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IN DEGREE PROJECT TECHNOLOGY,FIRST CYCLE, 15 CREDITS
, STOCKHOLM SWEDEN 2018
Estimating the Potential Spatial Implications of Shared Autonomous Vehicles
A Case Study of Stockholm
MARTIN GUTSCH
KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT
TRITA TRITA-ABE-MBT-18434
www.kth.se
Foreword Since I started the bachelor program in Civil Engineering at KTH, I have been interested in
the future of cities and the possible disruptive phenomena that will change the way we think
about cities. From discussions with friends and teachers, as well as online lectures, I’ve
become interested in the potentially disruptive changes stemming from the introduction of the
autonomous vehicle, and especially the shared autonomous vehicle. This thesis is my
exploration of how this change could affect Stockholm, and what could be done with the
potential for reallocation of space. Through my work I have had great help from my supervisor Andrew Karvonen, whose
positive spirit and insightful comments both got me started and kept me going. Without
Andrew, this thesis had not been done, and I am very grateful for all his help. Norrköping, July 2018 Martin Gutsch
Abstract The area of autonomous vehicles is relatively new and not too many new studies have been
produced. Autonomous vehicles do however have an incredibly disruptive potential to alter
our cities. Through three scenarios constructed from the current literature on autonomous
vehicles, this study will examine the potential for reallocation of space from cars to pedestrian
or other uses made possible by the adoption of autonomous, and shared autonomous vehicles
in particular. Once the three scenarios were constructed, three areas were chosen to examine how they
would be impacted from each of the scenarios. Using examples of urban space reclamation
projects from other cities, examples of potential new uses were constructed. The results of this study are that the potential for reallocation is indeed substantial, but that it
varies with the adoption of autonomous vehicles and shared autonomous vehicles.
Abstract
Autonoma fordon är relativt nytt och antalet nya studier som har producerats på området är
lågt. Autonoma fordon har emellertid en oerhört stor potential att förändra våra städer. Genom
tre scenarier som bygger på den nuvarande litteraturen om autonoma fordon undersöker denna
uppsats potentialen för den omfördelning av utrymme från bilar till fotgängare eller andra
användningsområden som möjliggörs genom autonoma och autonoma fordon i synnerhet.
Tre scenarier konstrueras utifrån tillgänglig litteratur och tre områden väljs för att undersöka
hur de skulle påverkas av vart och ett av scenarierna. Med hjälp av exempel på projekt från
andra städer med syfte att återföra gatutrymme från bilar till andra ändamål skapades exempel
på potentiella nya användningsområden.
Resultaten av denna studie är att potentialen för omfördelning verkligen är stor, men att den
varierar med antagandet av autonoma fordon och delade autonoma fordon.
Table of contents Foreword ............................................................................................................................................. 1
Abstract ............................................................................................................................................... 2
Abstract ............................................................................................................................................... 2
Introduction ............................................................................................................................................. 1
Background .......................................................................................................................................... 2
Projects for reclaiming automobile infrastructure .................................................................................. 3
Barcelona’s Superblocks ...................................................................................................................... 3
Copenhagen’s Strøget ......................................................................................................................... 4
Stockholm’s summer walking streets (Levande Stockholm) ............................................................... 4
Literature review ..................................................................................................................................... 5
What are autonomous vehicles? ......................................................................................................... 5
Change in automobile use ................................................................................................................... 6
Benefits of highly automated vehicles ................................................................................................ 6
Impacts on Urban Space and Traffic Flow ........................................................................................... 7
Road use .............................................................................................................................................. 7
Highway ........................................................................................................................................... 7
Arterial streets ................................................................................................................................. 8
Parking ............................................................................................................................................. 8
Smaller vehicles ............................................................................................................................... 9
Summary of results.............................................................................................................................. 9
Arterial streets ................................................................................................................................. 9
Highways ......................................................................................................................................... 9
Parking ............................................................................................................................................. 9
Change in VMT ................................................................................................................................ 9
Methodology ......................................................................................................................................... 10
Selection of representative streets in Stockholm ............................................................................. 12
Odengatan/Sveavägen neighbourhood ........................................................................................ 13
Kungsgatan .................................................................................................................................... 13
Olofsgatan ..................................................................................................................................... 14
Findings ................................................................................................................................................. 15
Change in urban space due to AV implementation .......................................................................... 15
Possible scenarios .............................................................................................................................. 15
Stockholm today ................................................................................................................................ 15
Baseline scenario ............................................................................................................................... 16
Low adoption of SAVs ........................................................................................................................ 16
Moderate adoption of SAVs .............................................................................................................. 16
High adoption of SAVs ....................................................................................................................... 16
Summary ........................................................................................................................................... 17
GIS Analysis ........................................................................................................................................ 18
Odengatan/Sveavägen .................................................................................................................. 18
Kungsgatan .................................................................................................................................... 19
Olofsgatan ..................................................................................................................................... 20
Scenarios for the locations ................................................................................................................ 21
Odengatan/Sveavägen .................................................................................................................. 22
Kungsgatan .................................................................................................................................... 22
Olofsgatan ..................................................................................................................................... 22
Conclusion ............................................................................................................................................. 23
References ............................................................................................................................................. 24
Image sources .................................................................................................................................... 25
1
Introduction Our cities grew from a need to meet and trade with each other. They went from small
gatherings of houses with dirt roads, to larger cities with paved streets and separate districts,
into the cities we know and live in today, with hundreds of thousands to even millions of
inhabitants, covering vast areas with buildings, roads and public space. The transport need of
cities grew with them. From walking across the street to the next house or bringing your
wares to town on small roads, to horse and carriage on paved roads and the invention of trains
and streetcars all the way to the invention of the car. The car was a monumental shift in
transportation, allowing the average family to travel anywhere they wished and helping
commerce flourish. We owe a lot of our development to cars.
