Car fleet modelling:
Data processing and discrete choice model estimation
Y
US
HENMASTER’STHESIS
SUPERVISOR: EMMAFREJINGER
KTH ROYALINSTITUTE OF TECHNOLOGY
STOCKHOLM, SWEDEN JUNE, 2011 TSC-MT 11-017
谨以此文献给我的父母和妻子
Abstract
This thesis deals with the modelling of the choice of new car based on the registra- tion data of the whole Sweden car fleet for 2005 to 2010. It is divided into two parts.
In the first part, to obtain the observations of new car choices for the discrete choice modelling, a subset based on the first registration date of each car is extracted. Then, a descriptive analysis based on the new car choice data is presented to find the variances of the attributes for the modelling. Specifically, two major issues are paid attention to.
One is the change of market share of each car make in these years and the other is the incremental demand of diesel and hybrid fuel cars.
The second part of the thesis deals with the discrete choice modelling. In order to designate the alternatives, another dataset showing the new car supply in Sweden is in- troduced. In the supply data, the alternatives are shown in the car version level, whereas the registration data only contain the names of car models. Additionally, the supply data also have some attributes that are unavailable in the registration, e.g. price. Thus, this thesis presents various matching methods to match the supply and the registration to define the alternatives for the modelling and also to obtain a higher precision of each attribute than that in matching with model names only. Finally, we choose to match the data by the same model name with the same maximum power, which is defined as the “model-engine” level. Therefore, based on these model-engine level alternatives, 18 MNL models are estimated from 2005 to 2010, with 3 different ownerships, namely private owned, company owned and company owned but leasing to its employee which is named as “leasing users”. The results show the slump of the brand constants of Saab among these years in private owners and leasing users due to the close-down crisis when the coefficient of Volvo is fixed to zero. By contrast, the brand value of Kia for private owners and the value of VW for leasing users go up. Meanwhile, this thesis analyses a shift of car buyers’ attitude to the alternative fuel car from negative in 2006 to positive in 2007 when a “clean car” compensation policy is implemented from Jan. 2007 to Jul.
2009. And in 2010, the coefficient of the alternative fuel remains positive. These results indicate that this policy was quite successful.
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Acknowledgements
First, I want to thank my dear parents, Zhencheng Shen and Honggang Du, and my beloved wife, Jing Wu. Without their fully helps, I can hardly finish my master study in Sweden. Second, I am deeply grateful to my supervisor, Dr. Emma Frejinger. Without her suggestion, I cannot even imagine that I would have an opportunity to take part in this project. During these months, the discussions and meetings of this thesis with Emma indeed help me a lot in both professional knowledges and scientific writings. And I also appreciate Visiting Professor Staffan Algers and Dr. Muriel Beser Hugosson for their kindly helps to this thesis. Then, I want to appreciate all the colleges in Division of Transport and Location Analysis, especially Shiva Habibi, Qian Wang, Dr. Tom Petersen and Tongzhou Bai, for their enthusiastic help to my work in different ways. Meanwhile, I would like to thank all the teachers and classmates in Transport Systems programme for their helps in these two years, e.g. Professor Lars-G¨oran Mattsson, Professor Haris Koutsopoulos, Dr. Joel Franklin, and also my classmates Yu Liu, Shuang Zhang, etc, just to name but a few. Finally, I want to thank those who read this thesis. Your readings and comments make my work valuable. Tack s˚a mycket!
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Contents
Abstract 3
Acknowledgements 5
Contents 9
List of figures 12
List of tables 14
1 Introduction and literature review 15
1.1 Background . . . 15
1.2 Literature review . . . 15
1.2.1 Discrete choice modelling . . . 16
1.2.2 Modelling methodology . . . 17
1.3 Thesis structure . . . 18
1.4 Scope and limitations . . . 19
2 Data storage and processing 25 2.1 Introduction . . . 25
2.2 Data storage and migration . . . 25
2.3 Software and processing . . . 26
I Descriptive analysis 29
3 Descriptive analysis of vehicle ownership 31 3.1 Introduction . . . 313.2 Car ownership analysis . . . 32
3.2.1 Car ownership share by make . . . 32
3.2.2 Car ownership by vintage . . . 34 7
8 CONTENTS
4 Descriptive analysis of new car registries 39
4.1 Introduction . . . 39
4.1.1 Extraction of new car data . . . 39
4.1.2 Model name generation for 2005 to 2007 . . . 41
4.2 Market analysis of choices . . . 43
4.2.1 Issues of defining price . . . 43
4.2.2 Market analysis in Sweden new car market . . . 44
4.2.3 Comparative analysis of the new car market in other countries . 47 4.3 Analysis of fuel type choices . . . 50
5 Descriptive analysis of car attributes 53 5.1 Introduction . . . 53
5.2 Share of fuel types in supply . . . 55
5.3 Distribution of the attribute values . . . 55
5.4 Technology attributes . . . 58
II Disaggregated analysis 61
6 Data matching 63 6.1 Description . . . 636.2 Methodology of matching . . . 65
6.2.1 Standardisation of model name . . . 65
6.3 Results and drawbacks of model level . . . 66
6.4 Matching in a more detailed level . . . 68
6.4.1 A level between model and version . . . 68
6.4.2 Matching with power or weight . . . 69
6.4.3 Results of matching by power . . . 76
6.4.4 Conclusion about matching with power and weight . . . 77
7 New car choice modelling 79 7.1 Introduction and methodology . . . 79
7.1.1 Analysis of new car choice sets . . . 79
7.1.2 Estimation tool - BIOGEME . . . 80
7.2 Model estimation . . . 81
7.3 Estimation results . . . 83
7.3.1 Sampling from private owned car data . . . 83
CONTENTS 9 7.3.2 Parameter analysis for private owner choices . . . 84 7.3.3 Parameter analysis for company owner choices . . . 88 7.3.4 Parameter analysis of the choices of company cars for leasing . 91 7.4 Analysis across various years . . . 95 7.4.1 The impact of “clean car” compensation . . . 95 7.4.2 The brand value decline of Saab . . . 97
8 Conclusion and discussion 99
8.1 Summary of results . . . 99 8.2 Comparison results in literatures . . . 100 8.3 Future works . . . 101
