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Lägerhyddsvägen 1. Hus 4, Plan 0. Postadress: Box 536. 751 21 Uppsala ...... The city of Malmö has a layout plan to grow notably in the years to come. ..... Charging an electric car will in ordinary Swedish homes, with a regular fuse (10A ) ... The cost to purchase a new Volvo V70 Ethanol/Gasoline car is circa 255 900 SEK.

TVE 20 015 juni

Examensarbete 15 hp Juni 2012

Carpool in Östra Sala backe Case study on how the parking standard is affected Jonas Andersson David Jakobsson Magnus Larsson

Abstract Carpool in Östra Sala backe Jonas Andersson, David Jakobsson and Magnus Larsson

Teknisk- naturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student

In this case study the area of Östra Sala backe, that will be built in the eastern part of Uppsala, has been studied. Uppsala County wants to lower the area needed for parking from todays parking standard of 1,1 parking per household. Östra Sala backe has a vision of becoming an environmental friendly area and a living neighborhood. The way the inhabitants use cars for transports currently is not sustainable. Östra Sala backe wants to change the behavior to become sustainable and to encourage usage of alternative transport, without affecting the freedom the car provides. If fewer journeys is to be made by car or the car use is more efficient the parking norm will decrease. In the case study a model has been developed using data about population distribution and travel patterns. Data has been obtained from two similar projects in Sweden, Västra Hamnen in Malmö and Hammarby Sjöstad in Stockholm. These projects have been chosen due to the similarities in size, located in regions with a high growth of population and they are all environmental pilot projects. Data from reports about travel behavior and patterns among the Swedish population has been used to simulate the model developed. A few assumptions have been made about Östra Sala backe due to lack of statistic and the fact that the area has not yet been built. This data is the basis for the simulation to find a suitable size and mix of cars in the carpool of Östra Sala backe.

Handledare: Anders Hollinder Ämnesgranskare: Joakim Munkhammar Examinator: Joakim Widén ISSN: 1650-8319, TVE 20 015 juni

Table of content 1.

Introduction.................................................................................................................... 3

1.1

Aim of the case study ................................................................................................... 3

1.2

Limitations ..................................................................................................................... 3

1.3

Structure of the case study .......................................................................................... 4

2.

Background.................................................................................................................... 4

2.1

The basic idea of a carpool .......................................................................................... 5

2.2

Three areas of expansion ............................................................................................. 5

2.2.1

Östra Sala backe ...................................................................................................... 6

2.2.2

Hammarby Sjöstad in Stockholm .......................................................................... 8

2.2.3

Västra Hamnen in Malmö ...................................................................................... 10

2.3

Similar but different .................................................................................................... 14

2.3.1

The behavior and conditions ................................................................................ 14

2.3.2

The areas sizes their distributions of age ........................................................... 14

2.3.3

The parking standard ............................................................................................ 15

2.4

Economy and environmental aspects ....................................................................... 15

2.4.1

Electric car ............................................................................................................. 15

2.4.2

Fuel driven cars ..................................................................................................... 15

2.5

Cost of establishing parking lot ................................................................................ 16

3.

Methodology ................................................................................................................ 17

3.1

The gathering of data .................................................................................................. 17

3.2

The model..................................................................................................................... 18

3.3

Criticism of the sources ............................................................................................. 19

3.4

Statistics....................................................................................................................... 19

3.4.1

Distributions ........................................................................................................... 20

3.4.2

Long way journeys ................................................................................................ 22

3.4.3

Distance of different type travels ......................................................................... 23

3.4.4

Work related journeys ........................................................................................... 23

3.4.5

Time of rent ............................................................................................................ 24

3.4.6

Time of rent related to work and studies ............................................................ 24

3.4.7

Time of rent related to other trips ........................................................................ 25

3.4.8

Long way journeys and time of rent .................................................................... 25

3.5

The purchase cost of the cars ................................................................................... 25

3.6

Passengers and car travels ........................................................................................ 26

4.

The simulation and data ............................................................................................. 26

4.1

Limitations of the simulation ..................................................................................... 27

4.2

Different scenarios ...................................................................................................... 28

4.2.1

Motivating the different scenarios ....................................................................... 28

1

4.3

The simulation of Östra Sala backe .......................................................................... 29

4.3.1

Deciding the number of travels by car ................................................................ 29

4.3.2

Type of trip and means of conveyance ............................................................... 30

4.3.3

Calculating the parking norm ............................................................................... 31

4.3.4

Choosing a car- example calculation .................................................................. 31

4.3.5

Deciding the parking norm of Östra Sala backe – example .............................. 32

4.3.6

Calculating economical impact – example ......................................................... 33

4.3.7

Calculating environmental impact-example ....................................................... 34

5.

The results of the simulation ..................................................................................... 36

5.1

Sensitivity analysis ..................................................................................................... 42

6.

Discussion ................................................................................................................... 43

6.1

Efficiency and incitement ........................................................................................... 44

6.2

Environmental affect and costs of the carpool ........................................................ 45

7.

Conclusion ................................................................................................................... 46

8.

Sources ........................................................................................................................ 47

8.1

Literature ...................................................................................................................... 47

8.2

Internet-based sources ............................................................................................... 47

8.3

Figures and tables ....................................................................................................... 50

9.

Appendix ...................................................................................................................... 51

9.1

Appendix A - Code used for plots in MATLAB ......................................................... 51

9.2

Appendix B - Java code of the simulation ................................................................ 56

9.2.1

The Main class ....................................................................................................... 56

9.2.2

The class Systemet ............................................................................................... 57

9.2.3

The class Queue .................................................................................................... 67

9.2.4

The class GraphicData .......................................................................................... 68

9.2.5

The class Inhabitants ............................................................................................ 69

9.3

Appendix C – Examples of the simulation ................................................................ 72

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1. Introduction 1.1 Aim of the case study The purpose of the study is to investigate the impact a carpool will have in Östra Sala backe when built. The aim is further to estimate how a carpool can be used as a substitute for car ownership for each resident without any drawback in terms of car availability. If this requirement can be met the area needed for parking could decrease and the case study will investigate by how much. Furthermore a minor aim of the study is to examine the environmental as well as the economical effect a carpool will have to Östra Sala backe. The economical aspects taken into consideration will be how much capital that could be saved if building fewer parking lots compared to the purchase of cars when establishing the carpool. When examining the environmental aspects the amount of carbon dioxide emissions when driving cars of the carpool will be considered. The question formulation of the case study is: Is it possible to reduce the area needed for parking by integrating a carpool in Östra Sala backe and what will the economical and environmental impact be?

1.2 Limitations In this case study the aspects of how the carpool will be managed by its’ managers and how employees of the carpool will behave will not be taken into consideration. The study will not consider that different inhabitants are not equally susceptible to be environmental friendly. The carpool of the case study is not integrated in the surrounding society hence it is only located and used by inhabitants of Östra Sala backe. It is possible that the carpool of Östra Sala backe could more effective and used by a larger population if a population outside of the areas boundaries. This scenario will not be taken into consideration in the case study. In the case study it will not be regarded that some trips to work and studies demand special types of cars because of the nature of the errand. The sorts of travels are assumed to be made by a regular medium sized car, which is not entirely realistic. Travels related to the category other errands has a share of fuel driven cars that answers to special needs. Notable is that there are electric cars with a large load capacity and such that might answer to many needs that in the study will be partly handled by fuel driven cars. The environmental aspect of this study is rather limited since the only thing taken into considerations is the amount of carbon dioxide emissions related to driving the simulated distances, thus only the combustion of fuel and an average emission of producing electricity. Obviously a car will have other impacts on the environment 3

during the production as well as when used in the carpool and sold on a second-hand market. If a life cycle analyze would be used to investigate a wider impact on the environment the quota of impact of the two different types of cars (electric and fuel driven) is expected to be higher than in this case study since the emissions of driving an electric car is much lower than for driving a fuel driven car. The comparison of emissions of this study will only reflect the emissions related to driving the cars, thus the emission related to the usage of Östra Sala backes carpool. In the case study the private economy of the population will not be considered. The purchase of private cars will not be included in economical calculations nor will parking tariffs related to private cars be considered. The economic aspect of the carpool will be an comparison of the capital to be saved when establishing fewer parking lots and the purchase of cars to the carpool. The case study will not regard aspects as administration, service and management of the carpool.

1.3 Structure of the case study This case study consists of three parts of data, Three area of expansion, Economy and environmental aspects and Statistics. The structure with three data sections has been used to make the reading more convenient. Three area of expansion will present statistic about how Östra Sala backe most likely will be when it comes to age distribution and car use. The statistic is based on data from two other Swedish projects, where the construction has progressed further. In Economy and environmental aspects the driving cost and carbon dioxide emission is presented. Section Statistics will present national statistics about travel patterns for the general Swede.

2. Background About one in two Swedish trips are made by car as well as a large segment is sustainable traveling, the segment of walking or riding a bike. The distribution is not equal to those of the areas we have analyzed and the distributions vary amongst them. The way the shares of transport are distributed may depend on local factors as well as what alternatives that are provided both regionally and locally. The general Swede travels as shown in Figure 1.

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3,2%

Car 32,7%

Public Transport 52,7%

Walk or bicycle Other transport

11,4%

Figure 1, The Swedish distribution of transport

2.1 The basic idea of a carpool The idea of the carpool is to make both the environment and the users a favor, but it is also supposed to be economical for the individual. The member would not need to do a major economical investment that a car purchase involves. The membership means that all the users share expenses for all the cars such as fees, taxes, insurances and service. The idea is that you pay for the car when you use it. But the carpool will deal with all the time-consuming parts that's need to be done if you own a car, such as cleaning and change of tires. An alternative might be that the members could perform these tasks and get a minor financial compensation. You should also be able to make a reservation easily and whenever you would want to. There are carpools today where you either reserve a car from your cellphone or online. The car gets unlocked using some kind of identification card, smartcard or your own cellphone. (Hammarby Sjöstad, 2012, tag service-bilpool) Just like the reservation needs to be simple so does the payment. By using a carpool the cars will be used more efficient and the space needed for parking may decrease, which is the major aspect of the study to investigate.

2.2 Three areas of expansion The following three areas are located in regions of expansion, in different proportions but still an expansion. Hammarby Sjöstad is located in Nacka and a part of the Stockholm region. This region is growing faster than ever. (Stockholms Läns Landsting, 2012) The project started in 1990 and is calculated to be finished in 2017. (Stockholm Bygger, 2012) Focus on the environment has been an important part of the construction of Hammarby Sjöstad. The investment on the environment includes improving the traffic situation and public transportation. (Stockholm Stad, 2009)

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The city of Malmö has a layout plan to grow notably in the years to come. The plan suggests that 1500 new residences are to be built and as many new jobs established yearly until 2020. (Malmö Stad, 2001, p. 10) Östra Sala backe is a part of Uppsala city, which also is growing quite rapidly (in comparison to other parts of Sweden else than Malmö and Stockholm). In 2009 the population in Uppsala town was not far from 200 000 inhabitants (Statistiska Centralbyrån, 2010, p. 95) and the increase was almost 4100 from the previous year. (Statistiska Centralbyrån, 2010, p. 113) The exact increase of the population was just more than 2 % in a year. This makes Uppsala interesting for companies who want to expand. (Uppsala Kommun, 2010a, p. 4) Östra Sala backe is located in the east of Uppsala, one of the most expanding locations in the county. (Uppsala Kommun, 2011c, p. 6) Since Östra Sala backe has not yet been built, data from Västra Hamnen as well as Hammarby Sjöstad has been used in the model to obtain a picture of how the population distribution and car use probably will be in Östra Sala backe. Why these areas have been chosen and used is motivated later on in the study, but the major factor is that they are in many ways comparable. The three following projects are all pilot projects in the sense to make the areas more environmental sustainable. The goal with the projects is to change the inhabitants’ behavior when it comes to transportation. 2.2.1 Östra Sala backe The vision with Östra Sala backe is a living a neighborhood of squares, parks, and business places. That it will be a neighborhood where people can and do meet.(Uppsala Kommun, 2011c, p. 4) The district should be an important part in Uppsala’s east districts and be innovative. Östra Sala backe should be built with an awareness of the environment and the people living in the surrounding area. The structure of Östra Sala backe will link together the older neighborhoods and create a neighborhood with character as a smaller downtown.(Uppsala Kommun, 2011c, p. 9) Östra Sala backe will strive for technological innovation and awareness of the environment combined with long-term financial sustainability. The latest in technology and engineering knowledge will be used. There is a vision for the area to become Uppsala's most climate-friendly area (Uppsala Kommun, 2011c, p.11) where sustainability will be the focus of ecologist, socially and economically sustainable solutions. (Uppsala Kommun, 2010a, p. 7) Östra Sala backe can be seen as Uppsala's pilot project for eco and sustainable construction. (Uppsala Kommun, 2011d, p. 4) The small distance of 2 km to the core of the city and E4 gives a good opportunity for public transportation and to use sustainable transports such as bicycle and walking to the city core. (Uppsala Kommun, 2011c, p. 6) It should become natural to walk, ride the bike and choose public transportation over the car. (Uppsala Kommun, 2010a, p. 26) Östra Sala backe shall be one of Sweden’s most bicycle dense areas. To meet the goal with sustainable transportation solutions more public transportation lines will go past Östra Sala backe. (Uppsala Kommun, 2011d, p. 10) Sustainable transportation solutions will be encouraged in Östra Sala backe and for those who does not have access to an 6

own car the possibility to one will be provided. (Uppsala Kommun, 2010a, p. 14)It is in line with Uppsala’s high ambition on environmental, energy and climate change prevention. (Uppsala Kommun, 2011c, p.11) In 2002 Uppsala had the second largest supply per inhabitant of general route (public line traffic) services in Sweden, with 105 km per inhabitant. (SIKA, 2004, p. 22)A large part of Uppsala is covered by already established public transports. Sala backe and Årsta is a part of the area surrounding the proximity of Östra Sala backe and today almost 10 000 people live in Sala backe and 8 000 people in Årsta. A large majority of the households are families that live in multifamily houses, 97% in Sala backe and 78% in Årsta. (Uppsala Kommun, 2010b, p.6) The distribution of families with and without children in Sala backe and Årsta including single households is as follows in the diagram. (Uppsala Kommun, 2011a)

21,7% No child Child 78,3%

Figure 2, Distribution of families with and without child The large amount of families in Uppsala does affect the distribution of age. There are a relatively large segment of children younger than 16 years old distributed on a not as large segment of adults.