But widespread car adoption comes with several costs and drawbacks. Every year about 1.3
million people die from car related accidents, and road accidents are estimated at a whopping
price tag of 518 billion dollars annually. Apart from accidents, cars pollute both the
environment and degrade local air quality by emissions of pollutants such as CO2 and
particles. They create noise pollution and encourage urban sprawl. But one overlooked factor
that we hardly ever reflect upon, because we have grown used to it, is the enormous space that
cars and their infrastructure require. Cars take up a lot of space. They need space for
highways. They need space for city roads. They even need space when we’re not using them,
sitting idle in parking lots. Except for parks, squares, sidewalks and the occasional bike or bus
lane, cities are either for buildings or cars.
Figure 1: The space occupied by cars highlighted in orange
The spatial impact of cars has only become stronger the more prevalent the car has become.
But there seems to be a possible paradigm shift coming up. Cities are becoming more and
more aware of the negative effects of cars, and car reclamation projects are on the rise.
Barcelona is implementing superblocks to reduce the space dedicated to cars and Stockholm
recently started the summer walking streets initiative, closing some streets to traffic and
parking and dedicating the streets to pedestrians, bicyclists, and parklets or outdoor seating for
restaurants and cafés. Copenhagen has had a major street dedicated to pedestrians and
bicyclists for decades, with infrastructure suited to those needs.
With the invention of the autonomous car, there is hope that we can drastically alter the
impact of car travel on our cities. The shared autonomous vehicle in particular promises big
changes, removing the idle time the vehicle has while parked. Autonomous vehicles could
drive more efficiently than humans. They could park at a different location than where the
passenger exits the car, moving parking spaces from high traffic areas to peripheral areas.
And if we’re prepared to share, we could reduce the number of cars drastically, since
autonomous cars will rarely have to stay parked. All in all, these are changes that are poised to
drastically alter the way we experience our cities.
2
The purpose of this thesis is to examine the potential spatial implications of autonomous
vehicles on the urban landscape. It will examine how potential reductions in vehicle miles
travelled, number of cars in the fleet and required parking spaces, as well as increases in lane
capacities and ride sharing, will affect urban space. It will use the findings of the literature
review to create scenarios with varying degrees of adoption of shared autonomous vehicles
and use the examples of Barcelona, Copenhagen and Stockholm to suggest alternative uses
for reallocated land from vehicular transportation and parking. This thesis will only be
concerning the automation of cars for transport of people, other forms of transportation such
as freight trucks, delivery vehicles etc., while interesting, will be left to other studies.
Questions that will be researched:
How will shared autonomous vehicles change automobile use?
How will the implementation of shared autonomous vehicles change the use of urban
space?
What can be done with the potential reallocation of space from vehicular transport to
other uses?
Background The car has been a great driver of progress throughout the 20th century, but we are now
waking up to the hangover. Cars take up a lot of space both when in use on the road, when
parked on the side of it, or in dedicated parking structures. They emit pollutions that both
reduce the quality of life and even shorten it. Every year, people are hurt, crippled or killed in
car accidents. Roads are barriers that have to be crossed on dedicated crossings, impairing the
flow of pedestrians. All in all, the negative aspects of the car are abundant. But we are
beginning to find solutions to this problem. In cities around the world, local planning
authorities are trying to limit the impact of cars on urban space.
Barcelona is working on a project called “Superblocks”, a project whose aim is to group
together adjacent city blocks into a superblock, prohibiting through traffic and reducing on
street parking on streets within the superblock, directing it to the outskirts of the superblock.
This frees up space inside the superblock to other uses such as expanding pedestrian space or
providing space for outdoor seating.
Copenhagen’s high street Strøget was among the first streets in the world to be transformed
from a street for cars into a pedestrian street. Today, Strøget is a popular shopping street, with
many of the more luxurious store located there. It is also a popular tourist attraction.
The Stockholm initiative “Levande Stockholm” is the city’s attempt at reclaiming urban space
from vehicles and giving it back to pedestrians as walking streets, outdoor seating for
restaurants or parklets. One of the components of the project, called “Sommargågator”
(summer walking streets) is closing streets for car traffic, placing furniture and green plants,
and encouraging bars and restaurants to set up outdoor seating, making the street a livelier
place.
3
Autonomous vehicles and shared autonomous vehicles could give these kinds of projects a
helping push. Autonomous vehicles could drive more efficiently than human drivers,
increasing the capacity of roads and therefore making it easier to divert traffic from one street
to other streets to reallocate the space as pedestrian, park space or other. Shared autonomous
vehicles wouldn’t have to be parked, instead continuing to drive passengers, reducing the
need for parking. Ride sharing would reduce the total number of cars and vehicle miles
travelled. But very few studies have been done to research potential spatial implications of
AVs. This study will attempt to contribute to this area.
Projects for reclaiming automobile infrastructure
Barcelona’s Superblocks
Barcelona has begun implementing their plan for what they call “Superilles”, superblocks.