List of appendices 107
A List of car makes and models 107
B List of numerical attributes 111
C Estimated parameters comparison 115
10 CONTENTS
List of Figures
3.2.1 Total market share of different brands . . . 35
3.2.2 Ownership by vintage of 1984 to 2004 . . . 36
4.1.1 Different numbers of new car registration . . . 40
4.1.2 Comparison of monthly sales . . . 41
4.1.3 Procedures of finding model names before 2008 . . . 42
4.2.1 Shares of car make by origin area in 2007 . . . 45
4.2.2 Shares of car make by origin area in 2010 . . . 45
4.3.1 Share of various fuel types of new registered cars . . . 51
4.3.2 Share of various fuel types in Bil Sweden . . . 51
5.2.1 Share of various fuel types in supply . . . 55
5.3.1 Histogram and density of price . . . 57
5.3.2 Histogram and density of log price . . . 57
5.3.3 Histogram and density of power . . . 57
5.3.4 Histogram and density of displacement . . . 57
5.3.5 Histogram and density of weight . . . 58
5.3.6 Histogram and density of acceleration . . . 58
6.3.1 CV of price, model level 2007 . . . 67
6.3.2 CV of price, model level 2008 . . . 67
6.3.3 CV of price, model level 2009 . . . 68
6.3.4 CV of price, model level 2010 . . . 68
6.4.1 CV of price, model-engine level 2007 . . . 71
6.4.2 CV of price, model-engine level 2008 . . . 71
6.4.3 CV of price, model-engine level 2009 . . . 71
6.4.4 CV of price, model-engine level 2010 . . . 71
6.4.5 CV of price, matching by power and gear 2007 . . . 72
6.4.6 CV of price, matching by power and gear 2008 . . . 72 11
12 LIST OF FIGURES
6.4.7 CV of price, matching by power and gear 2009 . . . 72
6.4.8 CV of price, matching by power and gear 2010 . . . 72
6.4.9 CV of price, model-weight level 2007 . . . 74
6.4.10CV of price, model-weight level 2008 . . . 74
6.4.11CV of price, model-weight level 2009 . . . 74
6.4.12CV of price, model-weight level 2010 . . . 74
7.4.1 Change of MWTP of alternative fuel . . . 96
List of Tables
1.4.1 Summary of literatures . . . 21
3.1.1 Fuel types and codes . . . 32
3.2.1 Rank of car ownership shares by make . . . 33
4.1.1 Different numbers of new car registration . . . 39
4.1.2 Numbers and shares of new cars deregistration in 2008 . . . 43
4.2.1 Numbers and shares of new registries by vintage . . . 44
4.2.2 Market Shares of Top 15 Brands in New Car Market . . . 44
4.2.3 Top 20 models and sales in Sweden new car market . . . 46
4.2.4 Top 10 brands of new car registries in Germany . . . 48
4.2.5 Passenger cars share by origin in China in 1st half of 2010 . . . 48
4.2.6 Sales and market shares in the U.S. in 2009 and 2010 . . . 48
4.2.7 Top 10 models in other European countries in 2010 . . . 49
4.2.8 Top 10 models in U.S. and Asia in 2010 . . . 50
5.1.1 Number of versions in supply without data missed . . . 54
5.1.2 Shares of vehicle types in the supply . . . 54
5.3.1 Correlation of attributes . . . 58
5.4.2 Shares of dummies in supply data 2007 and 2008 . . . 59
5.4.3 Shares of dummies in supply data 2009 and 2010 . . . 60
6.1.1 Shares of new cars matched in different aggregation level . . . 64
6.1.2 Shares of Fiat can be matched . . . 64
6.2.1 Comparison of the number of models . . . 66
6.3.1 Greatest 5 models with most versions . . . 67
6.4.1 Data missing in displacement . . . 69
6.4.2 Matching and mismatching by power . . . 73
6.4.3 Matching and mismatching by weight . . . 74
6.4.4 Summary of CV of price from 2007 to 2010 . . . 75 13
14 LIST OF TABLES
6.4.5 Shares of data after matching (excluding imported cars) . . . 76
6.4.6 Paired t-test of 2009 and 2010 . . . 77
7.1.1 Size of choice sets from 2005 to 2010 . . . 80
7.3.1 Estimation results of private owned cars . . . 84
7.3.2 Estimation results of company owned cars . . . 88
7.3.3 Estimation results of company leasing cars . . . 92
A.1 Car makes and models in Swedish new car market . . . 107
B.1 Statistical analysis of the variables . . . 111
C.1 Comparison between sample and total observations for model 2009 . . 115
Chapter 1
Introduction and literature review
1.1 Background
The automobile industries and markets play crucial roles in modern society. Among the industrialised countries with greatest gross domestic product (GDP), most of them have mighty car industries and huge markets, such as U.S. (GM, Ford), Japan (Toyota, Honda), Germany (Volkswagen, Mercedes) and France (Renault, Peugeot). Even in the newly industrialised countries, like China (FAW, Chery) and India (Tata), their automo- bile industries are booming as well. Meanwhile, in Sweden, from 2009 to 2010, both Swedish car makes, Volvo and Saab, go through a reselling crisis by their former U.S.
parent companies respectively, Ford and GM, due to the effect of late-2000s financial crisis. Looking at the aspect of climate change, the emissions of vehicles on the road contribute a great amount of greenhouse gases, e.g. carbon dioxide, which lead to the global warming. It would be essential for the transport sectors to know the consumers’
behaviour in car purchase in order to control the carbon budgets. Therefore, it is of interest to study and model the consumers’ choice cars.
Given that an individual has already decided to purchase a car, she usually has two alternatives: to buy a new car or to buy a second-hand car. This project specially focuses on the choice of new cars. That is, which make/model/version of car one will probably choose in a particular year (or vintage) if she wants to purchase a new car.
1.2 Literature review
Before this project, to my knowledge, the disaggregate car choice model has been stud- ied by many researchers since late 1970s. As forerunners, Lave and Train (1979) present the earliest disaggregated model to study the vehicle choice decisions. Then, under the circumstance of 1979 oil crisis, face on different data, locations and years, various dis- aggregate models have been developed in the U.S. since 1980s. Exemplarily, Manski
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16 CHAPTER 1. INTRODUCTION AND LITERATURE REVIEW and Sherman (1980), Mannering and Winston (1985) conduct their car choice models in multinomial logit forms, while Berkovec (1985), Berkovec and Rust (1985) develop the models in nested logit structures. Later on, due to the consideration of environment, energy consumptions and the effects of market, various models are constructed to find the endogenous reasons, e.g. Mannering et al. (1991), Choo and Mokhtarian (2004), Train and Winston (2007) and Hess et al. (2009), just to name but a few.