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20,0%

14,2%

4,3% 0-15 years 16-19 years 20-64 years 65> years

61,6%

Figure 3, Age distribution of the inhabitants in Sala backe and Årsta The construction of Östra Sala backe is estimated to around 2000 apartments and 4500 new inhabitants. In a further expansion 500 apartments could be built for another 1100 inhabitants. (Uppsala Kommun, 2010b, p. 6) In the model the population minimum will be 4500 inhabitants and the maximum 5600 inhabitants. The population in Uppsala County has increased every year since the fifties and is expected to grow with another 40000 people to 2030. To be able to meet the growth approximately 20 000 new residences need to be built, this means 1000 new residences each year until 2030. (Uppsala Kommun, 2010a, p. 9) Today's parking standard is 1,1 in Uppsala. This means that each household has an own parking space and one additional parking space for guests on every tenth house. To solve the parking issue in the area of Östra Sala backe an underground garage could be build, or alternatively solve the problem by implementing a carpool that would reduce the parking standard. (Uppsala Kommun, 2010a, p. 27) 2.2.2 Hammarby Sjöstad in Stockholm In the year of 2009 about 17 000 people lived in Hammarby Sjöstad. When the whole project is finished the population living in Hammarby Sjöstad will be approximately 25 000 individuals, and housing around 10 000 jobs. Hammarby Sjöstad is a project that tries to establish a modern suburb close to the city, but with the closeness to surrounding water in mind. (Stockholm Stad, 2009) A large majority of the population of Hammarby Sjöstad is within working age and a considerably segment are children, which are younger than 16 years old. There is just a small segment of elderly, only a few percent.

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5,0%

19,0% 3,0%

0-15 years 16-19 years 20-64 years 65> years

73,0%

Figure 4, Age distribution of the inhabitants in Hammarby Sjöstad There are 7 000 parking spots in Hammarby Sjöstad. The planned parking norm is to be 0,7 parking per household when the project is finished. That number includes both private and public parking for the inhabitants of Hammarby Sjöstad. The planned parking standard is significantly lower than current parking norm in Uppsala of 1,1. In fact it is almost 36,4 percentages less parking spots in Hammarby Sjöstad. That means that the inhabitants manage to live and use about two thirds of Uppsala’s parking spots. The lowered parking norms have had an effect on the population’s behavior and car ownership because the parking norm has dropped from 0,75 to 0,7 between the years of 2005 and 2007 and in the same years the household with car has dropped by 4 percentages. (Stockholm Stad, 2010) During weekdays you need to pay a fee to use the parking lot for guests. If you are a car owner you can be granted to park without to pay a fee, but then any other who also pays can park there. To get a private parking lot you need to pay for it. (Hammarby Sjöstad, 2012, tag service-parkering) That might be an economical incitement to use a carpool if you aren’t a frequent driver of your car, an extra cost to the ownership besides taxes, fees etc. To be able to build Hammarby Sjöstad the infrastructure has been transformed. Traffic barriers have been removed and old facilities has been redesigned or removed to be able to make reality of the vision of Hammarby Sjöstad. A factor of significance is that both Nacka County and Stockholm City has been able to agree largely and therefore effectively collaborated on both sides of the municipal borders. (Stockholm Stad, 2009) Hammarby Sjöstad offers several transportation options and this has been an important part in the commitment to the environment, and a part of the vision of Hammarby Sjöstad. There is an opportunity to take the bus to Stockholm, available both day and night. Also there is an opportunity to cross the Hammarby Lake with cheap or free ferry 9

trips. Tvärbanan is a tram and gives the resident one more option to get into Stockholm on most hours of the day. Both Tvärbanan and the bus connect with the many ferry routes. (Hammarby Sjöstad, 2012, tag service-kommunikationer) Hammarby Sjöstad also has carpools that offer the residents more environmental friendly transportation options than an own car. (Hammarby Sjöstad, 2012, tag service-bilpool) The current usage of transports in Hammarby Sjöstad is not evenly distributed between the possible ways to travel. Just over half of the population uses a private car to travel.

27,0% Public transport Private Car 52,0%

Walk and bicycle

21,0%

Figure 5, Distribution of traffic in Hammarby Sjöstad 2003 2.2.3 Västra Hamnen in Malmö Västra Hamnen is experiencing a change, a transformation from being an area of industries to becoming a modern urban settlement. The area’s proximity to the core of Malmö, Malmö C, means that there are good opportunities for a large segment of journeys to be made with environmentally friendly and sustainable transports. To break the trend of car usage cyclists will have plenty of space in reserved bike lanes. This is an action that itself will not solve the future traffic problem and achieve sustainable travel in the area of Västra Hamnen and its’ surroundings. (Malmö Stad, 2010b, p. 3) There are just above 2500 residences and about 10 000 job sites in Västra Hamnen today. But that’s only a third of the planned residences for the future. When everything is built according to the plans there will be 20 000 inhabitants (Malmö Stad, 2012a, p. 11) distributed on around 8000 apartments (Malmö Stad, 2010b, p. 6)and Västra Hamnen will include somewhere around 17 000 work places. (Malmö Stad, 2012a, p. 11) The university has calculated to establish facilities for 11 000 students in the area of Västra Hamnen. (Malmö Stad, 2010b, p. 6)

10

9,9%

9,1% 8,1% 0-18 years 19-24 years 25-64 years 65- years

72,9%

Figure 6, Age distribution in Västra Hamnen, January 2011 The traffic in Västra Hamnen has increased substantially the last couple of years. The high exploitation of new residence in the area are expected to affect the commune traffic as well as the car traffic, which in turn will result in cues during peaks of traffic flows. (Malmö Stad, 2010b, p. 3) The area of Västra Hamnen is particularly sensitive to increasing flows of traffic since there are a few entrances and exits to the half island. (Malmö Stad, 2010b, p. 6) Malmö Stad wants to reallocate the traffic by taking actions to lower the private usage of cars (Malmö Stad, 2010b, p. 3) as well as support the usage of sustainable transports alternatives. (Malmö Stad, 2010b, p. 6) Estimations of future traffic are based on earlier regional studies. The distribution of traffic in Malmö 2003 was as visualized in Figure 8. (Malmö Stad, 2010b, p. 7)

3% 10% Car 14%

Bicycle Walk 52%

Bus Train

21%

Figure 7, Distribution of traffic in Malmö 2003

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An estimation of the distribution of traffic in Västra Hamnen in year 2015 if no interventions are made to favor sustainable travelling is as shown in Figure 9. (Malmö Stad, 2010b, p. 7)

13% Car 43%

14%

Bicycle Walk Bus

8%

Train 21%

Figure 8, Västra Hamnen in year 2015 Scenario 40 is a future scenario where the distribution of traffic in Malmö will not be affected markedly, to and around Västra Hamnen. This will also affect Västra Hamnen and its’ traffic distribution. It is supposed that the inhabitants of Västra Hamnen have reduced their car use, but the percentage has not dropped drastically. It is estimated that the labor related journeys with car would be around 25 percent and the transportation by car for the inhabitants somewhere around 40 percentages. The total percentage of car journeys will end up at approximately 34 percent according to this scenario, as presented in Figure 10. A result of the dropping usage of cars means that the percentages of alternative travelling will raise, especially the commuter train traffic that is believed to have great potential. To reduce the proportion travelling by car to Västra Hamnen will probably require more drastic measures. Many trips are made over weekends when the alternative transport is less available. One possible solution to reduce car usage would be to lower the parking standards by integrating carpooling in the area. (Malmö Stad, 2010b, p. 11)

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16% 34% 16%

Car Bicycle Walk Bus Train

11% 22%

Figure 9, Scenario 40 in year 2015 Scenario 30 is an estimation of how traffic will be distributed if considerable interventions are made to favor sustainable travelling and the parking standards are lowered. Car traffic and mainly through traffic has in this scenario been reduced and the travelling to Västra Hamnen will in large part be done collectively, along with bicycle or by walking. The result of Scenario 30 will be that the traffic flows in and to Västra Hamnen will be significantly lowered and also that congestion at entrances and exits drastically reduced.(Malmö Stad, 2010b, p. 12) The shares of expected travels are shown in Figure 11.

17%

26%

Car Bicycle Walk

23%

Bus 25% 8%

Train

Figure 10, Scenario 30 in year 2015 For the city of Malmö the parking norm vary depending on the type of housing. For an individual house or villa the parking standard is about twice of an apartment building. Although the norm vary for apartments with a minimum and maximum of 0,6 and 1,6 spots per household, the most common range of spots are between 0,7 to 1 spots per household. For groups with less need for a car as student or elderly, the parking standard is significantly lower with 0,15 to 0,3 sites per household. All these standards include parking sites for visitor parking. (Malmö Stad, 2010a, p. 18) 13

One part of the measures proposed to reduce the parking standard and to achieve sustainable travel is to establish a carpool in Västra Hamnen. (Malmö Stad, 2010b, p. 15)

2.3 Similar but different The three areas have several things in common, but also some important differences. 2.3.1 The behavior and conditions All these areas are located in larger cities and regions that are growing. One factor that unites them is that the areas want to change the behavior of their inhabitants. They all want to make their population to travel less often by car and instead use more sustainable alternatives. They strive to be innovative and to come up with environmental friendly solutions and to make the neighborhood a living area for all of its population, but with a downtown suburb touch to it. They have adapted or will adapt the infrastructure to fit the commitment to the environment and to be able to build their vision. If you are to change the inhabitants’ behavior you have to have access to alternative traveling and if you are to be able to walk or ride a bike the areas location must be rather close to the city core. This criterion is in fact true in each project and therefore it is reasonable to say that the way of traveling is at least comparable, if not equal. 2.3.2 The areas sizes their distributions of age The sizes of the three different populations are comparable, but with one difference is that the new area of Östra Sala backe is just a few thousand but those in the whole area including Sala backe and Årsta is comparable in size. The distribution of ages in Hammarby Sjöstad and Västra Hamnen is not equal. There are a bigger percentage of inhabitants in the working age in Västra Hamnen than in Hammarby Sjöstad. It is reasonable to assume that there are a larger number of families in Hammarby Sjöstad, since it is not only fewer inhabitants in working age but also a larger number of children. In fact it is around twice as many in children in Hammarby Sjöstad’s age segment. But the distribution of ages are not that different if you see too younger and elderly people. If you take into account those groups that would not use car as often as the other segments the differences are just a few percentages. But then if you compare the distribution of age in Sala backe and Årsta to the two other areas you can notice a considerably difference. That is probably because of todays living standard in Sala backe and Årsta, which more villas and multifamily residences than the other areas. We will in this study assume that the new area to be built with apartments will mirror the areas of Västra Hamnen and Hammarby Sjöstad.

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2.3.3 The parking standard One thing that does differ between the areas is the parking standard, not that much but still significantly. The highest parking standard is in Uppsala with 1,1 but elsewhere it is as low as 0,7. The standard in Malmö city is not certain, but the average is somewhere between 0,7 and 1,0 and it is not unreasonable to set a number of 0,8 which is not that different from the number in Hammarby Sjöstad.

2.4 Economy and environmental aspects In this study the environmental as well as the economical aspect are studied. To be able to make relevant comparisons between different kinds of cars regarding effluent emissions and economical costs three different cars have been chosen. All these three cars have an environmental profile and will form the basis of all economic and emission calculations. 2.4.1 Electric car The car of the year 2011 was Nissan Leaf (Dagens Nyheter, 2011), an electric car with a top range of 175 km (Gröna Bilister, 2011a). In the simulation of the study the electric car that will be used as a reference is Nissan Leaf. The facts and numbers of Nissan Leaf is the numbers our calculations will rely on. The purchase price of a new car is estimated to circa 320 000 SEK (Gröna Bilister, 2011a) and the fuel consumption of Nissan Leaf is 34 kWh/100 miles, which is almost equal to 2,11 kWh/10km. (Green Car Congress, 2010) An estimation of total emissions is calculated to 10-30 grams carbon dioxide/km (g CO2/km) and is depending on the mix of electricity. (Gröna Bilister, 2011a) In our study we will use the mean value for our calculations, which is the maximum and minimum of the emission interval divided by two (equals 20 g CO2/km). Since it is unrealistic to use the top range we will use a range of 100 km for the electric car. (Vattenfall, 2012) That is because there are circumstances that will affect the driving range such as weight, weather, driving, and discharge of battery as well as usage of AC or instruments. Those factors will reduce the driving range and therefore a 100 km as maximum driving range is used in the simulation. Still a fully charged electric car can handle the majority of everyday travels. (Park Charge, 2012) Charging an electric car will in ordinary Swedish homes, with a regular fuse (10A) and one-phase wall sockets, take about ten hours. (Park Charge, 2012) In the model an assumption is made that an electric car will be fully charge, or near to fully charged whenever it is taken into use. The motivation to the assumption is that you could charge a battery or change to a fully charged at the carpool if needed. 2.4.2 Fuel driven cars In the study and model two fuel driven cars have been chosen to match Nissan Leaf and to answer to longer journeys as well as special needs. One of the cars is a Volvo V70 Ethanol/Gasoline and the other car is an Audi A3 Diesel.