The concept of a superblock comes from stringing together several normal city blocks and
prohibiting through traffic, creating a space for pedestrians and cyclists. The newly created
space can then be filled with green plants, cafés and parklets, enhancing the pedestrian
experience and lessening pollution effects. The project is an effort to reduce vehicle travel in the city, and to give back space to
pedestrians. One of the major reasons that the project started was that Barcelona repeatedly
failed to meet air quality targets set by the EU. The pollution from cars was so severe that an
estimated 1200 deaths could be avoided if the EU targets were reached. The superblocks in the Eixample neighbourhood would be about 400 by 400 meters large,
consisting of 3 by 3 regular city blocks. The population of such a superblock would be around
5500 people. With an average road width of about 10 meters, that would free up around
15600 square meters, an average of about 3 square meters per resident. If even a fraction of
this newly recovered space was used for new green areas or parklets, this would be a major
improvement for Eixample’s 1.85 square meters of green space per inhabitant.
Figure 2: Superblock structure
4
Copenhagen’s Strøget
Strøget is a large main street in Copenhagen, consisting of four streets; Nygade,
Vimmelskaftet, Fredriksbergsgade and Østergade. There are also three squares, Gammeltorv,
Nytorv and Amagertorv. It’s been an historically important street, but with the adoption of the
automobile in the early 20th century the street became more and more car oriented. However,
following the example of other projects where streets were turned back into pedestrian streets,
Strøget was closed to car traffic in 1962, thus creating a pedestrian street. To emphasize the
change from a car dominated street towards a pedestrian street, planners used new paving and
cobblestone.
Figure 3: Section of Strøget
Stockholm’s summer walking streets (Levande Stockholm)
Since 2016, the city of Stockholm
closes some streets to cars every
summer, dedicating the street to
pedestrians, cyclists, green space
and outdoor seating for restaurants
and cafés. This is a step in trying
to make the city more attractive
for pedestrians, while it also
lessens both pollution and noise
pollution in the area. The concept
of summer walking streets has
been expanding every year since
its start, so a potential for
reallocation of roads to pedestrian,
cycling and commercial uses from
the inception of autonomous
vehicles would fit well with the
expansion of “Levande Stockholm”.
Figure 4: View of Rörstrandsgatan, a summer walking street
5
Literature review
What are autonomous vehicles?
Autonomous vehicles (AVs) are vehicles that to some degree can handle the tasks that a
human driver normally takes care of. According to SAE (SAE INTERNATIONAL and
J3016, 2014), there are 5 levels of autonomous cars, as well as level 0 which is the
conventional driven vehicle (CDV) fully operated by a human. In summary, level 1 might
have systems like adaptive cruise control (ACC), which is a function that could adjust the
speed to the vehicle in front. Level 2 can control steering and speed at the same time, but only
for shorter periods. Level 3 is essentially autonomous but will give back control to the human
driver if the system can’t function properly in a situation. A working example of this level is
Tesla’s Autopilot. Level 4 is even more autonomous, and requires no human input, instead
bringing itself to a halt if a situation becomes too difficult. Level 5 is complete autonomy and
requires no human input whatsoever. It isn’t until levels 4 and 5 that many of the benefits
discussed in this thesis will begin to emerge.
Figure 5: Levels of automation
6
Change in automobile use
AVs and SAVs are theorized to increase car VMT by enabling people who can’t drive to use
cars, as well as by reducing the cost of travel time, making the car a more attractive mode of
transport. For those who don’t want to invest in a car, automation of taxis would reduce their
cost of travel, thus making it a more affordable option (Litman, 2018, p. 9).
As people reach retirement age, they drive less. They also drive less due to age related
illnesses. They might still travel by car, but they will be driven by chauffeurs. The decrease in
elderly travel that stem from illness or the cost/inconvenience of chauffeurs might disappear
when AVs eliminates those barriers to vehicle travel (Wadud, et al., 2016, p. 9). The study
estimates AVs could also increase VMT due to a reduction in cost of driver’s time. The
increase is found to be about 60% for full (level 4) automation (Wadud, et al., 2016, p. 9).
Today, adolescents too young to drive or people with disabilities who can’t drive themselves
have to be chauffeured or find other means of travel. AVs will enable them to travel by car,
increasing total VMT. However, even though these two demographics represent about 10-
30% of the population, the increase from AVs are predicted as just a few percent.
This suggests a pent-up demand for vehicle trips that could serve kids, adolescents and
elderly. The implementation of AVs could enable these age groups to travel more. Wadud, et
al. (2016, pp. 9-10) estimates that the increase in vehicle travel of the elderly and adolescents
over the age of 16 from the implementation of AVs to be around 2-10%. Adding to that the
estimated increase of about 2-5% for disabled people and adolescents below 16, AVs would
increase the total VMT by about 4-15%.
Fagnant & Kara (2015, p. 172) estimates that increases in capacity will increase VMT by 26%
at a 90% adoption of AVs. (Bierstedt, et al., 2014) estimates the VMT increase from AV
adoption at 95% to be 35% on some parts of the transport network. While AVs are likely to
increase overall VMT, SAVs could lessen the effects of that increase. According to (Litman,
2018, p. 21), households who enter car sharing arrangements reduce their VMT with about
25-75%.