In Nordic countries, laying on great emphasis on environmental protection, some recent disaggregate car choice models are developed as well. For instance, in Denmark, Arnberg et al. (2008) present the Danish individuals’ new car choice by estimating a multinomial logit model to investigate the impact of fuel cost. In Sweden, Hugosson and Algers (2011) estimate Sweden car fleet models to analyse the policy effect of the increase share of “clean” cars.
In terms of the master theses focusing on the same issue as here, Nilsson (2008), K¨unnapuu (2009) develop new car choice models respectively to test various policies or markets scenarios and the consequent environmental effects. These these provide comprehensive instructions to deal with the Sweden new car choice modelling with the similar data in this project.
Some of these researches are reviewed explicitly in tabular form by Choo and Mokhtarian (2004). Next, we attempt to continue their works to extend such a table, shown in Table 1.4.1. In this table, we summarise several recent researches adapted in the U.S., Denmark and Sweden, which may be quite of help to the work of this project.
These papers, especially for the researches conducted in Sweden, show the alternatives in their studies and the significant explanatory variables, which can be the references to our works.
1.2.1 Discrete choice modelling
To model the car choice behaviour, a good way is to employ the discrete choice mod- elling methods. The following discussions are based on the description regarding to discrete choice theory in the book of Train (2009), Discrete Choice Methods with Sim- ulation.
To apply the discrete choice methods, the following three assumptions of our alter- natives must hold:
� Mutual exclusivity: that the car buyers purchase one car indicates she does not buy other cars.
� Exhaustiveness: all possible alternatives are covered and the car buyers are to
1.2. LITERATURE REVIEW 17 choose one of the alternatives.
� Finiteness: the number of alternatives is countable.
Among these criteria, what has to be noted is the second one that our choice sets should be exhaustive. Our choice sets include all available car information in Sweden domestic passenger car market. Actually, one can buy foreign cars directly from other countries, and one can also buy pure electrical cars or formula-shaped cars, which are conceptual. These alternatives are not counted in this thesis as first such information of these cars are of lack. And, as what has been analysed before, the share of these cars are rather minor. One may complicate the model by taking account of these data but the affect to the whole model would be limited. So, in this project, we focus on the prevailing passenger cars in the domestic automobile market.
1.2.2 Modelling methodology
Before the car buyers make their decisions, we assume that their purchase behaviours are rational, which means that they intend to maximise the utilities of their choices.
Thus, we are to construct the utility functions of car choices like the shape of equa- tion 1.1.
Unik = Vnik+ εnik (1.1)
Utility function 1.1 shows the utility of individual n choosing car alternative i in year k, which are consisted by two parts, the deterministic term Vnikand the error term εnik. The deterministic part of utility Vnik is linear in parameter represented by βk�xnik where βk� is a vector of parameters of car attributes xnik in year k. The error term εnik
is treated as the random error, which captures the unobserved part of the utility. In logit model employed in this project, each εnik is assumed to be independent and identical with Gumbel distribution, of which density function is like:
f (εnik) = e−εnike−e−εnik
After several mathematics derivations, the probability for individual n to choose i in year k is:
Pnik = eVnik
�J
j=1eVnjk = eβ�kxnik
�J
j=1eβ�kxnjk (1.2) To estimate the model, we try to find the value of vector βk, which can maximise the log-likelihood function 1.3, where ynikequals 1 if individual n chooses alternative iin year k.
LL(βk) =
�N n=1
�
i
ynikln Pnik (1.3)
18 CHAPTER 1. INTRODUCTION AND LITERATURE REVIEW Plug equation 1.2 into 1.3, we can eventually obtain the form of log-likelihood func- tion like:
LL(βk) =
�N n=1
�
i
ynik
�
eβk�xnik�
−
�N n=1
�
i
ynikln
� J
�
j=1
βk�xnjk
�
(1.4)
To maximise the log-likelihood function, we simply take the first-order condition by making the derivative of function 1.4 equal to zero:
dLL(βk) dβk
=
�N n=1
�
i
(ynik− Pnik) xnik = 0
While we get the estimated parameters, ˆβk, we can study the likelihood ratio as
ρ = 1− LL( ˆβk) LL(0) .
to see how the likelihood ration can be better. In this goodness of fit measurement, LL(0) indicates the log-likelihood by setting all other parameters as zero. So, this ρ provides us a relative value between 0 to 1 reflecting the improvement from the esti- mated parameters to zero parameters. Extremely, if ρ = 1, it means that the model predicts the choices in the sample perfectly.
1.3 Thesis structure
To handle the analysis of Sweden new car purchase, two analysis methods are intro- duced. One is descriptive analysis, dealing with the statistical analysis in an aggregated level. In the aggregate analysis, we do not know how various parameters may affect the behaviour of buying a new car. Thus, the second part is taken account, which is disag- gregated analysis. With the help of discrete choice modelling, the resultant models in this part can be of help in forecasting and in response to policies.
Therefore, this project is divided into 2 major parts:
• Part 1 is from Chapter 3 to Chapter 5, conducting the descriptive analysis of the demand (car registries) and the supply (car attributes), involving the market- and statistical analysis. Specifically, Chapter 3 makes an analysis of the whole Swedish car fleet data, which is the basis for the following data analysis and modelling. Chapter 4 extracts subsets from the car fleet data as the new car registration data in each year which is to be used for the modelling. Due to the incomplete car characteristics information in the new car registration data, we added an additional car supply data including the car attributes to our analysis, which is conducted in Chapter 5.
1.4. SCOPE AND LIMITATIONS 19
• Part 2, from Chapter 6 to Chapter 7, shows the methodology of data matching as well as the structures and results of modelling. In this part, Chapter 6 analyses different scenarios of matching under various criteria. And the matching method with the best result is proposed. Chapter 7 constructs in total 18 multinomial logit models for each year (2005 to 2010) with each type of ownerships (private owned, company owned and company owned for leasing). The results of these models are explicated. And two major issues are analysed. One is the influence of “clean car” compensation, whereas the other is the decline brand value of a Swedish car maker, Saab.
Before the analysis, the motivation to deal with this project is introduced and a sum- mary of previous related researches is drawn. After the 2-part analysis, the conclusion is made and some limitations are discussed.
Last but not least, in this project, the data sources of all the tables and charts are from author’s calculation, unless otherwise stated.