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The cost to purchase a new Volvo V70 Ethanol/Gasoline car is circa 255 900 SEK. Since Östra Sala backe and its’ carpool want to have the environment in mind an assumption is made that these cars will use ethanol only. The fuel consumption for a Volvo V70 is 0,9 liters/10km and the emission of carbon dioxide is 80 g/km. (Gröna Bilister, 2011b), The fuel consumption of diesel cars is generally lower than gasoline and ethanol cars. The diesel consumption of this Audi A3 1.9 TDI EcoPower is said to be 0,45 liters/10km, but when Gröna Bilister tested the cars fuel consumption it showed that the car consumed a bit more. Therefore the consumption of diesel by this Audi A3 is in the simulation set to 0,5 liters/10km. The purchase price of a brand new Audi A3 is circa 226 000 SEK. (Gröna Bilister, 2007) The affect on the environment from diesel is 2,48 kg carbon dioxide per liter. When calculated with a consummation of 0,5 liters from the Audi A3, the environmental effect is around 124 g carbon dioxide/km. (Konsumentverket, 2011)

2.5 Cost of establishing parking lot In Östra Sala backe there are 250 parking lots and an extra 70 guest parking curbstones along the streets today. It is obvious that an expansion of Östra Sala backe will increase the need of parking lots. An regional study regarding city planning shows that a basement garage in the houses of Östra Sala backe will handle that need, given a parking norm of 1,0. The already established parking lots of Årsta and Sala backe in the area of Östra Sala backe will make place for the construction of Östra Sala backe. Thus the 250 parking lots that exist today will be removed. To solve the higher need of parking lots that will be a result of Östra Sala backe, there will be an 200 extra curbstones parking lots along the streets, that will be guest parking (0,1 parking per household) and the addition need of parking will be covered by basement garages in the houses (1,0 parking per household). There are also other potential areas for parking, on the housing association garden but this is the housing associations own responsibility. (Uppsala Kommun, 2010b) The cost of a regular basement garage parking lot vary in the interval 200 000-400 000 SEK depending on conditions but 250 000 SEK would probably reflect an average cost. The cost of a normal parking lot in a parking house can be lower but the parking house needs more area. A regular ground parking lot cost only 10 000 SEK. (Stockholm Stad, 2005, p. 1) If a curbstone lot is to be built the second price of ground parking is used.

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3. Methodology It was from the beginning of the project clear that the major aim was to investigate the parking standard of Östra Sala backe by develop a model and make a simulation. The result of the simulation should then mirror the parking norm when integrating a carpool in the area. Since Östra Sala backe will have the environment in mind it came naturally to extend the boundaries of the case study to be able to make an investigation of how the mix, emission and economic profile of the carpool would be. The scenario of Östra Sala backe is analyzed and studied through a stochastic simulation of a model. The reason why stochastic processes are used is to represent random variations in reality as well as random human behavior. The model is based on data and statistics obtained from authorities, reports as well as from local and regional surveys. The model has been developed during the time of the case study and limited by lack of relevant data as well as limited time given the study. The simulation itself is coded in Java, and it is a recursive simulation. This recursive type of code contains a method that repeats itself automatically until it reaches an end, in the case of this study a given time. In the case study fuel driven cars are those combusting ethanol, gasoline or diesel, thus an electric car will be referenced as electric car not fuel driven car. When calculating and comparing the amount of emissions to be saved if integrating a carpool in Östra Sala backe, the share of emissions saved is the result of a comparison with a scenario were all travels were by fuel driven cars. The mix of cars in that scenario is an equal share of ethanol and diesel cars but not one single electric car.

3.1 The gathering of data The data this study is based on is statistics of behavior patterns for transportation, varying statistics of the different studied areas as well as average figures of how long journeys are for different types of errands. All this data has been gathered in the purpose of simulating our designed model. The areas that are studied are related to projects that are in many ways similar to Östra Sala backe. The data from these two other projects could be adopted in Östra Sala backe due to the areas resemblances. Relevant data from these projects have been collected from reports describing visions and progress as well as the expected future. Furthermore has some general facts obtained from each projects website as well as from the counties websites. Figures that have been used are size, age distribution, method of transportation and parking norm. Data from two reports published by SIKA, Statens Institut för Kommunikations Analys, has been use to collect data about general Swedish travel patterns. Data and figures 17

about the distribution by type of transportation, of type journeys for each hour of the day and distribution of distance traveled have been obtained and used in the simulation of the model. To get the economical and environmental aspects of the carpool three cars has been chosen, each one with a relatively environmental friendly profile. Two of the cars are powered by a fuel, diesel or ethanol, and the third is a fully electric car. Carbon dioxide emission and purchase costs for each car have been obtained from websites organizations and companies as Vattenfall and Gröna Bilister.

3.2 The model The stochastic model developed in the case study is visualized in Figure 2. Initially there will be some amount of inhabitants, N (0), in the model (1), but with time this value of inhabitants will vary: N = N(t). The parameter inhabitant is not a fixed figure, but refers to the amount of inhabitants that is available and could possibly travel at a given time. The first process (2) in the model is to decide the number of travellers, the amount of the parameter inhabitants that are to make a journey. The number of travellers is stochastically decided and is a function of inhabitants, N (t), and the distribution of starting a travel, d (t). The stochastic process could be described as: T (N (t), d (t)).When the amount of travellers is decided a new process (3) will calculate what share of the travellers that will be travelling by car. To receive the number of car users, C, the stochastic process (2) is considered in relation to the actual scenario, S. The scenario will answer to a specific share of travellers that will choose a car as the mean of conveyance, thus: C (t) = T (N (t), d (t)) * S. In the category of car users all individuals will be included, not only drivers but also passengers. In the third (4) stochastic process the type of errand is to be decided. The type of errand is related to a distribution specific for each sort of errand. Furthermore they have an expected time of usage as well as expected distance of travel. The time of usage is needed to stochastically model a queue, Q (t), thus how many users there is a given hour of the day and when users is returned to be available inhabitants. The number of inhabitants, N (t), is therefore the same as the initial number of inhabitants, N (0), subtracted by the queue size, Q (t) as: N (t) = N (0) – Q (t). When the errand is definite it will affect what type of car that is to be chosen (5). Some errands have higher probability to choose a fuel driven car than others and vice versa. The final process (6) is to return the users when their expected time is up. The returned individual is once again available and the car is to be used again by some other user.

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Figure 11, The model

3.3 Criticism of the sources The reports where the data, about the three areas, have been gather is believed to be reliable since the data have been made by local and regional actors and each projects county. Data gathered about the carbon dioxide emission and cost for each car may not be entirely correct since the figures obtained from the organization might be affected by its’ opinion. The statistic about travel patterns for the general Swede is collected from a report made by an independent Swedish state department and should be considered reliable. The cost of establishing a parking lot varies. A parking lot built in a garage is more costly than a curbstone lot. Furthermore, the source of the cost of establishing a parking lot used in the case study may not reflect the actual of Östra Sala backe.

3.4 Statistics The statistic that has been gathered about travel patterns address how an average day looks like, whether it is a weekday or weekend/holiday. This would not affect the result since the simulation is over a large amount of “days” and the figures for an average day are being used. In the model an assumption has been made that all the car rents is driven by a single driver and have a maximum of one passenger. This data may not be reflecting the 19

average of travellers in Uppsala, but is a figure regarding the travellers per car in Stockholm. This data has been used since similar data related to the average of travellers per car in Uppsala was not obtained. 3.4.1 Distributions In the simulation several distributions have been crucial. People travel very differently depending on the hour of the day. To be able to decide if individuals will start a journey a general distribution of journeys have been used. There is one major extreme value in the morning, as well as a minor in the evening, which is strongly related to travels to and from the work place. But of course other types of trips, especially around the later extreme value, also affect the maximum. The data is based on a survey made during approximately one year between September 2005 and October 2006 the Swedish population made about 13,4 million trips each day, less than 5 billion in common during that period. (SIKA, 2007, p. 23) The basis of the statistics gathered from a SIKA study is a sample of 41 225 Swedes between the ages of 6-84 years. (SIKA, 2007, p. 47) 1400 1200 1000 800 600 400 200 0 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Figure 12, Distribution of journeys for each hour of the day, thousands During different hours of the day people are more or less prone to make different types of trips as seen in Figure 11. The distributions that has been used to decide what a traveller will do on its’ trip are those in Figure 13. The distributions regarding the errand of trip are not evenly distributed with several peaks. Travels and errands related to work and studies have peaks in the morning hours as well as in the afternoon and early evening. But the other distributions have their peaks at lunch and then reducing, except for the spare time journeys that has a second major peak just after the second work related peak.

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1250 1000 Work and studies

750

Service and shopping 500

Spare time journeys

250

Other journeys

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Figure 13, Distribution of journeys by type for each hour of the day, thousands A large majority of journeys that begin during the later afternoon and evening are assumed to be journeys back home, which means that those journeys start at work and not at the carpool. Furthermore that a new car for the journey back home will not be rented once again. Therefore the model has been built in a way that an individual at work or in school will not rent a new car from the carpool during this period. In Sweden, 73 percent of the working population is working full time, which is 40 hours per week. The figures are measured during year 2009 and it based on information from circa 60 000 persons between 16 and 64 years. (Landsorganisationen i Sverige, 2009) An assumption is made that this study and statistics mirror the area of Östra Sala backe as well as the whole population of Östra Sala backe. These 73 percent are assumed to already have rented a car when the trip goes from work to home during hours afternoon or early evening hours, thus been removed from the distribution. But the other 27 percent assumed not to be working nor studying is able to rent a car for work and studies related journeys during hours 15-18. The figures of Figure 13 has been recalculated to a new distribution that reflects how many cars that need to be rented for work and school related trips from during hours 1518, when the working and studying population of 73 percent are kept in mind. The second peak of work and studies travels during the later hours still exist but the quota between work and school related trips versus total trips has decreased with this assumption, therefore fewer travels with this errand will be made during the afternoon and evening.

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1250 1000 Work and studies

750

Service and shopping 500

Spare time journeys

250

Other journeys

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Figure 14, Calculated distribution of journeys by type for each hour of the day, thousands To visualize the amount of trips made on the different hours it is useful to see to the same statistics as above, but in percentage instead. Small numbers causes the noncontinuity in the early hours. But the differences shows that a large majority of the trips in the night and morning is made related to work and studies, but later on in the day the distributions are more evenly distributed. During lunch you can see that a large majority of the trips made are for daily errands and such. 100,0% 80,0% Work and studies

60,0%

Service and shopping

40,0%

Spare time journeys

20,0%

Other journeys

0,0% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Figure 15, Distribution of journeys for each hour of the day 3.4.2 Long way journeys Each day in Sweden there are circa 200 000 long distance journeys made, that is longer then 100 km. That many journeys a day makes around 73 million long distance trips a year in Sweden. (SIKA, 2007, p. 34) That number should be compared to 5 billion, which is the sum of trips the Swedish population made during a duration of a year between fall 2005 to fall 2006. That conclusion of the comparison is that about 1,5 percentages of all trips made by Swedes during a year are long journey trips. The dominating mean of conveyance was the car with a segment of 68 percentages, followed by airplane and train at 11 percentages together. (SIKA, 2007, p. 22)

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3.4.3 Distance of different type travels The distance travelled is not equal amongst the different type of journeys. Therefore statistics related to each sort of journey has been used in the simulation. Lack of data regarding the time consumed by each kind of errand has forced assumptions. 3.4.4 Work related journeys In 2005 the distribution over distance to work for inhabitants of Uppsala were not equally distributed. (Statens väg- och transportforskningsinstitut, 2009, p. 68) One reason could be that a noticeably segment travel out of county to Stockholm to work or travel in other directions to other cities. Of course a share of the population also travel within the county to work. 25,0%

21,9%

20,0%

20,0% 15,0% 10,0%

6,6%

7,8%

9,8%

10,4% 11,3%

9,8%

5,0%

0,7%

0,3%

0,4%

1,0%

0,0%

Figure 16, Distribution of the distance to work I Uppsala County in 2005 The statistics of length to work is used to determine what sort of car to be used in the simulation of the model. Travels associated with services or shopping errands have an average distance of 30,5 km (SIKA, 2007, p. 66), thus within the range of an electric car. But it is unrealistic that all those travels could be made with an electric car. Therefore an assumption has been made in the study that fuel driven cars will answer to 10 percent of the trips associated with these tasks and errands. The average distance for journeys related with spare time and other purposes is just more than 51 km. The critical total distance of journey by an electric car is in this study 100 km, as discussed in the chapter named Electric car. An assumption has been made that an electric car is to be chosen if possible, that is if the one-way distance is below or equal to 50 km. The reason why that assumption is made is because Sunfleet, an established carpool, recommends its’ members to use a fossil fueled car at distances over 50 km. (Sunfleet, 2012) Furthermore that assumption will in the model mean that there is almost a 50-50 chance to choose an electric car instead of a fossil car for these errands. To be certain that a electric car will not be chosen when it is doubtful that the