Benefits of highly automated vehicles
Litman (2018) discusses several benefits of implementing AVs. Among them are increased
mobility for individuals who for some reason can’t drive themselves, potential reductions in
crashes and accidents due to the removal of human error, decreases in pollution from
increased fuel efficiency and, one of the most important factors for this study, the facilitation
of carsharing and ridesharing. So how does AVs change anything from conventionally driven vehicles? Personally owned
AVs would still use as many cars to do the transport work of conventionally driven vehicles.
The strength of AVs come from their ability to communicate with each other and react to
events faster than humans can. This means they can drive in a more coordinated way,
particularly in a formation called platooning (Litman, 2018, p. 12). Platooning minimizes
wind resistance by having several vehicles drive closely behind each other. This enables for
decreased fuel consumption from drag related inefficiencies, as well as packing more cars in a
smaller space, thus increasing lane capacity.
7
But the real advantages come from shared autonomous vehicles (SAVs). SAVs are AVs that
are shared between several individuals or owned and operated by a company. One of the most
efficient forms is a sort of ride hailing service like Uber and Lyft, but operated with AVs, thus
eliminating the cost of the trip that goes towards the driver’s salary. SAVs would be
summoned to whatever time and place you would like your trip to start and then take you to
your destination. Litman (2018, p. 9) estimates that the average cost of an AV taxi (a SAV
owned by a company) would be roughly 1$/mile.
Another interesting aspect is the potential of SAVs for use in first mile/last mile problems.
First mile/last mile in transit is, as the name suggests, the first and last legs of your journey. If
you for example walk from your door to a commuter train, take the train to the central
business district and then take a cab from there to your destination, e.g. your workplace, the
first mile trip is the walk to the commuter train and the last mile trip is the cab trip to your
workplace (Best, 2017). SAVs could help in this problem by chauffeuring people to transit
hubs. Furthermore, once you buy a car you are likely to use it more than strictly necessary,
since you want to maximize the benefit of the investment (Litman, 2018, p. 21).
Impacts on Urban Space and Traffic Flow
Road use
Highway
According to a study by Shladover, et al. (2012) AVs can increase highway capacity by up to
80%. A recent study from Stockholm (Trafikanalys, 2017, p. 12) has found that Essingeleden,
an important highway, could see capacity increases of up to 70%. However, all these studies
assume high (>90%) adoption of AVs. That increase in capacity could be utilised either for a
higher flow of vehicle trips, or for some lanes to be converted into bike lanes, or to reduce the
size of future highways (Trafikanalys, 2017, p. 5). A study by Tientrakool, et al. (2011) concerning cars using “Adaptive cruise control” (ACC),
vehicle sensors and vehicle to vehicle communications (V2V) suggests that if the vehicle fleet
consisted of just these types of vehicles (no conventional vehicles at all), highway capacity
could see a remarkable increase of 273%. Another positive effect of AVs on traffic is their potential for lessening so called “Phantom
Jams”. A phantom jam is a phenomenon that occurs when cars are traveling close to each
other and one car for some reason suddenly brakes, causing the following car to react quickly
and brake harder, this sequence then traveling further down the line and increasing until one
car must come to a complete stop, causing a traffic jam behind it as all other cars are forced to
brake to a standstill (Calver, et al., 2011, p. 614). AVs could potentially help this by acting as
a buffer with better reaction time and coordination with other AVs through platooning
behaviour. A platoon of AVs communicating with each other could realize that the first car in
the platoon is braking and then brake together in a way that would enable the last car in the
platoon to hardly break at all, therefore eliminating the phantom jam before it even happened.
Calver, et al. (2011, p. 618) estimates this to lead to a 68% increase in throughput, however,
for this effect to emerge, complete AV adoption is necessary.
8
Arterial streets
At the arterial level, i.e. city roads, not all studies are as optimistic. Fagnant & Kara (2015, p.
174) expects the benefits to be much smaller for the city streets than for the highway, with a
mere 15% reduction in congestion at a 90% adoption of AVs. This is due to increases in lane
capacity. However, a model from Trafikanalys (2017, p. 12) gives a more optimistic view,
finding that if every vehicle on the road was autonomous, the streets of lower Kungsholmen,
Stockholm, could see their capacity doubled. The difference in the findings of these studies is
likely to be that even a few human drivers in the road network could hinder the AVs a lot by
interrupting platooning behaviour or interactions at intersections.
What is important to note is that these two previous studies consider personally owned AVs,
as opposed to SAVs with ride sharing. The major difference to the resulting traffic and
congestion between the two versions of AV is that the ride shared SAVs could reduce overall
vehicle trips by a lot. According to a study from KTH, a scenario of ride shared SAVs could
reduce the overall VMT by 89% from the baseline case of private cars with single passengers
(Rigole, 2014, p. 28).
Parking
According to a 2014 study (Fagnant & Kockelman, 2014), SAVs could reduce the total
number of cars in the fleet by about 90-92%. This is because SAVs are not privately owned,
and would rarely have to park, instead going to the next passenger instead of parking. Rigole (
2014) calculates that since the average car in Sweden drives 12000km per year, at an average
speed of 60km/h it has a utilization rate of just 2.3%. That means that the average Swedish car
(as of 2014) is parked about 97.7% of the time.