1.4 Scope and limitations
This thesis focuses on the data processing and modelling approaches dealing with the Sweden car fleet modelling. Different from other master’s these, this thesis estimates models from 6 years, with a model-engine level, which have more accurate attributes (e.g. price) with smaller coefficient of variation. This thesis also develops a compre- hensive method adapted to deal with the Sweden new car choice data, which can be replicated for the following researches.
However, there are still some issues that should be paid attention to. This thesis handles the choice of new car in Sweden, where the term of “new car” actually refers to
“new passenger cars”. This means that the luxury cars (e.g. Ferrari and Lamborghini), recreational vehicles1 (e.g. B¨urstner), formula shaped cars (e.g. Ariel Atom 2), and conceptual cars, (e.g. Think City), are not taken account into our models. And due to the incompleteness of our car supply data, there is no information about gas fuel (e.g. LPG or Bio-gas) cars. The information about this kind of cars cannot be captured though there are gas fuel cars being registered in each year. In this thesis, the omittance of alternatives in terms of gas fuel limits the accuracy of the resultant models.
This thesis estimates one static MNL model for each type of ownerships per year.
And it may be better if we estimate dynamic models with the consideration about time parameters. To develop dynamic models, we have to track the information of each
1This term is also called as camper van, which refers to the vehicles installed with living space and home amenities.
20 CHAPTER 1. INTRODUCTION AND LITERATURE REVIEW individuals. However, the car registration data we have only contain the identity (reg- istration number) of each car instead of each owner. And it is hard to find the unique attribute of each car owner.
Another issue is that, when we match the data in Chapter 6, the price of car is irrelevant to the fuel types. This can introduce errors to the results, since in this case, the only parameter distinguishing different fuel type of cars is the fuel dummies. But actually, the prices of various fuel type cars are different, and this may play an important rule in affecting car buyers’ choice behaviour.
1.4.SCOPEANDLIMITATIONS21 Table 1.4.1:Summary of literatures
Reference Data source (year) Model Notes Alternatives Significant attributes
Mannering et al. (1991) 488 complete vehicle ownership NL � Upper level: chosen price (-)
histories in U.S. (1989) new or used car alternatives weight (+)
� Lower level: from RP data repair index (+)
make/model/vintage utility vehicle (-)
� U.S. car:
age (+)
� Japanese car:
pacific coast (+) metropolitan area (+)
� brand loyalty:
Pre 1980s: Chrysler 1980s: Japanese Big 3
Choo and Mokhtarian (2004) A survey of 1904 residents in MNL 9 categories: travel attitudes
San Francisco Bay Area (1998) small; compact; personality
mid-sized; large; (e.g. calm, organizer) luxury; sports; lifestyle
minivan/van; (e.g. workaholic) pickup; SUV demographics
(e.g. age, HH income)
Train and Winston (2007) A random sample of 458 Mixed logit 200 makes and retail price (-)
consumers from U.S. (2000) models with the retained value (+)
vintage of 2000 horsepower/weight (+) auto transmission (+) wheelbase (+) length - wheelbase (+) fuel consumption (-) car type dummies car maker dummies
Continued on next page
22CHAPTER1.INTRODUCTIONANDLITERATUREREVIEW
Table 1.4.1 – continued from previous page
Reference Data source (year) Model Notes Alternatives Significant attributes
Arnberg et al. (2008) 131,214 observations from MNL log of price 1,266 new car log-price (-)
Denmark (1992-2001) versions fuel consumptions (-)
weight (+) payload (+) acceleration (+) air-bags (+) ABS (+) 4 doors (+) car types
Hess et al. (2009) RP data from telephone, CNL � Upper level: 15 car types × price (-)
SP data from mail-back paper car nest: 15 car types; 7 fuel types, income (+) or online survey, from fuel nest: 7 fuel types; combining with car age (-)
California (2008-2009) � Lower level: 4 stated choice acceleration (+)
105 combinations alternatives range (+)
fuel efficiency (+) fuel availability (+) large HH for large vehi- cle (+)
large vehicle with alter- native fuel (-)
Hugosson and Algers (2011) Complete Swedish vehicle stock NL � Upper level: 300 car models price/benefit tax
from Swedish Car Register and car brands running cost
an SP survey of new car purchase � Lower level: size class
from autumn 2005 car models tank volume
rust protection warranty safety
engine power share of fuel stations car make
End of table
1.4.SCOPEANDLIMITATIONS23
24 CHAPTER 1. INTRODUCTION AND LITERATURE REVIEW
Chapter 2
Data storage and processing
2.1 Introduction
In this thesis, we consider a static discrete choice model and estimate it based on the revealed preference (RP) data within each year. Three main data sources are available.
The first one is the Sweden whole car fleet registration data from 2004 to 2010, includ- ing the information about every car being registered in Sweden, e.g. car registration number, name of car makes, horsepower, etc.. The second one is the car registry data of new vehicles from 2007 to 2010, which can be treated as subsets of the ownership data. These data are used in previous studies about Sweden car fleet modelling. The third source of data covers the supply information in Sweden. The supply data show the actual car alternatives in Swedish new car market and their features, including more than 100 attributes of each car version which have been available in Swedish car market since 1999.
2.2 Data storage and migration
The original data are stored in the SPSS1(.sav) format. Each size of these files is more than 2 gigabyte since there are more than 5 million rows of records in each year. With these huge amount of data, we may find that SPSS cannot process these data rapidly.
In addition, as a commercial software, SPSS cannot be available in every computer.
Besides SPSS, another spreadsheet software, Microsoft Office Excel, is not even able to store such amount of data, since the maximum row in Excel 2007 is limited to 1,048,576 rows.
Therefore, one of the feasible solutions is to migrate the data from SPSS to an SQL database, which is able to process the data in a very short computational time.
In this project, an open source database, MySQL2, is chosen. In fact, besides SQL
1IBM SPSS statistics. http://www.spss.com/
2MySQL Community Server. http://www.mysql.com/
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26 CHAPTER 2. DATA STORAGE AND PROCESSING database, another database, Microsoft Access, is also available. But, since one single Access database file cannot exceed 2 gigabytes, this database cannot be used for the migration here. However, since the speed of table joining in Access is faster and more user-friendly, the usage of Access for data matching is specified in Chapter 6.
Due to some practical issues, namely, that the MySQL server is set up in a Unix- based system, and the “myodbc” connector has some unknown problems in such a system, one cannot export the data directly from SPSS to MySQL database. As a result, another feasible method is to export the whole data into a .csv file, and than use some SQL commands to import the .csv file. Because of the decimal symbol in Sweden is comma as well, this situation may lead to a conflict with the comma delimited format.