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driving range is enough, the probability has been set to 45 percentages to chose an electric car and 55 percent to chose a fuel driven. The distribution of length of long way journeys is not even. The average distance is 185 km but the median is 155 km. There is a 25 percentages percentile at 110 km and a 75 percentages percentile at 260 km. (SIKA, 2007, p. 34) Therefore the median has been used as a average distance mark with the motivation that most journeys will be around 155 km. The assumptions regarding the choice of car is not affecting the parking standards of the simulation, only the results related to emissions and economy. Those figures are not supposed to be exact but rough indicators. 3.4.5 Time of rent A row of assumptions has been made to be able to decide the time individuals will rent their cars, that is the time being away. Furthermore is every travel type associated with an average driving time (SIKA, 2007, p. 68), back and forth. Those numbers have calculated into hours from minutes. Those driving times have then been added to the time of the rent. Table 1, Average driving time for each sort of errand Work and studies

Service and shop

Spare time

Other trips

0,5 hours

0,725 hours

1,1 hours

1,01 hours

3.4.6 Time of rent related to work and studies The foundation of the reasoning about times of rent in the study is that the majority of individuals, the car users, will begin to return home with the cars in the late afternoon or evening. Therefore, an assumption have been made that a large segment of those leaving in the morning hours will have an average day away of eight hours, but those who leave for work later in the morning will not work a full day. Further assumptions are made that those who leave for work later in the morning will work at least two hours, but a time are randomly added of up to four hours to their workday. That will make those individuals reach the second distribution top of work travels in the later hours, those hours where most people return home. But for all the other hours of the day as well as night the minimum time of a rent is the driving time but the maximum is the driving time plus up to an extra three hours. The adding is in this case a randomly chosen number with an even distribution, which is that every hour between three and one has the same probability to be added. It has earlier been presented that the distribution of work and studies travels has been slightly modified. That modification is in line with the reasoning rent time of work and 24

studies journeys. If an individual have a workday of eight hours, that individual is not supposed to pick a second car from the pool but drive the already rented car home. 3.4.7 Time of rent related to other trips The time of travelling associated with the different type of errands is not that unlike, they all are relatively close to an hour. But the time gone on the different types of errands are not equally alike where an assumption is made that travels associated with individuals spare time have a maximum of five hours and all other trips have a maximum of three hours. The actual time of being away is decided by multiplying a random number to the specific time associated with the type of errand so that the number varies between the time of travel and the maximum time of being away plus the time of travel. The model’s time away related for spare time errands, shop and service errands and other errands, thus that is not long way journeys nor work or study related, are estimated. It is not possible to justify time gone in these types of errands with the statistics and data obtained in this case study. Further it is not possible to motivate the time away in the same ways as for work and study trips since there are no distinguished peaks for the type of travels. Therefore the time of errands are estimated. 3.4.8 Long way journeys and time of rent For long journeys there is a fifty percent chance of being away one day, if you are away longer the time of being away is 2.8 days (Rese- och turistdatabasen, 2009), equal to just over 62 hours. In the time away of a daytrip is assumed to be between eight and twelve hours. The actual time is randomly decided and each hour has the same probability of being chosen. For longer trips it will be added an extra few hours as time of travelling back from the long journey. That time should reflect a car driving a distance of half the median long journey distance (77,5 km) and has been assumed to be an average of nine hours. The total time of several days long journey is then the sum of the hours, a total of 71 hours.

3.5 The purchase cost of the cars In the model the assumption has been made that the cars are not purchased at a full price. This has been made due to factors such as quantity discount, different tax reliefs’ etcetera. The assumption has been made that the cars are purchased for 90 percent of its total price.

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3.6 Passengers and car travels The traffic in the inner city of Stockholm is not equal to the traffic of Uppsala, but in the model the lack of data makes it impossible to see to passengers without making the assumption that the traffic is comparable. One major difference is the traffic tolls, even though they are not affecting the amount of travelling companions other factors could. The average of travellers per car is 1,27 in Stockholm during year 2006, and has not significantly changed since 1994. (Transek, 2006, p. 22f) To be able to see to the passengers of Östra Sala backes car travels the statistics from Stockholm will be used. The figure will not be exact and therefore affect the result slightly, but not to use the statistics would be utterly misleading of the results.

4. The simulation and data The building of Östra Uppsala backe has in the simulation been simplified in a few ways, mostly because lack of data or statistics. The major objective of the simulation is to generate a picture of the amount of cars needed in Östra Sala backes carpool as well as what mix of cars that is to be required to serve the needs of the inhabitants. Furthermore it is to gather data viewing how different errands could be made with an environmental friendlier car instead of a fuel driven car and how much of the emissions of carbon dioxide that could be reduced. A third objective of the model is to receive an economical picture. Enough data is to be collected to be able to make a rough calculation of how much that could be saved or lost by establishing a carpool and therefore reduce the number of parking lots. The model will not reflect the exact distribution of how cars are used, but a rough picture will be received. The exact usage of cars is for the major purpose of this case study not relevant because the sought information is the everyday extreme values and a rough estimation of the length of driving for each type of car. To get an accurate simulation of the amount of cars and the mix of the carpool the simulations interval must be relatively extensive. When executing the simulation over a few hundred days there are some varies, but when reaching a thousand days those differences are reduced. Therefore our simulation has a limit at 1095 days, equal to the days of three years. To chose a larger amount of days would only give an insignificantly change on the result, if any change at all. To get an accurate parking standard the simulation is executed five times for each scenario and for different amount of individual members of the carpool. The average value is then calculated from the executions and then plotted in MATLAB. To secure the results the simulations a number of single runs have been compared and plotted with those average values. An assumption has been made that if a percentage of the inhabitants are members of the carpool it is equal to the same percentage of the households being members in the carpool. This assumption has been made to calculate the parking per household for Östra Sala backe. There could be a variation that some 26

households consist of more members than others, since this variation can change the result both negative and positive and the simulation is executed over a large amount of days this would only affect result slightly, if at all.

4.1 Limitations of the simulation This report will only consist of one single carpool. The members of the carpool are only able to pick up and return the car at the carpool in Östra Sala backe. If the major carpool of Östra Sala backe could be integrated with other carpools the usage of cars would be more effective. Due to lack of time, statistics and insecurity of where to place these minor carpools, no scenarios with several carpools will be simulated. Figures about the driving time each car user spends for each errand has been found in the unit minutes. These figures have been rounded up to the closest integer to get the figures in the unit hours. This is a reasonable since the cars are rented by the hour. The time each errand are estimations and should be kept in mind. There are still some years until Östra Sala backe will be built. Because of this there is a possibility that the maximum range an electric car could be driven will improve. If this improvement becomes reality it will affect the mix of our carpool and therefore the affect on the environment as well as the economic aspect. The figure of maximum range an electric car could be driven is lower in reality and in this study we have relied on recommendations from established car pools, their recommend maximum range of an electric car. If the range should increase in the years to come, the results of this report might change. An aspect that might not exact is the amount of passengers in Östra Sala backe since no reliable figures were found regarding Uppsala. The data and figures used are regarding Stockholm, a larger city with other traffic needs and behaviors. A result of this is that the results of the simulation might be slightly misleading. When the cost to build all parking lots of Östra Sala backe have been calculated, an assumption that no other parking alternatives then curbstones parking lots and basement garage have been built. Therefore our parking lots probably will be more expensive then the actually cost of parking lots in Östra Sala backe. The reason is that property owners may build parking lots of their own, thus the calculated number of parking lots will be slightly higher than in reality. It is because of this assumption a limit of the simulation that the number of externally built lots is not taken in to mind. Notable is that electric cars is influenced by weather and seasons, especially in a climate alike Sweden’s with cold winters. Driving an electric car in a cold climate, with an AC and other comfort functions will affect the driving range due to extra stress on the battery. That is a factor outside of the simulation and therefore a limit since it is not regarded in the simulation.

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The distributions describing when to start a travel, that is specific for the hour of the day, used in the simulation are not mirroring the travel patterns of weekdays and weekends, nor fluctuations over seasons. The figures are general and will affect extreme values slightly. For example, the distinct peaks during the early morning hours related to work and studies will be slightly lower than in reality since statistics from weekend trips are taken into the calculations and vice versa. Since the simulations extreme values are lower than it would be in reality there will be a slightly shortage of cars during this period.

4.2 Different scenarios In the simulation two alternatives have been used. One alternative was Östra Sala backe will be built to inhabit 4500 persons and another alternative were Östra Sala backe would inhabit 5600 persons. These population sizes mirror the maximum of possible inhabitants as well as the minimum of Östra Sala backe. Any size variations outside of this interval will not be reflected over in this study. A few different scenarios have been used in the simulation to investigate how sensitive the parking norm is to variations of share using the car as mean of conveyance. The different scenarios are scenario 26, scenario 34 and scenario 43. Each scenario has a different amount of car usage that should reflect in which extent the population uses alternative transports, the figures in their names. To be able to get relatively fair results from the simulation we have defined these three scenarios. The scenarios are estimations of the car use for travel in Östra Sala backe, but with statistics taken from reports of Västra Hamnen. 4.2.1 Motivating the different scenarios Scenario 43 is an estimation of what Västra Hamnen expects of its’ distribution of transports without any major changes in infrastructure or public transport in year 2015. A second scenario is when some changes are made, but not major ones and changes that are very costly. In the case of our second scenario the estimated car usage would be 34 percentages, therefore the name scenario 34. If there are major changes and expensive changes to be made, Västra Hamnen will have an even lower percentage of car usage, as low as 26 percentages. This will in the model be a scenario with least amount of car usage, the scenario named Scenario 26. Both Östra Sala backe and Västra Hamnen want to be role models when it comes to environmental aspects and therefore they want to encourage environmental lifestyles of its’ inhabitants. The different areas of Västra Hamnen, Hammarby Sjöstad as well as Östra Sala backe will make different actions to give their populations alternative transport solutions and exactly how the sum of actions will affect the usage of car is uncertain, especially in Östra Sala backe that has not yet been built. The reason why statistics of car usage has been taken from reports of Västra Hamnen is because the areas are similar. Notable is that Hammarby Sjöstad already have made a change, they 28

have a low parking standard as well as good alternative transports and available carpools for their inhabitants. The scenarios of Västra Hamnen are relatively comparable to what is to be expected in Östra Sala backe. Therefore it is assumed in our simulation that the percentages of Västra Hamden’s scenarios are equal to those in Östra Sala backe. This means that the lowest car usage to be simulated is 26 percentages, which is reasonable since Hammarby Sjöstad has a car usage of 21 percentages. It would not be fair to use the number of Hammarby Sjöstad’s car usage since there in Stockholm are car tolls and queue problems to mention a few differences that will decrease car usage.

4.3 The simulation of Östra Sala backe The alternatives introduced in chapter the previous chapter Different scenarios is just simplification for the model of the case study. It is not realistic for all inhabitants to move in all at the same time. But since the purpose of the model and simulation is to get the maximum parking standard when the area is completed the simulation starts at a point when all the inhabitants have moved in. In this case study a few assumptions and simplification has been made due to lack of data. These assumptions and simplifications will affect the result slightly. The model is simulated over a considerable amount of time, 1095 days. The simulations were then repeated for each scenario five times to receive an average number to use in the plots. The final result is the average value from these five executions. When assumptions and simplifications have been necessary they have been made rounded up to get a higher parking standard. The priority has been made that it is better that the result shows a higher parking norm then in reality instead of a lower to avoid the problems that the carpool is undersized when implemented. Even if the value of maximum of needed cars will differ slightly the quota between cars needed and residents, parking norm, will only vary a few percentage. This reports parking standard will give a reliable result and be a good indicator on what the actually parking norm for Östra Sala backe will be. 4.3.1 Deciding the number of travels by car To decide how an individual will act in some way a random number is drawn between 0 and 1 and then compared to statistics, which is some figure obtained from a distribution or other data. In the simulation a number of comparisons like this are made to make sure that each individual acts randomly. One of the probability distributions describes the probability that a journey will begin, regardless of mode of transport and type of trip. Initially all residents are able to chose to begin a trip, but if residents randomly uses a car they become unavailable. The amount of individuals able to choose to travel varies with time. The process of deciding how many travellers there are every hour is made by randomly choose a number for each available individual and then comparing the obtained number 29

with the specific probability that a journey should start during the hour of the day. The sum of all those randomly decided travellers is the total population travelling that hour, and that sum is not available to make a new trip as long as they have their imaginary car. 4.3.2 Type of trip and means of conveyance When the amount of travellers for an hour is decided, it is possible to get the share that will choose to travel by car thus the given scenario provides a percentage. But since the percentage is a decimal numeral the product is a decimal number and in the simulation all shares of persons (when there is a decimal number referring to individuals) that number is rounded up. The rounded product of travellers and the percentage given by the scenario is the number of travellers by car specific for the hour. Knowing the process of choosing travellers by car it is obvious that the number of travellers grows with growing population, but it does not mean that the parking norm grows with population since it is a quota of maximum need and residences. Further, to ensure what errand to be made a new random number is drawn between 0 and 1. This number is then compared to probabilities, obtained from distributions for each type of errand in a given order. Those distributions are mirroring the type of journeys made during specific hours of the day, regardless of type of travel. The categories are work, service or shop, spare time travels, longer trips and other kind of trips. Every type of journeys has an own distribution over the hours of the day except the longer trips that is a percentage of every trip made. If the number is less then the probability it is compared to the next errand probability is tested against. The procedure is aborted if one errand is chosen, thus only one errand per individual can be chosen. Long travel trips are checked against a random number initially before the random number is checked against the other distributions. If a long trip is begun, it is not sure that the traveller will choose a car by travel of means. To decide if the car is to be used for the travel a random number is checked against a probability, and if a car is used the population of car travellers is reduced by one otherwise that individual will start another travel by car since it is already a car user. There is obviously some chance that there will be more travellers per car then just the driver. To decide whether a passenger should join the driver when a journey begins again a new random number is tested against some probability. If the comparison is true not just the driver is made unavailable for some future but the passenger as well. When knowledge of what kind of trip that is to be made statistics can be used to decide what kind of car that is to be chosen. The simulation is designed to pick an electric car if it is possible, which it is if the distance of the travel is within the driving range of the electric car. Probability of choosing an electric or fuel driven car is tested by comparison as earlier described, but the probability varies among the different types of travels. 30