The car mostly sits idle, occupying a parking lot somewhere, space that could be better used
for other purposes. The cost of a parking spot in a garage could be as high as 13,000-18,000
dollars/spot (Boulais, 2017). The cost of street parking is obviously lower due to the cost of
constructing multi-storey car parks, but the space it takes up, especially in the city centre,
could be worth far more if the land was available for other purposes such as buildings or
outdoor seating etc.
Fagnant & Kockelman (2014, p. 8) suggests that there could be potential reductions of about
11 parking spaces for every SAV. (Rigole, 2014, p. 24) also believes the reduction of parking
spaces could be significant, stating that only 5% of today’s parking spaces would be
necessary. Zhang, et al. (2015) is a little more conservative, estimating a decrease in parking
demand of around 90%. Even if the actual reduction of parking spaces would be much
smaller, if we are prepared to accept some empty vehicle driving, we could move parking
spaces from the central business districts and high streets to more peripheral areas of the city.
Fagnant & Kara (2015, p. 174) has found that moving a parking spot from the central business
district will save land owners in the central business district upwards of 2000 dollars, moving
it all the way to the suburbs would save upwards of 3000 dollars.
9
Smaller vehicles
The studies above consider normal vehicles, e.g. 5 seat sedans and larger vehicles. One of the
reasons that cars are heavy and bulky today is due to passenger protection in the case of
accidents and crashes. Considering that 90% of all automobile accidents are caused by human
error, AVs could potentially reduce the risk of crashes to almost a tenth of today’s levels. This
means that AVs could eschew heavy physical safety features for advanced AI driving
(Morrow, et al., 2014).
Figure 6: Size comparison for a firefly and an average SUV
For the city based SAV companies, there’s no reason all cars have to be larger than necessary,
considering many trips will be just one-person trips. In 2013, Google’s daughter company
Waymo started designing a compact pod car that they called “Firefly”. This smaller pod car
carried two persons at top speeds of 25 miles per hour, roughly 40 kilometers per hour. This
car would be ideal for city environments, taking up less space and fuel, and the 40 kilometers
per hour speed limit wouldn’t be a problem in the city’s slower road network (Ahn & Waydo,
2017).
Summary of results
Arterial streets
The literature review indicated that the improvements to arterial road capacity could range
from between a 15% to a 100% improvement, depending on the adoption of AVs
Highways
The results for highways were an increase in capacity between 80% and 273%. The difference
in the results stem from the adoption rate of AVs. With lower adoption rates, the capacity
improvements could be as small as around 10%.
Parking
As for parking, the reduction of parking spots needed would range between a 0-95% decrease.
This depends on the ratio of SAVs to personally owned vehicles, autonomous or not. Only
SAVs that rarely stop to park, instead constantly chauffeuring customers, would reduce the
demand for parking. With only 1 SAV needed to do the work of 12 CDVs, the parking space
needed for a scenario with complete SAV adoption would be just above 8% compared to an
all CDV scenario.
Change in VMT
VMT is very likely to increase with a switch to AVs, however reductions from switching to
shared vehicles and Stockholm congestion pricing might lessen the impact. Probable
estimates of VMT increases range between 20% and 35%.
10
Methodology The thesis started with going through the available literature on the subject and compiling a
list of facts about the parameters in question, such as parking reductions, VMT increase etc.
Once better grasp of the subject had been achieved, the relevant quotes and numbers were
used in the literature review. The findings in the literature review and the examples of urban
space reclamation was used in conjunction with GIS analysis of representative streets and
blocks around Stockholm to examine the potential for reallocation of urban space from
vehicular infrastructure to green spaces, pedestrian streets and bike lanes etc.
A wealth of projects of urban reclamation projects was reviewed. Three specific candidates
were chosen for their simplicity, effect and applicability. Once the projects had been chosen,
extensive walks around the city began, as a way to find areas and streets, between them a
somewhat good representation of different types of Stockholm streets.
The locations chosen were a neighbourhood close to Odengatan and Sveavägen, a section of
Kungsgatan and a section of Olofsgatan.
The Odengatan/Sveavägen area was chosen specifically as a candidate for applying the
superblock concept of Barcelona, as it has major roads on the outside of the area but smaller
mostly unused roads in its interior.
Kungsgatan was chosen specifically as a good candidate for applying the “Ströget” concept of
Copenhagen, as it is an old major road with an abundance of shops and a central location.
Olofsgatan was chosen as a good candidate for applying the “summer walking streets” of
Stockholm, as it is a small road with a good location close to the metro and Sveavägen, which
will bring in people. The road is also rather narrow, giving it a cosy feeling.
When the Stockholm locations had been chosen, more detailed surveyings were made in the
areas. Things such as proximity to transit stops, types and number of businesses, subjective
measures of area potential for conversion to pedestrian and bicycle uses, as well as the
distribution and location of roads, parking and sidewalks. The measurements of the different
types of uses were taken with a measuring tape and recorded. These site visits were made in
May 2018, spending about 60-120 minutes in each location.
The GIS analysis was done by using the measurements taken in situ, as well as using aerial
photos from Lantmäteriet (the Swedish surveying organisation). Polygons were made
covering the different uses, with the width from measurements in situ and the length from the
aerial photo and site photos. This was then used to quantify the amount and distribution of
parking, road and sidewalk.
Visualisations of street cross sections (the entire road from building to building) were created
from photos and measurements to illustrate the exact width of the space between buildings
and their use as of today and their potential use after a reallocation from vehicular
infrastructure.