Fortunately, in SPSS, the cells including comma are enclosed by quotation marks when a .csv file is exported. Thus, when one implements the data import, this fact should be defined in the SQL commands.
2.3 Software and processing
After, we migrate the data to our MySQL database as what has been introduced in Section 2.2. Since MySQL itself does not contain many data analysis tools, we use R3 for the statistic analysis. James (2001) proposed an open source interface package of R that can connect to MySQL database, named “RMySQL”. With the database interface (DBI) of R, the RMySQL package allows one to call the tables in MySQL. Therefore, the statistic analysis of the demand and supply can be easily done.
With comparison to Excel, the combination of R and MySQL has the following ad- vantages. First, the huge amount of data can be processed quickly in MySQL. Second, both MySQL and R are cross-platform, which means that they can be run in Microsoft Windows, Linux or Mac OS systems with good compatibility. In Excel, the Data Anal- ysis Toolbox is currently only available in Windows and there is no Microsoft Excel edition available for Linux system. Finally, MySQL and R are both open source that ones can make changes for their own preference.
Meanwhile, besides R, Matlab4can be another useful tool for the statistic analysis.
To connect Matlab with MySQL database, several interface codes are available. For instance, Almgren (2005) releases a function file named “Matlab Database Connector”, which is able to execute the SELECT query in Matlab and insert additional data to the database. Though both Matlab and R are compatible for either Windows-based or Unix-
3The R Project for Statistical Computing. http://www.r-project.org/
4http://www.mathworks.com/products/matlab/
2.3. SOFTWARE AND PROCESSING 27 based systems, Matlab is not a free software and an additional statistics toolbox5is also needed for the analysis. Despite of the charges, the computing performance of Matlab is more powerful than R as one can program her own code in Matlab with custom settings.
5http://www.mathworks.com/products/statistics/
28 CHAPTER 2. DATA STORAGE AND PROCESSING
Part I
Descriptive analysis
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Chapter 3
Descriptive analysis of vehicle ownership
3.1 Introduction
In this chapter, the vehicle ownership in Sweden are analysed. The ownership data show the information about every car which is currently registered in Sweden. The information is provided by two institutions: SCB1, which provides the data from 2004 to 2007, and the Swedish Transport Agency2, which provides the data from 2008 to 2010. In these stock data, the registration of the whole vehicle population these years in Sweden are all available. Due to the different sources, the registration details in these data are not identical. However, some useful information of car attributes can still be found in both resources, which are enumerated and explained below.
� Car plate license, which is called as “registry number” in the datasets with the shape of “ABC123”, indicating the Swedish car plate license of each registered vehicles.
Each vehicle has and only has one unique license serial number.
� Car make, each of which is given to an abbreviation with two letters in the stock data. For instance, VO is the abbreviation of Volvo; VW is short for Volkswagen.
� Vintage, which shows the produced year of a car.
� Direct import, which distinguishes between the domestic cars and the imported cars. Since the imported cars may be registered abroad before enter Sweden, they may have very old vintages and different attributes.
� Power, which demonstrates the maximum power of each registered car in kilo- watt.
� Total weight, which means the payload that a vehicle can afford plus the car’s own weight.
1Statistics Sweden, Statistiska centralbyr˚an in Swedish. For SCB, the data are from the Swedish Transport Agency as well, but the data we get have been processed by SCB.
2Transportstyrelsen in Swedish.
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32 CHAPTER 3. DESCRIPTIVE ANALYSIS OF VEHICLE OWNERSHIP
� Fuel type, which is coded from 1 to 17, each of which indicates a type of fuel.
The fuel types are listed in Table 3.1.1.
� Clutch, which mainly includes manual and automatic transmission.
� Length and width, which is measured in cm in SCB’s data but in mm in Swedish Transport Agency’s data.
� Other car attributes like colour, environmental class (Sweden standard) are also available in the datasets.
Besides these car attributes, some social-economic values are also registered, such as the car owner’s living area or the gender and birthday of the current owners.
Table 3.1.1:Fuel types and codes
Code 1 2 3 4 5 6
Name Gasoline Diesel Electricity Kerosene Liquid-gas Producer gas
Code 7 8 9 10 11 12
Name Ethanol Methanol LPGα Canola oil Paraffin Natural gas
Code 13 14 15 16 17
Name Biogas E85β RMEγ Methane Hydrogen
αLiquefied petroleum gas.
βE85 is the mixture of 85% methanol and 15% gasoline.
γ Abbreviation of Rapsmetylester in Swedish, which refers to a type of biodiesel.
3.2 Car ownership analysis
3.2.1 Car ownership share by make
Unlike buying yoghurt or coke, the price of a car is much higher and the purchase behaviour of a car for an individual is not recurrent in a short term (e.g. 1- or 2-year).
And a car is more durable than some daily goods (like yoghurt). Therefore, one has an (acceptable) experience about a particular car make, she might feel that the uncertainty of giving up a familiar brand but choosing another brand might be of importance. Under such an assumption, the conservative choice that purchasing the make have even owned may be the best choice. Actually, in the U.S., Mannering et al. (1991) investigate the brand loyalty into their research of the vehicle choice model in the U.S. automobile market in 1980s. They point out that brand loyalty is an essential explanatory variable that can affect the market shares. Train and Winston (2007) present an interpretation in terms of brand loyalty that, due to the confidence built in one brand for a consumer, her own experience with this brand is likely to affect her decision to buy the products of the
3.2. CAR OWNERSHIP ANALYSIS 33 same brand in the future. According to these explanations, if the consumer behaviour between Sweden and the U.S. is similar, it is necessary to study the historical car fleet ownership in Sweden, like what the market shares are and if the shares have shifted for these years.