If it is known the number trips, what trips as well as what type of car used it is possible to use the average distances to calculate the total distance travelled for each specific hours. Further more it is possible to calculate the distance driven by each type of car as well as in what type of errand. To determine the cost of all trips and the costs related to the different type of cars the known distances travelled is multiplied with the driving of electric and or fuel driven cars. The cost of driving is very unstable, it shifts almost every day because of the differences in cost of electricity and fuel. These two constants, the cost of electricity and fuel will therefore be estimation for the day of us but it should provide an approximate value of the actual cost. In the same way that driving costs are calculated, an estimation of emissions can be computed. The simulation will give us the length of all trips together and the total cost for those, but also the length and specific cost for each type of car, electric and fuel driven. Furthermore data needed to decide the mix of the carpool will be provided thus the maximum number of cars needed during on hour will be known as well as the maximum fuel driven cars and electric cars for an specific hour. 4.3.3 Calculating the parking norm The parking norm of the carpool is calculated in the simulation. The calculation in the simulation is based on the maximum need of cars divided by the number of residences. But given a share of members, the amount on non-members will affect the total parking norm of the whole area of Östra Sala backe. To receive the total parking norm an addition of two products are made. One of these products is the multiplication of the share of members in the carpool and the simulated parking norm of the car pool, the second product is the share of non-members multiplied with parking norm of Uppsala (1,1). The total parking standard of the different scenarios and population sizes is then plotted to be simpler to understand. 4.3.4 Choosing a car- example calculation This is an example is to show how a car is chosen in the simulation. In this example Östra Sala backe has not been expanded and therefore the population size is 4500, thus the initial parameter inhabitant in Figure 2 is 4500. Furthermore the share of membership is set to 30 percentages, which means that a share of 30 percent of the parameter inhabitant is available to the carpool (1). The actual amount of the parameter will be 1350 individuals. The second process (2) in the model is to decide the amount of travellers. During the 9th hour of the day the probability to make a journey is 10,34 percentages. Since every individual is given a unique random number between 0 and 1 that random number is compared to the actual probability to travel. In this example the random number is decided to be 0,0341. Since the random number drawn is lower than the probability to make a journey for that specific hour, random number < probability, a journey will be made by that inhabitant. This process of comparing random numbers to the probability 31

will be repeated for each single available inhabitant. A new random number is drawn for every inhabitant and compared with the probability of the hour to get how many inhabitants that will do a journey during that specific hour. In this example the amount of travellers that is to be making a journey during the 9th hour is 150 inhabitants.Th e amount of journeys that will be made by car is based on a scenario (3). In the simulation of the model three different scenarios is used: Scenario 26, Scenario 34 and Scenario 43. The figure in the name of the scenario indicates the percentage of journeys made by car. In this example Scenario 26 has been chosen and therefore 26 percentages of all travels will be made by car and the total number of car users in the example is calculated to be 39. When the amount of car travellers is decided the unique errands of the travels is to be decided (4). Again comparing probabilities just as in the second process but with a different distribution will decide what errand an individual will be making. A new random number will be compared to a probability of a type of errand and if the random number is lower than the probability that errand will be chosen. If it is not chosen the random number is compared to another errand until an errand is chosen. In this example the probability of errands related to work and studies is considerably higher than the other errands which is shown in the outcome of the example: Work and studies (30 travels), Service and shopping(4 travels), Spare time journeys (4 travels), Other journeys (1 travel).Further in this example only the errand category work and studies will be examined. When it is known how many car users each errand is given for the specific hour it is desirable to decide what sort of car that is to be used, fuel driven car or electric car. This process is mainly based on the expected distance of the travel, for the work and studies see Figure 15. All journeys that have a distance less than 50 km will chose an electric car, and the share of travels that is expected to be lower than 50 km for the examined category of errand is 86,40 percentages. Once again every single users specific for this category of errand will be compared to this probability to choose an electric car. In this example the randomly decided number is 0,6134, which means that an electric car is chosen as a mean of conveyance. 4.3.5 Deciding the parking norm of Östra Sala backe – example MATLAB take in values of the carpools parking norm from the simulation, and in this example it is 0,27 and the parameters for the simulation are: 5600 inhabitants, 75 percentage membership in the carpool and the scenario is set to be scenario 26, thus 26 percentage of the traveller uses car as a mean of conveyance. To decide the carpools share of the final parking norm the share of members in the carpool is multiplied with the simulated parking norm (0,27), which equals a value of 0,2025. But since there are a considerable share that is not members of the carpool and will have a different parking norm a similar calculation is made to decide their impact on the final parking norm. The share not members of the carpool is multiplied with the actual parking norm of Uppsala (1,1). This calculation equals a value of 0,2750. These

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received values added together will answer to the final parking norm of the example, thus the actual parking norm will be 0,4775. 4.3.6 Calculating economical impact – example The parking norm is already decided and in this example the calculated parking norm above will be used (0,4775). The number of parking lots needed is calculated by multiplying the parking norm with the amount of residences in Östra Sala backe, in this example set to 2500 residences. The multiplication is rounded up to the closest integer and is 1194 parking lots. If the population size is 5600 inhabitants a number of 200 curbstone parking lots will be built and if the population size is 4500 basement garages will answer to the parking needs and no curbstone parking lots will be built. Furthermore the circumstances states that 70 curbstone parking lots will be usable for the higher amount of population and will therefore not be built. This means that the amount of curbstone parking lots to be built is either none (4500 inhabitants) or 200 (5600 inhabitants). Since this example is examining a population size of 5600 inhabitants 270 curbstone parking lots will be removed from the needed parking lots, thus leaving the amount of garage parking lots. To receive the actual cost of building the parking lots the amount to be established is multiplied with the cost of establishing one parking lot (250000 SEK). When the costs of establishing garage parking lots is calculated the cost of establishing curbstone lots is calculated in the same way. The cost of establishing multiplied with the amount of parking lots to be built. These two costs added together answers to the total costs of establishing all needed parking lots in Östra Sala backe and equals 233 million SEK in this example. The calculated cost of establishing parking lots with an integrated carpool is already calculated. But to receive the capital saved the cost of establishing parking lots without a carpool must be calculated. It is done in exactly the same way as described, only using a parking norm of 1,1. This calculation gives us a figure of 622 million SEK. Thus the capital saved if building lesser parking lots, thus when integrating a carpool, will be the difference of the two calculated costs, which equals 389 million SEK. To calculate the total investments in cars for the carpool two calculations is made, one for electric cars and one for fossil cars. Distribution over the kind of cars in this example is based on the optimal mix. The optimal mix has a 45-percentage share of electric cars. To get the number of electric cars needed the parking norm(0,4775) is multiplied with the number of residences(2500) and the share of electric cars(0,45). This is rounded up to closed integer, 538. To get the cost for the electric cars the number of cars needed(538) is multiplied with the cost for buying one electric car. Which gives the 33

total cost of electric cars (172 160 000 SEK). To calculate the number of fossil cars needed the parking norm(0,4775) is multiplied with number of residences(2500) and share of fossil cars(0,55). The fossil cars are then divided with equal share of ethanol and diesel cars. This rounded up to the closest integer to get the total amount of ethanol cars(329) and diesel cars(329). To get the total investment in fossil cars the number of ethanol cars is multiplied with the cost of one ethanol car (2559000). This is then added on to the multiplication of the numbers of diesel cars with the cost of one diesel car (226000). This gives the total cost for fossil car (158545100 SEK). To get the total investment in cars for the carpool the cost for electric cars (172160000) is added to the cost of fossil cars (158545100). Which gives the total cost of investing in cars for the carpool (330705100 SEK) To decide if it is economically viable to integrate a carpool the cost of establishing parking lots is compared to the cost of investing in the cars of the carpool. By subtracting the cost if investment in cars from the capital saved when building fewer parking lots it is easily decided if it is economically viable. If the subtraction is less than 0, it is not viable. In this example this calculation will be economically viable since the capital of saved subtracted by the capital of investment (rough figures: 389 million subtracted by almost 331 million) equals almost 59 million SEK. This means that after purchasing all cars of the carpool there will be 59 million SEK left of the capital saved when building fewer lots. 4.3.7 Calculating environmental impact-example To calculate the emissions a scenario with a carpool is compared with a scenario without a carpool. The simulation with 100% membership gives the total distance travelled with or without the carpool, for 5600 inhabitants and scenario 26 = 30 008 500 km. The simulation calculates the distance travelled by all cars as well as the amount of carbon dioxide emissions related to driving the cars. These calculations are dependent on the type of errand since the errand is related to an expected average distance and some other parameters. Given the type of errand it will affect which type of car that is to be chosen, an electric or fuel driven car. When the errand and car is decided the simulation adds together the full distance of all electric cars and all fuel driven cars. The sum of those parameters is the total distance driven of all cars of the carpool. The amount of emissions is directly proportional to the distance related to the mean of conveyance. The distance travelled by electric car multiplied with the emissions of an electric car per kilometer gives the total amount of emissions related to electric cars. Furthermore half the distance travelled by fuel driven cars multiplied with the emission constant of diesel cars answer to the total amount of emissions of diesel cars. The other 34

half is related to ethanol cars and the total amount of emissions is calculated in the same way as diesel cars. To calculate the amount of carbon dioxide emissions the distance travelled is needed. In this example the distance will be 30 008 500 km and it reflects a simulation where there were 5600 inhabitants during Scenario 26 and the membership was 100 percentages. Each means of conveyance answer to an amount of emission per kilometer. Emissions from a diesel car in kilometer = 0,124 kg CO2/km Emissions from a ethanol car in kilometer =0,080 kg CO2/km Emissions form a electric car in kilometer = 0,02 kg CO2/km To receive the total amount of emission related to a carpool with only fuel driven cars a number of calculations is to be made. First the distance of fuel driven cars is divided in two, which means that the diesel cars is driving the exact same distance as the ethanol cars. This is an assumption of the case study. This distance received is then multiplied with the amount of emission per kilometer for both ethanol and diesel. The sum of the two amounts of emissions will answer to the amount of emission of fuel driven cars, which in this example is 3 060 867 kg CO2. To calculate the emission when integrating a carpool the emission from each sort of car is calculated and the sum of these calculations gives the total emission. The example will use a mix of about a third of each sort of car (Electric car 30 percentages and fuel driven cars 35 percentages each). In the optimal mix where there are an unlimited amount of cars the electric cars answer to about half the distance travelled, more exact 49 percentages. When simulating it is shown that the share of electric cars will answer to just about the same share of the distance travelled. Thus the emission from electric cars is calculated by multiplying the share of electric cars (0,3) with the distance traveled. This distance received is the distance travelled with electric cars and to gain the amount of emission this distance is multiplied with the emission of an electric car per kilometer (0,02 kg CO2/km). Emission from electric cars will in this example answer to 180 051 kg CO2. The emission of fuel driven cars is calculated by multiplying the share of fuel driven cars (0,7) with the amount of emission of fuel driven cars already calculated (3 060 867 kg CO2).The emission of fuel driven cars in this example is 2 142 607 kg CO2. The total emission from the carpool is calculated by adding the emission from electric driven cars (180 051kg CO2) with the emission from fuel driven cars (2 142 607 kg CO2) and this sum is 2 322 658 kg CO2. Comparison between the emission from the optimal mix of cars and the emission without a carpool gives the emission saved from integrating a carpool. To calculate the share of emissions saved when integrating a carpool a comparison of the total emissions with and without a carpool is made. When no carpool was integrated the amount of carbon dioxide emissions where 3 060 867 kg CO2, and when integrating 35

a carpool 2 322 658 kg CO2. The amount of emissions when integrating a carpool, rounded to closest integer in this example, is 76 percentages (2 322 658 / 3 060 867). This means that the amount of emissions to be saved is 24 percentages (100-76 percentages).

5. The results of the simulation To visualize the variation of the parking norm the results of several simulations of the model have been plotted. The figures in following chapter pictures the different scenarios for the two alternative sizes of the population as well as varying share of members in the carpool. From the plots it is possible to understand how these different factors affect the parking norm in Östra Sala backe. The maximum fuel driven cars, electric cars and the total maximum cars needed vary significantly with percentages of the population that are members in the carpool as well as the amount of inhabitants. The fuel driven cars do not have any driving limitations as the electric cars have, therefore the maximum of fuel driven cars is the base of the carpools mix. To reach the maximum cars demanded by the population, electric cars are added to the maximum number of fuel driven cars. The simulation of the model revealed that an expected mix of cars is just under a third electric car and each ethanol and diesel cars were just above a third. An interesting result of the simulation is that the quota of maximum fuel driven cars and the total maximum of cars only varies slightly for the optimal carpool, were the maximum need of types of cars always were met. If so the share of fuel driven cars was always within the interval of 54-56 percentages of the maximum demanded cars. This means that a percentage of approximately 55 percentages of fuel driven cars should cover the need of the population regardless of size or scenario. In the model of the case study the optimal circumstances, were there are unlimited numbers of all sorts of cars, are examined and given these circumstances the fuel driven cars answers to just over half of the total length driven, rounded to 51 percent. Furthermore the emission during these optimal circumstances is not equal between electric cars and fuel driven cars. The fuel driven cars answer to a major share, 84 percentages, of the carbon dioxide emissions. Furthermore the total electricity costs for distance driven by electric cars is far less than the costs of fuel for the ethanol and diesel cars. When simulating the model during optimal circumstances a comparison of driving costs reveals that the fuel driving costs answers to 75 percentages of the total driving costs. If this optimal situation were to be compared to a situation where there only were fuel driven cars, the emission would be lowered by 40 percentages. It is an interesting scenario but not at all economically viable, nor realistic. The simulation of the model shows that a suitable mix for inhabitants of Östra Sala backe is expected to be just below a third electric cars and the remaining segment equal shares of diesel and ethanol

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cars. That mix implies that the shares of emission and driving costs will be different. The emissions would in this realistic scenario be lowered by 24 percentages. The simulations parking per household is shown in Figure 17 and Figure 18. The plots show a close to linearity of the results. The different scenarios have almost linear curves, thus the parking norm needed decreases linearly with the number of members in the carpool. Furthermore it is visualized that the different scenarios only have a small differences in the gradients. The simulation plots are drawn with a step length of 25 percentages, which is five points total. It is possible to use a shorter interval, but the results would be very similar if not identical because of the close to linearity. Executions of the program have been made with shorter interval with almost identical results as in Figure 17 and Figure 18.