The information from the GIS analysis was then used in conjunction with the findings of the
literature review to estimate the potentials for reallocation of space from vehicular purposes
(i.e parking and roads) to space for pedestrians and bikes (i.e bike lanes or parks and car free
streets). This was done through the creation of three scenarios with varying degrees of SAV
12
Selection of representative streets in Stockholm For the GIS analysis, three areas were chosen. The areas were chosen because they could be
considered representative of the different types of streets and areas that are found in
Stockholm, as well as having a big potential for road reclamation projects. These criteria were
distance to major roads, prevalence of shops, distance to metro or other major transportation
routes, as well as a very subjective measure of how inviting the area is to walk in. With these criteria in mind, three locations were chosen to demonstrate the impact of urban
space reallocation from vehicular traffic and parking. The locations were also chosen by the
types of streets widths and uses.
Figure 7: Location of the chosen areas. Odengatan/Sveavägen in blue, Kungsgatan in red and
Olofsgatan in yellow.
13
Odengatan/Sveavägen neighbourhood
This area consists of the 6 city blocks just
southeast of the intersection of Sveavägen
and Odengatan. The area is enclosed by
the roads Döbelnsgatan, Kungstensgatan,
Sveavägen and Odengatan. The area is
roughly 150 meters by 310 meters,
measured on Odengatan and Sveavägen
respectively. The area was chosen since it contains
several different types of road widths, its
close proximity to Sveavägen and
Odengatan and the nearby bus station
with major lines 2, 4 and 6. Shops are
plentiful along Sveavägen and Odengatan,
and there are more shops and restaurants
inside the area.
Kungsgatan
This section of Kungsgatan was chosen
since it is a major road, roughly 410 meters long. This section has a lot of shops and
proximity to both the green metro line, the red metro line and buses 1 and 2. Kungsgatan is a
larger street, but not as large as other main thoroughfares such as Sveavägen or Odengatan. It
is well used by pedestrians as well as bikes and has the 1 bus running the length of it. The
middle of the street is dedicated to cars and buses, with three to four lanes and some parking.
The daily traffic is about 15100 vehicles.
Figure 9: Kungsgatan
Figure 8: Odengatan/Sveavägen
14
Olofsgatan
The section of Olofsgatan chosen is
between Adolf Fredriks Kyrkogata and
Olof Palmes Gata. It is roughly 130
meters long. Olofsgatan is a small street
with one lane of traffic and parking
running the entire length on one side,
with narrow sidewalks on each side.
Shops and potential shop locations are
plentiful, the street is in close proximity
to Sveavägen and only two blocks away
from the Hötorget metro station. The
daily traffic is 745 vehicles.
Figure 10: Olofsgatan
15
Findings Change in urban space due to AV implementation The literature review concludes that the need for parking could approach just 5% of today's
needs. Even with the slightly more conservative estimate of 10% or even higher, that is a very
significant reduction in the number of parking spots needed to serve the community. Seeing
that most arterial streets consist of one or two lanes, street parking and sometimes a bike lane,
the removal of the parking area would free up a very large area for use by other modes of
transportation or something unrelated to traffic, like green space, parklets, larger bike lanes
etc. The increases in lane capacity vary from rather small, to a doubling of capacity for arterial
streets. The study concerning Kungsholmen is very relevant in this case, seeing as Stockholm
is the subject of this study. Regardless of the increase in capacity, the reduction of total
number of vehicles due to ridesharing should still be able to reduce the number of vehicles on
the road. That means that maybe even some driving lanes could be converted towards other
purposes. But it all depends on the level of adoption of AVs and especially shared AVs. It is therefore
important that any predictions made in this thesis are balanced and nuanced enough to reflect
different possible futures. Therefore, three scenarios with different degrees of AV and SAV
adoption, as well as some policy decisions, will be created to try and address the variations.
Possible scenarios With the results in the literature review some possible scenarios can be formed, depending on
the adoption of SAVs. The difference between AVs and SAVs will be their impact on the
number of cars, and therefore parking. Every SAV will replace 12 privately owned vehicles.
Stockholm today
In Stockholm today, there are about 375 cars/1000 inhabitants in 2017. These cars and other
together make about 504 000 trips every 24 hours in the city in 2017. Stockholmers from the
whole county travelled about 5580 kilometres by car per inhabitant in 2016. The minimum requirements for curbside parking is 2.3 meters times 6 meters, which,
allowing for measuring errors, was encountered while doing the studies of the respective
areas.
Stockholm today
Cars/1000 inhabitants 375
Road requirements (relative to today, %) 100%
VKT (kilometres/inhabitant and year) 5580
Parking demand (relative to today, %) 100%
In all scenarios, AV adoption is set to 100%, meaning that highways will see a capacity
increase of 273%, whereas arterial streets will see capacity doubling to 100% of today’s
levels. This increase in capacity, combined with the reduction in value of time from
autonomous vehicles freeing the passenger from focusing on driving, an increase in the
number of drivers on the roads is likely. Pent up demand from elderly and people without
driver’s licenses will also increase cars in traffic.
16
However, congestion pricing and car sharing could lessen the increase. This study will, based
on the results in the literature review, assume that car traffic, the number of cars, parking
requirements and VMT could see an increase of around 20% with complete adoption of AVs.