Table 3.2.1:Rank of car ownership shares by make
2004 2005 2006 2007 2008 2009 2010
1 Volvo Volvo Volvo Volvo Volvo Volvo Volvo
0.2335 0.2362 0.2346 0.2335 0.2303 0.2292 0.2270
2 VW VW VW VW VW VW VW
0.0982 0.0988 0.0985 0.0982 0.0976 0.0981 0.0995
3 Saab Saab Saab Saab Saab Saab Saab
0.0860 0.0890 0.0877 0.0860 0.0835 0.0812 0.0778
4 Ford Ford Ford Ford Ford Ford Ford
0.0730 0.0874 0.0757 0.0730 0.0698 0.0686 0.0671
5 Opel Toyota Toyota Toyota Toyota Toyota Toyota
0.0571 0.0543 0.0553 0.0563 0.0572 0.0589 0.0598
6 Toyota Opel Opel Opel Audi Audi Audi
0.0533 0.0539 0.0513 0.0487 0.0460 0.0478 0.0479
7 Audi Audi Audi Audi Opel Opel Opel
0.0470 0.0476 0.0481 0.0483 0.0460 0.0449 0.0430
8 Mercedes Mercedes Mercedes Mercedes Mercedes Mercedes Mercedes
0.0394 0.0394 0.0394 0.0393 0.0388 0.0387 0.0387
9 Renault Renault Renault Renault Renault Renault BMW
0.0345 0.0357 0.0366 0.0369 0.0367 0.0367 0.0373
10 BMW BMW BMW BMW BMW BMW Renault
0.0298 0.0311 0.0325 0.0340 0.0353 0.0362 0.0373
11 Peugeot Peugeot Peugeot Peugeot Peugeot Peugeot Peugeot
0.0275 0.0296 0.0314 0.0331 0.0352 0.0352 0.0355
12 Nissan Nissan Nissan Nissan ˇSkoda ˇSkoda ˇSkoda
0.0271 0.0258 0.0243 0.0228 0.0233 0.0242 0.0256
13 Mazda Mazda Mazda ˇSkoda Nissan Citro¨en Citro¨en
0.0257 0.0239 0.0223 0.0214 0.0211 0.0206 0.0203
14 Mitsubishi Mitsubishi Mitsubishi Mazda Citro¨en Nissan Nissan
0.0210 0.0209 0.0205 0.0206 0.0199 0.0200 0.0200
15 ˇSkoda ˇSkoda ˇSkoda Mitsubishi Mazda Hyundai Hyundai
0.0159 0.0176 0.0195 0.0196 0.0188 0.0190 0.0200
16 Citro¨en Citro¨en Citro¨en Citro¨en Mitsubishi Mazda Mitsubishi
0.0154 0.0167 0.0175 0.0189 0.0187 0.0183 0.0175
17 Hyundai Hyundai Hyundai Hyundai Hyundai Mitsubishi Mazda
0.0126 0.0142 0.0156 0.0167 0.0182 0.0180 0.0174
18 Honda Honda Honda Honda Honda Honda Honda
0.0108 0.0108 0.0108 0.0112 0.0119 0.0119 0.0121
Continued on next page
34 CHAPTER 3. DESCRIPTIVE ANALYSIS OF VEHICLE OWNERSHIP
Table 3.2.1 – continued from previous page
2004 2005 2006 2007 2008 2009 2010
19 Seat Chevrolet Chevrolet Chevrolet Chevrolet Chevrolet Chevrolet
0.0079 0.0082 0.0087 0.0092 0.0104 0.0105 0.0107
20 Chevrolet Seat Seat Seat Seat Seat Kia
0.0074 0.0080 0.0081 0.0083 0.0085 0.0085 0.0097
Sum of the shares
0.942 0.940 0.939 0.936 0.929 0.927 0.924
End of table
According to Table 3.2.1, Volvo, Volkswagen, Saab and Ford have the highest own- ership in Sweden from 2004 to 2010. Especially, Volvo shares about 23% of whole ownership in Sweden. For another Swedish car make, Saab, though it has the third largest market share in Sweden, its whole ownership share decreases continuously since 2005. For foreign car makes, except Ford and Toyota, most Swedish car owners own the European make of cars. Meanwhile, the trend of the ownership of European cars seems to increase in these years, represented by ˇSkoda and Citro¨en. But for some Japanese makes in this table, Nissan, Mazda and Mitsubishi, their total market shares of ownership decline continuously in these 7 years. These trends are shown clearly in Figure 3.2.1. Although the share of Volvo goes down slightly, it still shares more than 20% of the whole ownership, twice more than Volkswagen. The shares of Saab and Ford decline while the share of Toyota increases steadily. If this trend did not change, Toyota would replace the standing of Ford, even Saab, in the future. As a typical exam- ple of European make, ˇSkoda’s market share continuously goes up, whereas Mazda, as an example of Japanese make (except Toyota), follows a converse trend of ˇSkoda.
If Mannering and Train’s assumption holds, these trends may demonstrate that if an individual owns a Volvo (or Volkswagen) car, she may probably choose another Volvo (or Volkswagen) as well if she wants to purchase a new car. If one owns a Nissan, Mazda or Mitsubishi, she may likely choose another makes as her new car due to the continuous decline of ownership.
3.2.2 Car ownership by vintage
Due to the different data source, there are a large amount of vintage data omitted from 2004 to 2007. That is because in the registration, it is not mandatory for the car own- ers to register their car vintage. In fact, in data from 2008 to 2010, there are still a
3.2. CAR OWNERSHIP ANALYSIS 35
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Figure 3.2.1: Total market share of different brands
large amount of data missed, but the Swedish Transport Agency provides the following priority to cover the omitted vintage:
� Year of the model, ˚arsmodell in Swedish.
� Registry date of import, which means the date of first registration abroad.
� Production month.
� Registry date, if none of above is available, the last choice is to use the year of registration.
Therefore, to obtain a better consistency in 2004 to 2007, we use the same prece- dence order, provided by the Transport Agency above. What have to be noted is that, in these 4 years’ data, we do not have the data of registration abroad. So, in our ad- justment, we consider the production month as the second priority. However, this may cause some confusions. The year of the model can be an indicator of the model’s shape, since for different years, the shapes can be various. One of the examples is Volkswagen Passat, of which 2004’s model (Passat B5) is different from the 2005’s model (Passat B6). However, the Passat model produced in the begin of 2005 is still the 2004’s model (Passat B5), since the new model, B6, is first exhibited in March of 2005 at the Geneva Motor Show. Then until the summer of 2005, this model starts to be available in Euro- pean new car market. In this case, the production year/month and the year of the model are not be equivalent. Fortunately, one cannot say this situation may happen frequently,
36 CHAPTER 3. DESCRIPTIVE ANALYSIS OF VEHICLE OWNERSHIP
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$()'" $()#" $()*" $()+" $())" $()(" $((!" $(($" $((%" $((&" $(('" $((#" $((*" $((+" $(()" $(((" %!!!" %!!$" %!!%" %!!&" %!!'"
!"#$%&'(%)
Vintage
Car ownership by vintage in 2004 - 2010
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Figure 3.2.2: Ownership by vintage of 1984 to 2004
since that if a model were launched to the market, it would be stable for several years.