Figure 17, Parking standard with 5600 inhabitants

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Figure 18, Parking standard with 4500 inhabitants There is only a slightly different parking standard for the three scenarios when the percentage of members is low in the carpool. When the percentage of members increases the result differ more among the three different scenarios. This is because of when the percentage of membership are low the up to date parking standard of 1,1 is dominating and the carpools influence on the parking standard is insignificant. Further the result Figure 17 and Figure 18 shows that the parking per household does not differ that substantially at 100 percentages membership either. The different Figures do have almost the same linear curves, just a slight difference of their differential quotient. Still it is a sizeable difference in inhabitants, 1100 more inhabitants in the plot of Figure 17. That conclusion is that the population does not have a major impact on the parking norm but it is the percentage of members and the scenarios that will affect the parking norm.

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Figure 19, Costs of establishing parking lots given 5600 inhabitants

Figure 20, Costs of establishing parking lots given 4500 inhabitants Figure 19 and Figure 20 shows a similar decrease of building costs is a result of the linearly decreasing parking norm. Since the costs of building parking lots is depending on the amount of cars, thus the parking norm. The differences are obviously changing with the numbers of residences, membership and the number of cars outside the carpool. Depending on scenario, a very rough estimations are that the maximum amount of 39

capital to be saved is about 60-80 percentages of the total cost, but a more reasonable valuable are a share of circa 18 percentages at the segment 25 percentage membership in the carpool. With a 50-percentage membership, the capital saved could be as much as 30-35 percentages of the total cost.

Figure 21, Variation of costs between the scenarios The figure above is a plot where the maximum and minimum costs of building parking lots (Scenario 26 and Scenario 43) are compared to Scenario 34. The comparison shows the increase or decrease of costs in relation to the average Scenario 34, thus the zero line is the costs of establishing parking lots in Scenario 34 for both 4500 as well as 5600 inhabitants. Figure 20 shows that a fair amount of capital could be saved or lost even if a small share of the populations’ behavior is changed in some way.

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Figure 22, Variation of costs of purchasing cars to the carpool given 5600 inhabitants

Figure 23, Variation of costs of purchasing cars to the carpool given 4500 inhabitants By comparing the costs saved when establishing fewer parking lots and the costs related to purchasing the cars of the carpool, it is shown that the capital saved outweighs the costs of purchasing the cars of the carpool as long as the share of members in carpool is not below circa 10 percentages. If it is below 10 percentages the costs of purchasing all cars will not be covered by the capital saved when building fewer lots. If the membership of the carpool is somewhere around 30 percent, the capital saved from 41

building fewer parking lots will be approximately for Scenario 43 circa 25 million SEK and for Scenario 26 the capital saved is 75-175 million SEK. The significant variation is because of the great variation of cars needed in the different scenarios and for the varying population size. Even though the amount of capital is sensitive to changes in population and scenario, the capital saved with a membership of 10 percent or more will always cover the costs of purchasing all cars to the carpool. The capital saved will always increase with share of members in the carpool as well as lower segment of car trips. If the population is smaller, 4500 inhabitants, the economical efficiency of the carpool will not be as high as if a larger population were members in the pool. A lower population causes the amount of cars per member to increase, which also means that the cost of investment in cars will increase per capita since more cars are needed.

5.1 Sensitivity analysis The queue time in the simulation will affect how long inhabitants will be busy, thus unable to pick a new car from the car pool. If the length of the queue time is to be lowered the amount of travels will increase since more individuals are able to make trips, and statistically more trips will be made. The result is an increasing amount of demanded cars and a longer total distance travelled, further affecting the parking norm and emissions. If the queue time is to be higher, the result will be the opposite. Incorrect distribution of hourly travels will only affect the amount of demanded cars not anything else. This is because if the distribution was to be changed, a loss or gain in amount of travels will be answered to another hour since the sum of all hourly percentages always will be 100 percentages. But the distributions of type of errands will not affect the number of cars in the simulation, only the distance traveled and by what mean of conveyance. Thus the distributions of errands will affect the amount of carbon dioxide emissions not the parking standard. The parking norm and the economical as well as the environmental aspects are not equally sensitive to the same parameters. The parking norm is the quota of the number of cars and number of residents. Since the amount of residents does not vary significantly, the factor that affects the parking norm is the amount of cars. The parking standard is notably sensitive to the share of inhabitants that chooses to travel by car, thus the scenario will impact the parking standard considerably. By lowering the share of the population that chooses to travel by car it is possible to more rapidly decrease the parking norm. A second parameter of significant impact is the quota of members in the carpool. If the percentage of members could be increased, the parking norm needed will be decreasing. A third factor with a noteworthy with a minor impact on the parking norm is the population’s size, in this case study. By having an larger size of population the efficiency of the carpool may be greater, thus as a result the needed parking norm will be lower. But given a small population, the population is to be expected to have a greater impact on the parking norm because the efficiency will be lower. 42

The costs of establishing the full amount of parking lots needed is depending on one single factor, the parking standard of Östra Sala backe. Because of this, the parameters affecting the cost of building the parking lots will be the same as for those of the parking standard. The number of demanded cars in the carpool will be directly depending on the parking norm, alike the costs of establishing the parking lots. But the mix of the carpool will also affect the capital investment when establishing the carpool. If Östra Sala backe will consider a environmental aspect and purchasing many electric cars the capital needed will increase. The electric car is more expensive than both the ethanol and diesel car in the study. Two parameters, the number of travellers as well as the mix of the carpool affects the emissions of carbon dioxide. If there are a larger amount of cars needed in the carpool it is obvious that the amount of emission will increase, but how much it will increase is depending on the mix of cars. If there is a larger share of electric travels the amount of emissions will be lower than if a fuel driven car was to be used for the same distance travelled. The length for each journey will also affect the emissions of carbon dioxide but in the simulation static variables, average length is used. This will therefore not be as affecting as it actually is, because of the small differences between the average lengths of each type.

6. Discussion The focus of the case study has been the parking norm in Östra Sala backe but environmental and economical aspects have also been taken into consideration. A lower parking norm of Östra Sala backe will have a positive environmental impact because of decreasing use of material when constructing the parking lots. One major uncertainty in the final result is the extent of how many inhabitants in Östra Sala backe that will be members in the carpool. A reasonable assumption is that maybe 20-30 percent of the residences will join the carpool, thus the same percentage of inhabitants. This would give a slightly decrease in the parking norm, from the original valuable of 1,1. For all three scenarios and both alternatives a membership of 20 percent in the carpool will give a parking norm of 0,94 - 0,97. With a membership of 30 percent, for all three scenarios and both alternatives, the parking norm will be 0,86 0,91. If the carpool is subsidized or free, it is possible to attract more inhabitants to join the carpool. A membership of 50 percentage will give a parking norm of around 0,77 for scenario 43, since the area Östra Sala backe strives to be Uppsala’s most environmental friendly neighborhood and encourage alternative transportation it is reasonable to assume that scenario 26 or 34 would reflect the car use of Östra Sala backe. This would give a parking norm of 0,69-0,73 with a membership of 50 percent for both alternatives, 4500 and 5600 inhabitants. 43

The optimal mix of the carpool will not occur in reality since there are not enough electric cars in the carpool to answer to all shorter journeys. Fuel driven cars maximum need has been used as a basis in the mix and then electric cars has been added until the maximum hourly need has been covered. Thus the maximum of electric cars needed is a higher amount than there are electric cars in the carpool. The share of emission related to fuel driven cars will obviously increase since these cars will be driven when an electric car could have been used. Of course the fuel costs will be affected in a similar way, an increase of fuel costs and indeed the fuel driven cars share of total fuel costs. The conclusion is that the shares of emissions as well as costs are lower in the simulation than it would be in reality. The fuel driven cars share of costs will be higher than 75 percentages as well as the share of carbon dioxide emission will be higher than 84 percentages. As highlighted earlier in the case study a number of unique simulations showed that the mix of the carpool is expected to be around 55 percentages fuel driven cars and the rest electric cars. If the electric cars would correspond to a minor proportion both emissions as well as fuel would increase significantly. Notable is that the parking norm calculated in this case study is not forced to be set. If a slightly lower parking norm is set the result might be as in Hammarby Sjöstad, which the behavior of the population will change. In Hammarby Sjöstad the parking norm was lowered almost 6,7 percentages, from 0,75 to 0,7 in a short period of time as well as the car ownership of the inhabitants was reduced. The parking norm could be set slightly lower than calculated to encourage a behavioral change, that is by capping the maximum cars housing within the area. Other actions like parking rates for visitors and other people living close to Östra Sala backe could be used to ensure that no unauthorized cars uses the parking lots of the inhabitants. A further action to be considered is increased tariffs if more than one parking lot is used per household. That might encourage co-ownership or to use the established carpool.

6.1 Efficiency and incitement In this case study only one single carpool, located in Östra Sala backe, has been considered. If the carpool in Östra Sala backe could be integrated with other carpools that have been strategically integrated in the society this would increase the efficiency of the carpool. Location that would be interesting to place these extra carpools could be by the train station, to favor commute to Stockholm, and other traffic dense areas in Uppsala. If the carpool could be integrated in a national perspective the efficiency could increase even more. By placing a carpool in Stockholm the cars traveling there could be used by other members during work hours. In the simulation in this case study the cars that has been rented for work cannot be used by other members, even doe the car is likely to only be used to travel to work and then being unused. This case study does not include what the annual cost will be for the members of the carpool. The annual cost will directly affect the rate of members that will be part of the carpool. Economical incitement is necessary to make the inhabitants choose to join the carpool instead of purchase an own car. The major investment for the carpool is the 44

purchase cost of the cars. The members of the carpool will divide the investment cost of cars if it is not financed in some other way. A significant annual cost to be a member in the carpool could discourage inhabitants to join the pool, thus reducing the environmental friendly impact of the carpool. If the purchases of all cars were to be financed to some extent from the savings of building fewer parking lots membership in the carpool would be encouraged. As the results of the simulation showed, the more members of the carpool, the more significant positive affect of the environment.

6.2 Environmental affect and costs of the carpool There is a small statistic chance that a large part of the members would need a car during the same hour. It is not economically viable to purchase cars to cover this unlikely event. To solve this problem, or similar events, an external solution will be needed. One solution that could answer to the unlikely extreme needs, that statistically could occur, an agreement with some external company. Someplace where members of the carpool could be able to rent a car at a beneficial price alternatively that the carpool would answer to the costs. But since the simulations is executed over an extensive amount of time with several individual actions, randomly decided, these unlikely situation will not be significant for the result of the simulation. Calculation on the environmental impact and the economical aspect has been made with average values. In theory this values should show a reliable result, but still, the distribution of the journeys could vary notably. The amount of emissions from the cars will also vary depending on factors such as weight, weather, driving, and usage of AC or instruments. This will make the calculations slightly misleading and should only be considered to be guidelines on the magnitude on the environmental savings the carpool could provide. The cars that have been used in this case study will affect the result of the financial aspects. No further economical comparison has been made to get the most economical car for each type of fuel. Therefore these three types of cars may not be the optimal economical solution. Nissan Leaf was chosen for the category of electric driven car because of Östra Sala backes pronounced environmental friendly vision. Since Nissan Leaf was announced to be the car of the year in 2011 it seemed to fit in Östra Sala backe profile. Volvo V70 and Audi A3 Diesel were chosen more because of their type of fuel and for the larger cargo capacity, which increases possible usage areas of the cars. No further comparison with other cars with the same type of fuel has been done. Furthermore the environmental profile of the cars as well as the level of emissions may not be best, thus the environmental comparison of this study may not be the optimal. An assumption has been made that the purchase price for the cars is 90 percent due to factors such as quantity discount, different tax reliefs’ etcetera. It is not realistic that Uppsala County pays the same amount for a car as a single individual would do. The figure of 90 percent is a rough estimation that should be considered. The consequence of this estimation in the developed model is that a car is bought for 90 percent of the 45

value and no retail value is ever brought back into the economical calculation. A more realistic situation is that cars in the carpool will be purchased and used for some time and later be sold at some resale value. This will give a slightly misleading economical result, thus in reality the capital saved when establishing fewer parking lots is greater. If the cost is enough it could make the capital savings outweigh the carpools establishing costs of all scenarios at most of the different shares of members. The result of the simulation is that at around a tenth of the members is the critical point, the point where the savings from building fewer lots and establishing costs of the carpool are equal. The critical point should probably be set slightly lower since capital from selling already used cars should bring back retail capital to the carpool.