Baseline scenario
Baseline case
Cars/1000 inhabitants 450
Road requirements (relative to today, %) 60%
VKT (kilometres/inhabitant and year) 7540
Parking demand (relative to today, %) 120%
Low adoption of SAVs
With a SAV adoption of 25%, the number of vehicles needed to do the baseline scenario’s
transportation will be decreased by 23%, translating to a reduction to about 346 cars/1000
inhabitants. The need for parking would also be reduced by 23%
Low SAV adoption
Cars/1000 inhabitants 346
Road requirements (relative to today, %) 60%
VKT (kilometres/inhabitant and year) 7540
Parking demand (relative to today, %) 92%
Moderate adoption of SAVs
With a SAV adoption of 50%, the number of vehicles needed for the baseline scenario’s
transportation will be decreased by 45%, translating to a reduction to about 203 cars/1000
inhabitants. The need for parking would also be reduced by 45%.
Moderate SAV adoption
Cars/1000 inhabitants 203
Road requirements (relative to today, %) 60%
VKT (kilometres/inhabitant and year) 7540
Parking demand (relative to today, %) 66%
High adoption of SAVs
With a SAV adoption of 100%, the number of vehicles needed for the baseline scenario’s
transportation will be decreased by 92%, translating to a reduction to about 38 cars/1000
inhabitants. The need for parking would be reduced by about 90%.
High SAV adoption
Cars/1000 inhabitants 38
Road requirements (relative to today, %) 60%
VKT (kilometres/inhabitant and year) 7540
Parking demand (relative to today, %) 10%
17
Summary
As we can see from the different scenarios, only the Cars/1000 inhabitants and parking
demand change with the adoption rate of SAVs. They are linked because the parking demand
is a result of how many cars need parking.
0
50
100
150
200
250
300
350
400
450
500
Baseline Low adoption Moderate adoption High adoption
Cars/1000 inhabitants
Cars/1000 inhabitants
0
20
40
60
80
100
120
140
Baseline Low adoption Moderate adoption High adoption
Parking demand relative to today, %
Parking demand relative to today, %
18
GIS Analysis
The different types of use are marked
with green for pedestrian use, orange for
roads and grey for on street parking.
Odengatan/Sveavägen
In the blocks at the Odengatan/Sveavägen
location, 2547 m2 is dedicated to parking,
7117 m2 to sidewalks and 7992 m2 to
roads. The total space in between
buildings is 17656 square meters. That
gives a ratio of about 15% parking, 40%
sidewalk, and 45% road.
Figure 12: A representative section of Markvardsgatan.
Figure 11: GIS analysis of the
Odengatan/Sveavägen area
19
Figure 13: A representative section of Rehnsgatan.
Kungsgatan
On Kungsgatan, 72 m2 is dedicated to parking, 4523 m2 to sidewalks and 6266 m2 to roads.
The total space is 10864 square meters. That gives a ratio of less than 1% parking, 42%
sidewalk, and 57% road.
Figure 142: GIS analysis of the Kungsgatan section
20
Figure 15: A representative section of Kungsgatan
Olofsgatan
On Olofsgatan, 265 m2 is dedicated to parking, 436
m2 to sidewalks, and 378 m2 to roads. The total space
is 1078 square meters. That gives a ratio of 25%
parking, 40% sidewalk, and 35% road.
Figure 16: GIS analysis of
Olofsgatan
21
Figure 17: A representative section of Olofsgatan.
Spatial Distribution
(%)
Odengatan/
Sveavägen
Kungsgatan Olofsgatan
Sidewalk 40% 42% 40%
Road 45% 57% 35%
Parking 15% <1% 25%
Scenarios for the locations
Summary Baseline Low
adoption
Moderate
adoption
High
adoption
Cars/1000 inhabitants 450 346 203 38
Road usage (relative to
today, %)
60% 60% 60% 60%
VKT
(kilometres/inhabitant and
year)
7540 7540 7540 7540
Parking demand (relative
to today, %)
120% 92% 66% 10%
22
Odengatan/Sveavägen
In the low SAV adoption scenario, parking is essentially unchanged at 92% of today’s
demand, which means that the roads need to be open to traffic to keep the parking available.
Through traffic could be prohibited, but due to the proximity to Sveavägen and Odengatan, no
change is really expected from this scenario.
In the moderate SAV adoption scenario, parking requirements is reduced to 66% of today’s
demand. This frees a lot of land up for other uses, and the interior of the blocks where
Markvardsgatan and Luntgatan meet could probably be made into a superblock with no
parking or traffic. The rest of the area would be left as is to meet parking and driving
demands. The newly formed small superblock could be populated with pedestrian friendly
benches and pop up parks, and restaurants could be allowed to have outdoor seating in the
summertime.
In the high SAV adoption scenario, parking requirements would be virtually non-existent
compared to today. The 255 m2 required could easily be accommodated on the outside of the
area and the entire 6 blocks could be turned into a superblock with through traffic and parking
banned on the inside of the area. This superblock structure would see pedestrian area almost
double, adding a lot of space for parklets, outdoor seating, perhaps a playground or other
pedestrian and bike friendly uses. A lack of trees could be fixed with space between buildings
not needed by car traffic.
Kungsgatan
Seeing as Kungsgatan has hardly any parking, the three SAV adoption scenarios would all
affect the street the same way. However, the complete adoption of AVs would reduce the
need for road area by 40% compared to today, which means some of the lanes could be
removed and used for other purposes, such as trees, smaller parks, bike parking or perhaps
new buildings such as fast food or smaller shops. It is however unlikely, and perhaps even
unwanted that the entire street should be closed off to car traffic and buses, since it is a major
road from the eastern parts of town to the western. Simply closing one lane in each direction
would make each of the sidewalks almost as wide as the cross section of Strøget.
