts of self-driving cars, the
potential impacts will almost certainly be used to argue for
and against an array of investments and investment types. In
St. Petersburg, FL, opponents have already used the promise
of self-driving cars to help defeat a public referendum to fund
a light-rail investment and expand bus service (Morris 2014).
As new technological breakthroughs occur, more automated
features become commercially available, and public awareness about vehicle automation increases, self-driving cars
will have an increasing influence on public discourse and
investment decisions. By getting ahead of this discourse,
planners will have a greater role in shaping how driverless
cars influence regional transportation investments.
Appendix A. List of MPOs Contacted and Interviewed and Date of the Most Recently Adopted RTP (as of March 2014).
Atlanta Regional Commission
Baltimore Metropolitan Council
Charlotte Regional Transportation Planning Organization
Chicago Metropolitan Agency for Planning
North Central Texas Council of Governments
Houston-Galveston Area Council
Plan Yeara
Appendix A (continued)
Los Angeles
San Diego
San Francisco
New York
San Antonio
St. Louis
Washington, D.C.
Southern California Association of Governments
Metropolitan Council
MetroPlan Orlando
Delaware Valley Regional Planning Association
Maricopa Association of Governments
Southwestern Pennsylvania Commission
San Diego Association of Governments
Metropolitan Transportation Commission
Puget Sound Regional Council
Denver Regional Council of Governments
South East Michigan Council of Governments
Boston Region Metropolitan Planning Organization
Miami-Dade Metropolitan Planning Organization
New York Metropolitan Transportation Council
San Antonio-Bexar County Metropolitan Planning Organization
(now Alamo Area Metropolitan Planning Organization)
East-West Gateway Council of Governments
Hillsborough Metropolitan Planning Organization
Metropolitan Washington Council of Governments
Plan Yeara
Emailed response
Emailed response
Not interviewed
Not interviewed
Not interviewed
Not interviewed
Not interviewed
Not interviewed
Not interviewed
Not interviewed
Note: MPO = metropolitan planning organization.
a. Year of original adoption. Many plans have had updates, which were also examined.
Appendix B. List of MPO Staff Interviewed by Region and Planning Organization.
Los Angeles
Los Angeles
Atlanta Regional Commission
Atlanta Regional Commission
Baltimore Metropolitan Council
Charlotte Regional Transportation Planning
Chicago Metropolitan Agency for Planning
David Haynes
John Orr
Terry Freeland
Robert Cook
Regional Planning Manager
Transportation, Access and Mobility Manager
Senior Transportation Planner – Policy
CRTPO Secretary
Kermit Wies
North Central Texas Council of
Houston-Galveston Area Council
Southern California Association of
Metropolitan Council
MetroPlan Orlando
Patrick Mandapaka
Michael Morris
Isaac Ramirez
Naresh Amatya
Ryan Kuo
Jonathan Ehrlich
Eric Hill
Nikhila Rose
Michael Boyer
Deputy Executive Director for Research and
Principal Planner
Director of Transportation
Chief Transportation Planner
Transportation Planning Manager
Program Manager
Senior Planner
Director, System Management & Operations
Transportation Planner
Manager, Office of Long-Range Planning &
Economic Coordination
Senior Transportation Planner
Manager, Office of Energy and Climate Change
Manager, Office of Transportation Operations
Associate Director, Systems Planning
Transportation Director
Project Manager, Regional Traffic-Signal Program
Connected Vehicle Program Manager
Principal, Planning
Director of Planning
Delaware Valley Regional Planning
Brett Fusco
Robert Graff
San Diego
San Francisco
Laurie Matkowski
Christopher Puchalsky
Eric Anderson
Domenic D’Andrea
Peter Thompson
David Ory
Charlie Howard
Maricopa Association of Governments
Southwestern Pennsylvania Commission
San Diego Association of Governments
Metropolitan Transportation Commission
Puget Sound Regional Council
Note: MPO = metropolitan planning organization.
Journal of Planning Education and Research 36(2)
Karen Frick, Eric Morris, Megan Ryerson, Scott Smith, and three
anonymous reviewers provided substantial and helpful suggestions
to improve the original manuscript.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
The author disclosed receipt of the following financial support for the
research, authorship, and/or publication of this article: Technologies
for Safe and Efficient Transportation, a U.S. Department of
Transportation University Research Center.
The National Highway Traffic Safety Administration has proposed rules to require car manufacturers to enable wireless
communications with other vehicles and/or infrastructure to
collect information, avoid collisions, and improve system performance (U.S. Department of Transportation 2014a, 2014b).
At the suggestion of an interviewee, I also looked at the Las
Vegas region’s regional transportation plan (RTP), since
Nevada was the first state to allow testing of autonomous
vehicles. The RTP does not mention driverless cars.
One planner mentioned that it might be acceptable to treat
autonomous vehicles as the latest in an ongoing trend of technologies that have helped lower fatality rates (like airbags) or
vehicle miles traveled (like telecommuting).
The 12 percent figure comes from extrapolating the Seattle
region’s model of a 3.6 percent increase in VMT from a 30
percent increase in capacity.
State Departments of Motor Vehicles are generally responsible
for regulating and issuing test licenses.
Interviewees tended to focus much more on congestion reduction than traffic safety. Even when pushed to discuss the safety
implications, the majority of MPO planners talked more about
how reducing collisions would impact congestion than injuries
and fatalities. This suggests that setting and meeting regional
road-safety goals has not yet become as embedded into MPO
planning as setting and meeting regional air quality, congestion, or level-of-service goals.
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Author Biography
Erick Guerra is an assistant professor in the Department of City
and Regional Planning at the University of Pennsylvania, where he
teaches courses in transportation planning and planning methods.
His research focuses on the relationship between transportation
infrastructure, land use, urban development, and travel behavior.

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