7. Conclusion This case study shows that an integrated carpool will decrease the parking norm in Östra Sala backe. A carpool in collaboration with other methods of transportation will fulfill the transportation needs without any drawback in terms of car availability of the inhabitants in Östra Sala backe. The magnitude of the decrease of the parking norm depends on some number of factors. The amount of members in the carpool will affect the parking norm substantially, but the general share of transport by car is more important factor if to decrease the parking norm. If the car could be replaced alternative ways of transportation the general share of transport could be lowered considerably, thus decreasing the parking standard as well as the environmental affect significantly. There is a close to linear relation between the parking norm and these two major factors described. The amount of capital that could be saved if building fewer parking lots in Östra Sala backe could easily outweigh the costs of purchasing every single car of the carpool if the amount of members is more than tenth of the inhabitants. The capital saved after purchasing all cars varies substantially, but the cost would at most be 78 percentages of the capital saved when building fewer lots. Furthermore, if the carpool was to be established with a suitable mix of just below a third electric cars, and the remaining segment equal shares of diesel and ethanol cars, the emissions of driving cars could be lowered by 24 percentages.

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8. Sources 8.1 Literature Statistiska Centralbyrån (2010), Statistisk årsbok för Sverige 2011, SCB-Tryck, Örebro

8.2 Internet-based sources Bensinpriser.nu (2012), Jet på Kungsgatan 72, URL: http://www.bensinpriser.nu/stations/car/uppsala-lan/uppsala/kungsgatan-72 (2012-0507) Dagens Nyheter (2011), Nissan Leaf årets bil 2011, URL:http://www.dn.se/motor/nyheter/nissan-leaf-arets-bil-2011(2012-05-07) Green Car Congress (2010), US EPA rates Nissan LEAF fuel economy as 99 mpgequivalent (combined); 73-mile range, URL: http://www.greencarcongress.com/2010/11/leaf-20101122.html#more(2012-05-10) Gröna Bilister (2011a), Biltest: Nissan Leaf, URL: http://www.gronabilister.se/public/file.php?REF=84117275be999ff55a987b9381e01f96 &art=655&FILE_ID=20110816105319_1_24.pdf(2012-05-10) Gröna Bilister (2011b), Biltest: Volvo V70, URL:http://www.gronabilister.se/public/file.php?REF=84117275be999ff55a987b9381e 01f96&art=655&FILE_ID=20110819130103_1_24.pdf(2012-05-07) Gröna Bilister (2007), Biltest: Audi A3 1.9 TDI EcoPower, URL:http://www.gronabilister.se/public/file.php?REF=84117275be999ff55a987b9381e 01f96&art=655&FILE_ID=20071231161238_2_3.pdf(2012-05-07) Hammarby Sjöstad (2012), Hammarbysjostad.se, URL: www.hammarbysjostad.se/(2012-03-31) Konsumentverket (2011), Drivmedel, URL:http://www.konsumentverket.se/bilar/Nybilsguiden/Drivmedelochutslapp/Drivme del/(2012-05-07) Landsorganisationen i Sverige (2009), Arbetstider år 2009, Mats Larsson, URL: http://www.lo.se/home/lo/home.nsf/unidview/FEB0FF2529E787E4C12576BC0072B4E 0/$file/Arbetstider_2009.pdf/(2012-05-11) Malmö Stad (2010a), Parkeringspolicy och Parkeringsnorm, URL: http://www.malmo.se/download/18.4027ea8b12af75326fc80003800/Parkeringspolicy+ och+parkeringsnorm+slutligt+f%C3%B6rslag+antagen+av+KF.pdf(2012-04-03) 47

Malmö Stad (2012a), Pågående stadsutveckling i Västra Hamnen, URL: http://www.malmo.se/download/18.6e1be7ef13514d6cfcc800018560/P%C3%A5g%C3 %A5ende+stadsutv+V+H+2012+sv+l.pdf(2012-04-03) Malmö Stad (2010b), Trafikutredning för Västra Hamnen, URL: http://www.malmo.se/download/18.56d99e38133491d8225800058297/Rapport%2B22 %2Bjanuari.pdf(2012-04-02) Malmö Stad (2012b), Västra Hamnen in figures 2012, URL: http://www.malmo.se/download/18.6e1be7ef13514d6cfcc80008049/v%C3%A4stra+ha mnen+i+siffror+2012.pdf(2012-04-03) Malmö Stad (2001), Översiktsplan för Malmö 2000, URL: http://www.malmo.se/download/18.5d8108001222c393c00800073514/sammanfattning op2000.pdf (2012-05-03) Park Charge (2012), Frågor och svar om laddinfrastruktur och laddfordon, URL: http://www.park-charge.se/index.php?pageId=14 (2012-03-31) Rese- och turistdatabasen (2009), Sverige Special Mars 2009, URL: http://www.tdb.se/dokument/TDB-FaktaSv_Mar_2009.pdf(2012-05-08) SIKA (2004), Statistik om kollektivtrafik – en genomgång av tillgängliga källor, URL: http://trafa.se/PageDocuments/sr_2004_2.pdf (2012-05-16) SIKA (2007), RES 2005-2006 Den nationella resvaneundersökningen, URL: http://trafa.se/PageDocuments/ss_2007_19_1.pdf (2012-05-16) Statens väg- och transportforskningsinstitut (2009), Empirical analyses of car ownership and car use in Sweden, URL: http://www.vti.se/sv/publikationer/pdf/empiriska-analyser-av-bilanvandning-ochbilagande-i-sverige.pdf(2012-03-31) Stockholm Bygger (2012), Hammarby Sjöstad – dit världen åker för att se framtidens stad, URL:http://www.stockholmbygger.se/stadsutvecklingsomraden/hammarbysjostad__28(2012-05-09) Stockholms Läns Landsting (2012), Befolkningsutveckling, URL: http://www.tmr.sll.se/Statistik/Demografi-och-prognoser/Befolkningsutveckling/(201205-03) Stockholm Stad (2009), Allmänt, URL: http://www.stockholm.se/Fristaendewebbplatser/Fackforvaltningssajter/Exploateringskontoret/Hammarby-Sjostad/Allmant/ (2012-05-03)

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Stockholm Stad (2010), Siffror och fakta, URL: http://www.stockholm.se/Fristaendewebbplatser/Fackforvaltningssajter/Exploateringskontoret/HammarbySjostad/Allmant/Siffror-och-fakta/ (2012-05-03) Stockholm Stad (2005), Ökade bygg- och bostadskostnader med höjd p-norm, URL: http://insyn.stockholm.se/mark/document/2005-12-15/Dagordning/34/34.pdf(2012-0511) Sunfleet (2012), Elbil i bilpool, URL:http://www.sunfleet.com/elbil-i-bilpool/ (2012-0509) Transek (2006), Fördelning av olika fordonsslag, URL: http://www.stockholmsforsoket.se/upload/Rapporter/Trafik/Under/F%C3%B6rdelning %20av%20olika%20fordonsslag%20060613.pdf (2012-05-10) Uppsala Kommun (2011a), Familjeuppgifter, URL: http://www.uppsala.se/Upload/Dokumentarkiv/Externt/Dokument/Om_kommunen/Omr adesfakta/Familjeuppgifter.pdf (2012-05-03) Uppsala Kommun, (2011b), Folkmängd, URL: http://www.uppsala.se/Upload/Dokumentarkiv/Externt/Dokument/Om_kommunen/Omr adesfakta/Folkmangd.pdf (2012-05-17) Uppsala Kommun (2011c), Stadsutveckling Östra Sala backe, URL: http://www.uppsala.se/Upload/Dokumentarkiv/Externt/Dokument/Bostad_o_byggande/ Stadsplanering/Ostra%20Sala%20backe/Prospect_Ostra_Sala_backe_webb.pdf (201205-03) Uppsala Kommun (2010a), Östra Sala backe Planprogram, URL: http://www.uppsala.se/Upload/Dokumentarkiv/Externt/Dokument/Bostad_o_byggande/ Stadsplanering/Ostra%20Sala%20backe/Planprogram%20Ostra%20Sala%20backe.pdf/ (2012-05-03) Uppsala Kommun (2010b), Östra Sala backe Planprogram Bilaga – förutsättningar och konsekvenser, URL: http://www.uppsala.se/Upload/Dokumentarkiv/Externt/Dokument/Bostad_o_byggande/ Stadsplanering/Ostra%20Sala%20backe/planprogram_forutsattningar_konsekvenser.pdf /(2012-05-03) Uppsala Kommun (2011d), Östra Sala backe Sammafattnig av planprogram, URL: http://www.uppsala.se/Upload/Dokumentarkiv/Externt/Dokument/Bostad_o_byggande/ Stadsplanering/Ostra%20Sala%20backe/poprversion_Ostra_Sala_backe_webb.pdf/ (2012-05-03) Vattenfall (2012), Vanliga frågor om laddning och elbilar, URL: http://laddaelbilen.vattenfall.se/sv/vanliga-fragor-om-elbilar.htm (2012-03-31) 49

8.3 Figures and tables Figure 1, SIKA (2007), tabellbilaga, p. 3 Figure 2, Uppsala Kommun (2011a) Figure 3, Uppsala Kommun (2011b) Figure 4, Stockholm Stad (2010) Figure 5, Stockholm Stad (2010) Figure 6, Malmö Stad (2012b), p. 3 Figure 7, Malmö Stad (2010b), p. 7 Figure 8, Malmö Stad (2010b), p. 7 Figure 9, Malmö Stad (2010b), p. 11 Figure 10, Malmö Stad (2010b), p. 12 Figure 12, SIKA (2007), tabellbilaga, p. 5 Figure 13, SIKA (2007), tabellbilaga, p. 5 Figure 14, SIKA (2007), tabellbilaga, p. 5 Figure 15, SIKA (2007), tabellbilaga, p. 5 Figure 16, Statens väg- och transportforskningsinstitut (2009), p. 68 Table 1, SIKA 2007, p. 68

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9. Appendix 9.1 Appendix A - Code used for plots in MATLAB % 100 a26 = a34 = a43 =

i bilpool, 5600 inv (0.2716+0.2772+0.276+0.2752+0.2728)/5; (0.3452+0.3504+0.3448+0.3472+0.346)/5; (0.4284+0.4232+0.4244+0.4312+0.43)/5;

% 100, 4500 inv b26 = (0.2785+0.277+0.279+0.2755+0.278)/5; b34 = (0.353+0.3485+0.3525+0.348+0.3505)/5; b43 = (0.4295+0.434+0.434+0.4305+0.43)/5; % 75, 5600 inv c26 = (75*((0.274133+0.2784+0.275733+0.275733+0.275733)/5) + (25*1.1))/100; c34 = (75*((0.352+0.3536+0.349333+0.349867+0.356266)/5) + (25*1.1))/100; c43 = (75*((0.434133+0.431+0.4304+0.43+0.432533)/5) + (25*1.1))/100; % 75, 4500 inv d26 = (75*((0.281333+0.281333+0.281333+0.282+0.28067)/5) + (25*1.1))/100; d34 = (75*((0.35533+0.3567+0.35133+0.35133+0.358)/5) + (25*1.1))/100; d43 = (75*((0.434+0.4333+0.438+0.44067+0.44)/5) + (25*1.1))/100; % 50, e26 = e34 = e43 =

5600 inv (50*((0.2816+0.2808+0.2832+0.2792+0.2856)/5) + (50*1.1))/100; (50*((0.36+0.3536+0.3592+0.3608+0.3528)/5) + (50*1.1))/100; (50*((0.432+0.44+0.436+0.4336+0.4368)/5) + (50*1.1))/100;

% 50, f26 = f34 = f43 =

4500 inv (50*((0.291+0.286+0.288+0.293+0.283)/5) + (50*1.1))/100; (50*((0.368+0.364+0.363+0.362+0.364)/5) + (50*1.1))/100; (50*((0.443+0.4448+0.445+0.449+0.446)/5) + (50*1.1))/100;

% 25, g26 = g34 = g43 =

5600 inv (25*((0.296+0.2976+0.2944+0.2976+0.296)/5) + (75*1.1))/100; (25*((0.3696+0.368+0.3728+0.3776+0.3649)/5) + (75*1.1))/100; (25*((0.4576+0.4544+0.456+0.4448+0.449)/5) + (75*1.1))/100;

% 25, h26 = h34 = h43 =

4500 inv (25*((0.302+0.302+0.296+0.304+0.306)/5) + (75*1.1))/100; (25*((0.378+0.38+0.386+0.378+0.388)/5) + (75*1.1))/100; (25*((0.472+0.468+0.462+0.46+0.456)/5) + (75*1.1))/100;

% 0, 5600/4500 inv k= 1.1; % 5600 asum26 asum34 asum43

inv = [a26,c26,e26,g26,k]; = [a34,c34,e34,g34,k]; = [a43,c43,e43,g43,k];

% 4500 inv bsum26 = [b26,d26,f26,h26,k];