So, the majority of the years of the model can be the same to the production years.
Meanwhile, the production year and the registration year can be different as well, e.g. a car assembled in December can be registered in January of the next year.
Figure 3.2.2 indicates the change of car ownership in 2004 to 2010 by different vintages. This figure summarises the ownership of cars the vintage from 1984 to 2004.
According to the chart, the differences of ownership are quite obvious before the 1997 vintage. An extreme example is that, in 2004, the ownership of 1988 vintage cars is approximately 350,000; but in 2010, the ownership of 1988 vintage cars decreases by 200,000, to the number of less than 150,000. However, with the vintage after 1997, the numbers do not alter largely. This may show that, during 2004 to 2010, the vehicles with the order vintage of 1997 are more likely to be scrapped. Namely, if the age of vehicle is older than 15 years, the owners may probably decide to change them. Or, we might say that the expected life-span of a car is roughly 15 years.
Additionally, this chart also shows that some particular vintages of cars (e.g. 1988 and 1999) are more than other vintage of cars in the stock data. And between the con- secutive registration years, the number of obsolescence of cars in various vintages are not the same. For instance, between year 2005 and 2006, more 1988 vintage of cars are scrapped than the 1998 vintage of cars. Plus, for the same vintage of cars, the number of abandonment of cars are various either in each year. Like, for the 1988 vintage of cars,
3.2. CAR OWNERSHIP ANALYSIS 37 there are about 100,000 cars of this vintage are abandoned from year 2005 to 2006, and only about 20,000 cars of this vintage are scrapped from year 2009 to 2010. Actually, in 2005, when the age of 1998 vintage of cars becomes 17 year-old, these car owners starts to abandon their cars. Till 2008, when the numbers of 1988 cars in the stock data decrease to about 170,000 from 330,000, the speed of obsolescence goes down. So, besides the judgement to the approximate life-span of a car, this phenomenon can be of help to the following researches in terms of car scrapping models.
38 CHAPTER 3. DESCRIPTIVE ANALYSIS OF VEHICLE OWNERSHIP
Chapter 4
Descriptive analysis of new car registries
4.1 Introduction
4.1.1 Extraction of new car data
To obtain the new car registration data, we have generate the RP data of new car regis- tration. The first method (method 1) is to use the new car registration data extracted by the transport agency. Since these data are used for previous studies in terms of Swedish new car choice modelling. The second alternative (method 2) is to find the cars which newly enter the stock data per year. That is, if a car is not available in 2007 but ap- pears in 2008, we would consider it as the new car in 2008. This interpretation is also reasonable since the data of new car can only be first appeared in the stock data of the purchase year. The final method (method 3) is simply to focus on the attribute named
“date for first registration” in each year’s stock data. For instance, if a car is registered in 2008 in the stock data of 2008, we may count it as a new car in 2008.
All of these three methods are quite reasonable, but we need to find the most ac- curate one among this three. To test and verify which method is more accurate, we consider the data provided by “trafikanalys” as our reference, which can be counted as the official statistic data, which are available in 2006 to 2010.
Table 4.1.1: Different numbers of new car registration
2006 2007 2008 2009 2010
Method 1 N/A 329,013 270,815 221,837 306,164 Method 2 307,423 302,734 318,612 218,238 320,289 Method 3 313,522 338,216 274,286 225,084 306,465 Trafikanalys 313,812 338,538 276,344 228,528 308,734
The comparison of the numbers generated by each method is shown in Table 4.1.1 and Figure 4.1.1. Comparing with these three methods, we may find that “method 3” is the best alternative to generate the new car datasets, since for method 1, the data before
39
40 CHAPTER 4. DESCRIPTIVE ANALYSIS OF NEW CAR REGISTRIES 2007 are not available, while for method 2, the differences from the reference data are quite great. Meanwhile, to test whether method 3 indeed fits the official statistics, Fig- ure 4.1.2 compares the monthly sales computed by method 3 from the sales provided by Trafikanalys from January, 2006 to December, 2010. These two lines almostly coin- cide, which means that the method that considers about the “date of first registration”
fits the official statistics. Therefore, we are to use this method to generate the new car data from 2004 to 2010. In fact, it is reasonable that all the data from method 3 are slightly smaller than Trafikanalys’ data, since there is a small amount of registration information omitted in the stock data each year, for instance, one may not provide any- thing about the registration date in the stock data. So, with a synthesis consideration above, the new car data generated by the 3rd method is finally chosen to be our new car choice data.
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Different number of new car registration
+,-./0$!$
+,-./0$%$
+,-./0$&$
12345363789$
Figure 4.1.1: Different numbers of new car registration
Due to the discussions above, the new car registration data are the subsets of the ownership data. According to the car registration, we can know the circumstances in- volving the annual sales of each car make and model. Nonetheless, the RP registry data do not only contain the information of new cars with the latest vintages, but also have the registration records with old car models. Mostly, the registration of records show the information of new cars, since the information of second-hand cars are al- ready registered. The change for a second-hand car simply leads to the change of its owner. However, there are still some exceptions that a used car is registered in the new car registration data. One of the cases is that if a vehicle is imported directly from an- other country. Since this used car has never been registered in Sweden, this information
4.1. INTRODUCTION 41
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!"#$%& Monthly sales of new cars
+,-./#01,1#
2314/151678#
Figure 4.1.2: Comparison of monthly sales
maybe appears in the new car registration data.
In the registration data, besides the car plate license (registration number) and vin- tage, the following attributes are recorded:
� Car model and model name, which include the make and model name of the registered cars, like “Volvo B + V70”. But such information is not available in data from 2004 to 2007.
� Group-code. In majority, each car model or version has a unique code with the form of “AA 123456”, where the first 2 letters indicate the abbreviation of car make and the last 6 digits refer to the car version. Nonetheless, unfortunately, the terminology of group-code is unknown. In Section 4.1.2, we try to find the terminology for each year.
� Owner, which indicates if the car is owned by personal or by a company.
� Leasing, which means that if a car is leased.
Except these values, some environmental related attributes like fuel type and envi- ronmental class are contained in the new car registration data as well.