Olofsgatan
The low SAV adoption scenario permits almost no change from today, as the almost
unchanged parking requirements will allow for no space allocation from parking or the road
that enables parking. A pocket park or bike rack could probably be installed.
The moderate scenario would enable the removal of a third of all the parking spaces. It would
therefore be possible to turn the northern parts of the road into a car free street, connecting it
with the park. It would also be possible to use some of the parking spaces for pop up parks or
other smaller changes such as bike racks.
The high adoption scenario essentially enables full conversion of the street into a walking
street. Potential restaurants could have outdoor seating and space for greenery would be freed
up.
23
Conclusion
As the literature review reveals, the potential for urban space reallocation is substantial, but
when or if these changes will happen is uncertain, both from a technological perspective, as
well as depending on policy decisions, public acceptance and the time that adoption of AVs
and SAVs take.
Depending on what scenario would play out, the resulting potential for space reallocation
differs greatly. The low adoption scenario would barely make a dent in parking space, the
moderate scenario would remove about a third of the parking demand, and the high adoption
scenario would almost eliminate the parking need in the city, cutting it by 90%
Odengatan/Sveavägen and Olofsgatan could both see a large change from the moderate and
high adoption scenarios, turning the converted parking spaces into parklets, bike racks or
complete walking streets complete with outdoor seating etc. Kungsgatan would be equally
affected by all three scenarios due to the low amount of parking. Because of the increase in
road capacity, some roads could be removed, especially those supporting parking spaces,
perhaps enabling a narrowing of Kungsgatan or conversion of lanes into bus or cycling lanes.
This study has been specifically about whether it is possible to use SAVs as a means to reduce
the space that cars require in our cities. However, we don’t really need advanced technology
to reduce the number of car infrastructure. We’ve had buses since the beginning of the 20th
century, and it is highly probable that SAVs would reduce the share of commuters due to a
more convenient commute. A study of how to maximize the societal benefits of SAV
adoption by policy making would be highly interesting.
This study, due to time constraints, has not looked at parking in garages or under buildings. It
is very possible that in the high adoption scenario, all parking demand could be serviced by
garages instead of curbside parking, almost completely removing car parking from the city
surface.
This study has tried to illustrate how SAV adoption could change the urban landscape in our
cities and given suggestions as to how those changes could be utilized and leveraged for a
better city. The potential for reallocation is substantial and there are plenty of good ideas for
what to do with the space taken from car infrastructure. But we can’t wait for SAVs to just
arrive. We have to start planning for them right now, to make sure that we make the most of
this big opportunity.
25
Trafikanalys, 2017. Självkörande fordon och transportpolitiska mål, Stockholm: Trafikanalys.
Wadud, Z., MacKenzie, D. & Leiby, P., 2016. Help or hindrance? The travel, energy and carbon
impacts of highly automated vehicles. Transportation Research Part A: Policy and Practice, Volym 86,
pp. 1-18.
Zhang, W., Guhathakurta, S., Fang, J. & Zhang, G., 2015. Exploring the impact of shared autonomous
vehicles on urban parking demand: An agent-based simulation approach. Sustainable Cities and
Society, Volym 19, pp. 34-45.
Image sources
Figure 1: Barcelona, how cities are taking streets back from cars, Vox media, viewed 20 june 2018
<https://www.vox.com/2016/8/4/12342806/barcelona-superblocks>
Figure 2: Superblock illustration, BNC Ecologica, viewed 7 june 2018
<https://www.vox.com/2016/8/4/12342806/barcelona-superblocks>
Figure 3: Cross section, Global Design Cities, viewed 13 june 2018,
<https://globaldesigningcities.org/publication/global-street-design-guide/streets/pedestrian-priority-
spaces/pedestrian-only-streets/pedestrian-streets-case-study-stroget-copenhagen/>
Figure 4: Private Photo
Figure 5: SAE International’s Levels of Driving Automation for On-Road Vehicle, © SAE
International.
Figure 6: Firefly and minivan, n.d, image, viewed 15 june 2018
<https://www.designweek.co.uk/issues/12-18-june-2017/waymo-drops-self-driving-car-firefly-new-
autonomous-minivan/>
Figure 7: Aerial photo of the chosen areas, 0.25 RGB raster © Lantmäteriet 2018.
Figure 8: Aerial photo of Odengatan/Sveavägen, 0.25 RGB raster © Lantmäteriet 2018.
Figure 9: Aerial photo of Kungsgatan, 0.25 RGB raster © Lantmäteriet 2018.
Figure 10: Aerial photo of Olofsgatan, 0.25 RGB raster © Lantmäteriet 2018.
Figure 11: GIS image of Odengatan/Sveavägen, made in ArcMap.
Figure 12: Sketchup rendition of Markvardsgatan, private image.
Figure 13: Sketchup rendition of Rehnsgatan, private image.
Figure 14: GIS image of Kungsgatan, made in ArcMap.
Figure 15: Sketchup rendition of Kungsgatan, private image.
Figure 16: GIS image of Olofsgatan, made in ArcMap.
Figure 17: Sketchup rendition of Olofsgatan, private image.