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bsum34 = [b34,d34,f34,h34,k]; bsum43 = [b43,d43,f43,h43,k]; x = [100, 75, 50, 25, 0]; % procent med i bilpool plot(x,asum26, x,asum34, x,asum43); title('5600 inhabitants'); legend('Scenario 26','Scenario 34','Scenario 43'); xlabel('Percentage of inhabitants members in the carpool'); ylabel('PPH'); gridon; axis([0 100 0 1.1]); figure; plot(x,bsum26, x,bsum34, x,bsum43); title('4500 inhabitants'); legend('Scenario 26','Scenario 34','Scenario 43'); xlabel('Percentage of inhabitants members in the carpool'); ylabel('PPH'); gridon; axis([0 100 0 1.1]); %-------------------------------------------------------------------% % NEW CALCULATION % %-------------------------------------------------------------------% res1=2500; res2=2000; cost=250000; costG=10000; v26_5600=[]; v34_5600=[]; v43_5600=[]; v26_4500=[]; v34_4500=[]; v43_4500=[]; percent26=[]; percent43=[]; v26_5600=(((res1*asum26)-270)*cost)+(200*costG); v34_5600=(((res1*asum34)-270)*cost)+(200*costG); v43_5600=(((res1*asum43)-270)*cost)+(200*costG); v26_4500=(res2*bsum26)*cost; v34_4500=(res2*bsum34)*cost; v43_4500=(res2*bsum43)*cost; for i=1:5 percent26_5600(i)=((v26_5600(i)-v34_5600(i))/v34_5600(i))*100; percent43_5600(i)=((v43_5600(i)-v34_5600(i))/v34_5600(i))*100; percent26_4500(i)=((v26_4500(i)-v34_4500(i))/v34_4500(i))*100; percent43_4500(i)=((v43_4500(i)-v34_4500(i))/v34_4500(i))*100; end figure; plot(x,v26_4500, x,v34_4500, x,v43_4500);

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title('4500 inhabitants'); legend('Scenario 26','Scenario 34','Scenario 43'); xlabel('Percentage of inhabitants members in the carpool'); ylabel('Costs'); gridon; figure; plot(x,v26_5600, x,v34_5600, x,v43_5600); title('5600 inhabitants'); legend('Scenario 26','Scenario 34','Scenario 43'); xlabel('Percentage of inhabitants members in the carpool'); ylabel('Costs'); gridon; figure; plot(x,percent26_5600, x,percent43_5600, x,percent26_4500, x,percent43_4500); title('Variation of costs compared with Scenario 34'); legend('Scenario 26, 5600','Scenario 43, 5600','Scenario 26, 4500', 'Scenario 43, 4500'); xlabel('Percentage of inhabitants members in the carpool'); ylabel('Percentage of variation'); gridon; %-------------------------------------------------------------------% % NEW CALCULATION % %-------------------------------------------------------------------% Electric=320000; Diesel=226000; Ethanol=255900; % Diverse p-normer av_5600_26_100 = (0.2716+0.2772+0.276+0.2752+0.2728)/5; av_5600_26_75 = (0.274133+0.2784+0.275733+0.275733+0.275733)/5; av_5600_26_50 = (0.2816+0.2808+0.2832+0.2792+0.2856)/5; av_5600_26_25 = (0.296+0.2976+0.2944+0.2976+0.296)/5; av_5600_34_100 = (0.3452+0.3504+0.3448+0.3472+0.346)/5; av_5600_34_75 = (0.352+0.3536+0.349333+0.349867+0.356266)/5; av_5600_34_50 = (0.36+0.3536+0.3592+0.3608+0.3528)/5; av_5600_34_25 = (0.3696+0.368+0.3728+0.3776+0.3649)/5; av_5600_43_100 = (0.4284+0.4232+0.4244+0.4312+0.43)/5; av_5600_43_75 = (0.434133+0.431+0.4304+0.43+0.432533)/5; av_5600_43_50 = (0.432+0.44+0.436+0.4336+0.4368)/5; av_5600_43_25 = (0.4576+0.4544+0.456+0.4448+0.449)/5; av_4500_26_100 = (0.2785+0.277+0.279+0.2755+0.278)/5; av_4500_26_75 = (0.281333+0.281333+0.281333+0.282+0.28067)/5; av_4500_26_50 = (0.291+0.286+0.288+0.293+0.283)/5; av_4500_26_25 = (0.302+0.302+0.296+0.304+0.306)/5; av_4500_34_100 = (0.353+0.3485+0.3525+0.348+0.3505)/5; av_4500_34_75 = (0.35533+0.3567+0.35133+0.35133+0.358)/5; av_4500_34_50 = (0.368+0.364+0.363+0.362+0.364)/5; av_4500_34_25 = (0.378+0.38+0.386+0.378+0.388)/5; av_4500_43_100 = (0.4295+0.434+0.434+0.4305+0.43)/5; av_4500_43_75 = (0.434+0.4333+0.438+0.44067+0.44)/5;

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av_4500_43_50 = (0.443+0.4448+0.445+0.449+0.446)/5; av_4500_43_25 = (0.472+0.468+0.462+0.46+0.456)/5; %Antal bilar average_5600_26 = [av_5600_26_100 av_5600_26_75 av_5600_26_50 av_5600_26_25 0]*res1*((0.55*0.5*Ethanol)+(0.55*0.5*Diesel)+(0.45*Electric)); average_5600_34 = [av_5600_34_100 av_5600_34_75 av_5600_34_50 av_5600_34_25 0]*res1*((0.55*0.5*Ethanol)+(0.55*0.5*Diesel)+(0.45*Electric)); average_5600_43 = [av_5600_43_100 av_5600_43_75 av_5600_43_50 av_5600_43_25 0]*res1*((0.55*0.5*Ethanol)+(0.55*0.5*Diesel)+(0.45*Electric)); average_4500_26 = [av_4500_26_100 av_4500_26_75 av_4500_26_50 av_4500_26_25 0]*res2*((0.55*0.5*Ethanol)+(0.55*0.5*Diesel)+(0.45*Electric)); average_4500_34 = [av_4500_34_100 av_4500_34_75 av_4500_34_50 av_4500_34_25 0]*res2*((0.55*0.5*Ethanol)+(0.55*0.5*Diesel)+(0.45*Electric)); average_4500_43 = [av_4500_43_100 av_4500_43_75 av_4500_43_50 av_4500_43_25 0]*res2*((0.55*0.5*Ethanol)+(0.55*0.5*Diesel)+(0.45*Electric)); for n=1:5 if n==2 average_5600_26(n)=average_5600_26(n)*0.75; average_5600_34(n)=average_5600_34(n)*0.75; average_5600_43(n)=average_5600_43(n)*0.75; average_4500_26(n)=average_4500_26(n)*0.75; average_4500_34(n)=average_4500_34(n)*0.75; average_4500_43(n)=average_4500_43(n)*0.75; end if n==3 average_5600_26(n)=average_5600_26(n)*0.5; average_5600_34(n)=average_5600_34(n)*0.5; average_5600_43(n)=average_5600_43(n)*0.5; average_4500_26(n)=average_4500_26(n)*0.5; average_4500_34(n)=average_4500_34(n)*0.5; average_4500_43(n)=average_4500_43(n)*0.5; end if n==4 average_5600_26(n)=average_5600_26(n)*0.25; average_5600_34(n)=average_5600_34(n)*0.25; average_5600_43(n)=average_5600_43(n)*0.25; average_4500_26(n)=average_4500_26(n)*0.25; average_4500_34(n)=average_4500_34(n)*0.25; average_4500_43(n)=average_4500_43(n)*0.25; end end figure; plot(x,average_5600_26, x,average_5600_34, x,average_5600_43); title('5600 inhabitants'); legend('Scenario 26','Scenario 34','Scenario 43');

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xlabel('Percentage of inhabitants members in the carpool'); ylabel('Costs of purchasing cars to the carpool'); gridon; figure; plot(x,average_4500_26, x,average_4500_34, x,average_4500_43); title('4500 inhabitants'); legend('Scenario 26','Scenario 34','Scenario 43'); xlabel('Percentage of inhabitants members in the carpool'); ylabel('Costs of purchasing cars to the carpool'); gridon; %-------------------------------------------------------------------% % NEW CALCULATION % %-------------------------------------------------------------------% % Kˆrningar gjorde med 100 % i bilpool fˆr att f fram den totala l‰ngden, % samt utsl‰pp frÂn bilpool som kan j‰mfˆras med utan bilpool. % 5600 A26 = 3.0085*10^7; % resultat frÂn simulation A34 = 3.8654*10^7; A43 = 4.6429*10^7; UtA26 = 1839524.63; UtA34 = 2330616.516; UtA43 = 2847973.725; % slut Diesel = 0.124; % variabler frÂn model Etanol = 0.080; El = 0.020; % slut Tot5600_26 = (A26/2)*Diesel + utsl‰pp, utan bilpool(antagit distansen) Tot5600_34 = (A34/2)*Diesel + Tot5600_43 = (A43/2)*Diesel + el30_A26 = 0.3*A26*El Elbilar kˆr runt 30 % el30_A34 = 0.3*A34*El el30_A43 = 0.3*A43*El slut

+ i + +

(A26/2)*Etanol; % Utr‰kning av totala att fossila bilar kˆr 100 % av (A34/2)*Etanol; (A43/2)*Etanol; % slut

0.7*(A26/2)*Diesel + 0.7*(A26/2)*Etanol; % vald mix 0.7*(A34/2)*Diesel + 0.7*(A34/2)*Etanol; 0.7*(A43/2)*Diesel + 0.7*(A43/2)*Etanol; %

% 4950 B26 = 2.4208*10^7; % resultat frÂn simulation B34 = 3.0656*10^7; B43 = 3.74194*10^7; UtB26 = 1479251.406; UtB34 = 1878856.404; UtB43 = 2295174.0; % slut Tot4500_26 = (B26/2)*Diesel + (B26/2)*Etanol; % Utr‰kning av totala utsl‰pp, utan bilpool(antagit att fossila bilar kˆr 100 % av distansen) Tot4500_34 = (B34/2)*Diesel + (B34/2)*Etanol;

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Tot4500_43 = (B43/2)*Diesel + (B43/2)*Etanol; % slut el30_B26 = 0.3*B26*El Elbilar kˆr runt 30 % el30_B34 = 0.3*B34*El el30_B43 = 0.3*B43*El slut

+ i + +

0.7*(B26/2)*Diesel + 0.7*(B26/2)*Etanol; % vald mix 0.7*(B34/2)*Diesel + 0.7*(B34/2)*Etanol; 0.7*(B43/2)*Diesel + 0.7*(B43/2)*Etanol; %

% Utr‰kningar: Res26_5600 = bilpool(alla Res34_5600 = Res43_5600 =

UtA26/Tot5600_26; utsl‰pp fossila), UtA34/Tot5600_34; UtA43/Tot5600_43;

%procent bilpool/alla utsl‰pp utan S26, 5600 %S34 %S43

Res26_4500 = UtB26/Tot4500_26; %S26, 4500 Res34_4500 = UtB34/Tot4500_34; %S34 Res43_4500 = UtB43/Tot4500_43; %S43 Mix26_5600 = el30_A26/Tot5600_26; %procent vald mix(uppfylld med fossilbil fˆrst)/alla utsl‰pp utan bilpool(alla utsl‰pp fossila), S26, 5600 Mix34_5600 = el30_A34/Tot5600_34; %S34 Mix43_5600 = el30_A43/Tot5600_43; %S43 Mix26_4500 = el30_B26/Tot4500_26; %S26, 4500 Mix34_4500 = el30_B34/Tot4500_34; %S34, 4500 Mix43_4500 = el30_B43/Tot4500_43; %S43, 4500 Res5600 = [Res26_5600 Res34_5600 Res43_5600] procentsatser beroende p scenario, 5600 med Res4500 = [Res26_4500 Res34_4500 Res43_4500] procentsatser beroende p scenario, 4500 med Mix5600 = [Mix26_5600 Mix34_5600 Mix43_5600] procentsatser beroende p scenario, 5600 med Mix4500 = [Mix26_4500 Mix34_4500 Mix43_4500] procentsatser beroende p scenario, 4500 med

% Ger oss alla optimal mix. % Ger oss alla optimal mix. % Ger oss alla vald mix. % Ger oss alla vald mix.

9.2 Appendix B - Java code of the simulation 9.2.1 The Main class package simulering; import java.io.IOException; import javax.swing.*; public class Main { public static void main(String [] args) throws IOException { String filename = "info.txt"; Systemet simulation = new Systemet(filename); JFrame f = new JFrame(); f.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); f.add(new GraphingData()); f.setSize(400,400); f.setLocation(200,200); f.setVisible(true);

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} }

9.2.2 The class Systemet package simulering; import java.util.*; import java.io.*; public class Systemet { int int int int

// Declaring variables and parameters maxTravellers=0; maxTravellersHour; maxNeed=0; maxUsersPerHours=0;

int int int int int int int int int int int

longTravel=0; fossilSS=0; fossilST=0; fossilO=0; fossilW=0; electricSS=0; electricST=0; electricO=0; electricW=0; maxElectricCar=0; maxFossilCar=0;

int ID; int number; int totalRents; //Arrays to be containing distributions Double hoursProbWork[] = new Double [24]; Double hoursProbServiceShop[] = new Double [24]; Double hoursProbSpareTime[] = new Double [24]; Double hoursProbOther[] = new Double [24]; Double hoursProbAll[] = new Double [24]; public static int [] usersPerHour; public static Double [] distribution = new Double [24]; int intensityHours[] = new int [24]; int queueHours[] = new int [24]; Double hoursProb[] = new Double [24]; public static int averageUsersHour []= new int [24]; ArrayList residentsArray = new ArrayList(); ArrayList queue = new ArrayList(); ArrayList typeArray = new ArrayList(); int int int int int int int int

tempHour; time=0; day=1; dayCount=1; yearCount=0; years=2; int months=12; int weeksPerMonth=4; weeks=4*12*years; days=365*(years);

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double percentageCarPool=1.0; int totalResidents=4500; int residents=(int)(Math.ceil(totalResidents*percentageCarPool)); // 4500 Alt 2 //According to Östra Sala Backe // Loading statistics and empiric data public Systemet(String filename) throws IOException{ Properties readIn = new Properties(); FileReader fileReader = new FileReader(filename); readIn.load(fileReader); //Initially creates an object for every resident for (int num=0; num