4.1.2 Model name generation for 2005 to 2007
The attribute of car model name is essential, since we cannot know which model of the car is without this information. However, this number is only available from 2008. To attain the model names before 2007, a process is developed, which is shown in the flow chart, Figure 4.1.3. The procedure is adapted designed for the new car registration data from 2005 to 2007. First, we need to match the new car registration data with the stock data in 2008 by registration number. In this step, we can add the model names of the same cars which are available in 2008 to the new car data. However, since a car can
42 CHAPTER 4. DESCRIPTIVE ANALYSIS OF NEW CAR REGISTRIES be registered in 2007 and be deregistered in 2008, in the second step, we separate the cars which are deregistered in 2008 from the cars which are still holding. Then, for the holding cars, we know their registered model names, like “Volvo B + V70”, whereas for the deregistered cars, we still cannot know the names of the models, which is shown as null. Thus, we need to find the car name information of these deregistered cars.
match with registration
number new car
registries 2005 - 2007
stock data 2008
new table
group code corresponds to car name
match with group code
deregistered in 2008 (car name
available) deregistered
in 2008 (car name unavailable)
holding in 2008 (car name
available)
final table holding
deregistered
Figure 4.1.3: Procedures of finding model names before 2008
What can be done is to reference the information from the holding cars and their corresponding group codes, since we can trust that the terminology of the group code holds the same within each year for the same car registration data. Next, based on this assumption, we can generate a mapping that each group code maps the corresponding car model name. With this map, we can then get the model names of the deregistered cars since we know their group-codes. What has to be noted is that, since some group- codes may not only indicate one car model, there may be some errors. For example, if the code is like “VO 000000”, no one can get any information on it, except the brand,
4.2. MARKET ANALYSIS OF CHOICES 43 Volvo in this case. That is, not all of the group-codes refer to a unique known car model. Nonetheless, it seems to be the only way to obtain the omitted model names.
Based on these discussions, we know that the mapping may introduce some errors.
To evaluate how many errors are introduced, it is necessary to provide the evidences in terms of the share of deregistered cars in 2008 for each year’s new car data. Thus, Table 4.1.2 shows the ratio of deregistration to the whole number of new car registration.
According to this table, we can say that the share of the deregistration is quite small in the whole new car registration data. In this minority car registries, the share of a car being deregistered with an ambiguous group code is quite small. So, even if there were some errors introduced by the mapping, it would not affect the total share significantly.
Eventually, with the generation of deregistered car model names, and the holding car model names, we need to combine these two parts of data to get the final table. This table thus contains model name information we need.
Table 4.1.2:Numbers and shares of new cars deregistration in 2008
2005 2006 2007
Deregistration 7,303 4,710 3,017 New car registries 311,242 313,522 338,216
Ratio 0.022 0.015 0.009
4.2 Market analysis of choices
In the Swedish new car market from 2005 to 2010, there are in total 47 car makes and more than 300 models that have ever been available in the market, which is listed in Table A.1 in Appendix A. However, the number of makes and models are not constant.
From 2009, a Romanian car make named “Dacia” enter the market with 2 models, Logan and Sandero. In 2010, Dacia introduces a new model of car, Duster, into the market. Similarly, other car makes also promote their new car models into the market and withdraw some of their models from the market.
4.2.1 Issues of defining price
Considering about the data matching part in this project. In the registries, we have different vintages of the cars which are sold as new cars. In fact, for instance, in car registry data in 2008, one may not know if a 2006 vintage of car is sold as the same price as that in 2006. There is not a clear boundary of it and such the price information
44 CHAPTER 4. DESCRIPTIVE ANALYSIS OF NEW CAR REGISTRIES of a same model in different years is causing lack. Even for a car with a vintage of 2006 being sold in 2008, we cannot be sure whether its model is really different from the one of 2008, or if they are indeed sold in various prices. So, we have to somehow arbitrarily designate a new definition of “new car” by taking account of the vintage of car from 2 years before to 1 year after in each registration year. And this issue in further discussed in the data matching part, in Section 6.4.3 in Chapter 6.
Table 4.2.1: Numbers and shares of new registries by vintage
2005 2006 2007 2008 2009 2010
All vintages 311,242 313,522 338,216 274,826 225,084 306,465 Previous 2-year 2,969 2,752 3,121 3,713 6,760 3,868 Previous 1-year 64,425 66,008 64,921 61,270 58,722 49,963 Vintage year 227,208 231,488 257,392 190,386 145,747 235,822
Next 1-year 1,572 1,477 1,162 1,648 1,096 939
Table 4.2.1 shows the volume of the annual new car registries by vintage in Sweden from 2007 to 2010. From this table, by our definition, the sales in new car market decrease roughly by 100,000 from 2007 to 2009, but in 2010, the annual sales recover to the level of 2007. And, the shares of our definition of “new cars” are approximately 95% of the new car annual registration.
4.2.2 Market analysis in Sweden new car market
Table 4.2.2: Market Shares of Top 15 Brands in New Car Market
Rank 2007 2008 2009 2010
1 Volvo 0.2372 Volvo 0.2195 Volvo 0.2447 Volvo 0.2082
2 VW 0.0961 VW 0.1138 VW 0.1251 VW 0.1323
3 Saab 0.0911 Saab 0.0914 Toyota 0.0856 Toyota 0.0653
4 Toyota 0.0682 Toyota 0.0725 Audi 0.0696 Ford 0.0624
5 Ford 0.0589 Audi 0.0652 Ford 0.0649 Audi 0.0568
6 Audi 0.0556 Ford 0.0632 BMW 0.0612 BMW 0.0499
7 Peugeot 0.0496 BMW 0.0542 ˇSkoda 0.0405 Renault 0.0492
8 BMW 0.0471 ˇSkoda 0.0416 Mercedes 0.0347 ˇSkoda 0.0458
9 ˇSkoda 0.0400 Peugeot 0.0359 Saab 0.0334 Kia 0.0370
10 Citro¨en 0.0357 Opel 0.0305 Renault 0.0333 Mercedes 0.0347
11 Opel 0.0339 Mercedes 0.0253 Kia 0.0294 Peugeot 0.0343
12 Renault 0.0260 Hyundai 0.0248 Peugeot 0.0265 Hyundai 0.0310 13 Mercedes 0.0217 Citro¨en 0.0244 Opel 0.0219 Saab 0.0300 14 Honda 0.0206 Renault 0.0224 Hyundai 0.0183 Citro¨en 0.0206
15 Kia 0.0186 Kia 0.0172 Citro¨en 0.0178 Opel 0.0200
Total 0.900 0.902 0.907 0.877
Table 4.2.2 shows the market shares of top 15 car makes in Swedish new car mar- ket from 2007 to 2010. These 15 brands occupy, on average, about 90% of annual
