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Department of Science and Technology Institutionen för teknik och naturvetenskap Linköpings Universitet Linköpings Universitet SE-601 74 Norrköping, Sweden 601 74 Norrköping

ExamensarbeteLITH-ITN-KTS-EX--05/005--SE

Side-effects on safety by makingcars lighter to reduce carbon

dioxide emissions.Ulrike Trommer

2005-01-21

LITH-ITN-KTS-EX--05/005--SE

Side-effects on safety by makingcars lighter to reduce carbon

dioxide emissions.Examensarbete utfört i kommunikations- och transportsystem

vid Linköpings Tekniska Högskola, CampusNorrköping

Ulrike Trommer

Handledare Lennart StrandbergExaminator Lennart Strandberg

Norrköping 2005-01-21

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Examensarbete B-uppsats C-uppsats D-uppsats

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LITH-ITN-KTS-EX--05/005--SE

Side-effects on safety by making cars lighter to reduce carbon dioxide emissions.

Ulrike Trommer

Both, reducing CO2 emissions and accidental injuries in road traffic, have been declared high prioritygoals of Swedish and international policy. Measures reducing either one or the other may conflict withone another. Vehicle weight is decisive of fuel consumption and of CO2 emissions. Therefore the effectof vehicle weight reductions on safety was analysed.

This thesis is structured in a qualitative and a quantitative part. While the fist part gives backgroundinformation about developments in road traffic, energy consumption and emissions from road traffic,and car design of the last decades. The second part is devoted to a quantitative analysis of therelationship of vehicle weight and safety. Statistical methods are applied to accident data, containing allpolice reported road accidents that occurred on public roads in Sweden from 1994 to 1999. But onlysingle vehicle accidents and two-car frontal collisions happened in 1999 were taken into account.Because of data limitations only accidents of passenger cars were analysed.

The attempt to quantify the effect weight has on safety on Swedish roads produced mixed results. Buteven though the regression models for single vehicle accidents and frontal collisions fit the data ratherbadly, basic trends could be found. The analyses of single vehicle accidents and frontal collisionsindicated advantages of heavier cars in protecting their occupants, but these advantages seem to be offsetbecause heavier vehicles tended to increase the injury risk of the drivers of the cars they collided with.

road traffic, road safety, vehicle weight reductions, single vehicle accident, frontal collision, vehiclelength, regression

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© Ulrike Trommer

Abstract

Both, reducing CO2 emissions and accidental injuries in road traffic, have been declared high

priority goals of Swedish and international policy. Measures reducing either one or the other

may conflict with one another. Vehicle weight is decisive of fuel consumption and of CO2

emissions. Therefore the effect of vehicle weight reductions on safety was analysed.

The first part of this master’s thesis gives background information about developments in

road traffic, energy consumption and emissions from road traffic, and car design of the last

decades. The second part is devoted to a quantitative analysis of the relationship of vehicle

weight and safety. Statistical methods are applied to accident data, containing all police

reported road accidents that occurred on public roads in Sweden from 1994 to 1999. But in

order to eliminate changes of driver attitude over time (speeding, alcohol and driving, seat

belt usage) accident data from a shorter period of time is used. Only single vehicle accidents

and two-car frontal collisions happened in 1999 were taken into account. Because of data

limitations only accidents of passenger cars were analysed.

The attempt to quantify the effect weight has on safety on Swedish roads produced mixed

results. But even though the regression models for single vehicle accidents and frontal

collisions fit the data rather badly, basic trends could be found. The analyses of single

vehicle accidents and frontal collisions indicated advantages of heavier cars in protecting

their occupants, but these advantages seem to be offset because heavier vehicles tended to

increase the injury risk of the drivers of the cars they collided with.

Keywords:

road traffic, road safety, vehicle weight reductions, single vehicle accident, frontal collision,

vehicle length, regression

Table of contents

PART I QUALITATIVE ANALYSIS 6

CHAPTER 1 INTRODUCTION 6

1.1 MOTIVATION 6

1.2 APPROACH AND MATERIALS THAT ARE INTENDED TO BE USED 6

CHAPTER 2 SIGNIFICANCE OF THE PROBLEM 7

2.1 TRANSPORT SECTOR IN GENERAL 7

2.2 ENERGY CONSUMPTION AND EMISSIONS FROM TRANSPORTATION 9

2.3 ENHANCED GREENHOUSE EFFECT 14

2.4 ROAD TRAFFIC SAFETY 16

2.5 SOCIETAL GOALS IN THE TRANSPORT SECTOR 20

CHAPTER 3 ENGINE PRINCIPLES AND CO2 REDUCTION MEASURES IN ROAD TRAFFIC 21

3.1 ENGINE PRINCIPLES 23

3.2 CO2 REDUCTION MEASURES 25

3.2.1 Reduce transport energy use – technical measures 25

3.2.2 Measures to improve fuel economy in safety context 29

CHAPTER 4 SIDE-EFFECTS FROM IMPROVED FUEL ECONOMY 30

CHAPTER 5 CONCLUSION OF PART I 35

PART II QUANTITATIVE ANALYSIS – WEIGHTPROBLEM 36

CHAPTER 6 HYPOTHESES 36

CHAPTER 7 DATA MATERIAL 38

CHAPTER 8 METHODOLOGY 41

CHAPTER 9 PRE-STUDY 42

9.1 DATA IN THE PRE-STUDY 43

9.2 POTENTIAL CONTROL VARIABLES 46

9.3 CORRELATION ANALYSES 47

9.4 RESULT OF THE PRE-STUDY 50

CHAPTER 10 RESULTS OF THE QUANTITATIVE ANALYSES 51

10.1 SINGLE CAR ACCIDENTS 51

10.1.1 Controlled analysis for single vehicle accidents 55

10.1.2 Regression of single vehicle accident data 58

10.2 FRONTAL COLLISION ACCIDENTS 63

10.2.1 Controlled analysis of frontal two-car collisions 71

10.2.2 Regression of frontal collision accident data 72

CHAPTER 11 CONCLUSION OF PART II 78

PART III FINAL CONCLUSION 80

APPENDIX A WRITTEN MATLAB SCRIPTS 87

APPENDIX B VARIABLES IN THE ACCIDENT DATA BASE 88

Table of figures

Figure 1: Passenger transport in EU15 – Modal split [%] 8

Figure 2: Performance by mode of passenger transport EU15 [1,000 mio pkm] 8

Figure 3: Worldwide per-capita movement of people and freight, 1850-1990 9

Figure 4: Passenger cars in Sweden by fuel used 10

Figure 5: Main emissions from road traffic in the EU [1,000 tonnes] 11

Figure 6: CO2 from fossil fuels by sector [mio tonnes CO2] 12

Figure 7: CO2 emissions from transport in the EU [mio tonnes CO2] 12

Figure 8: Trends in average CO2 emissions, power, weight and engine capacity for all new

cars in EU (base 100 = 1990) 13

Figure 9: Newly registered car in Sweden 1991-2002 by service weight [%] 14

Figure 10: Traffic injury volume model describing the traffic safety problem 17

Figure 11: Cars in use 1/1 2003 by year model (Sweden) 18

Figure 12: Stock of cars with catalytic converter 1987-2003 19

Figure 13: Injured and killed persons in road traffic [%], Sweden 1970-2002 19

Figure 14: Injured and killed persons in road traffic, Sweden 1970-2002 20

Figure 15: Diesel share of newly registered passenger cars [%] 22

Figure 16: Energy use in vehicles 23

Figure 17: Main actors in the causation of environmental problems and policy 26

Figure 18: Usage of fuel energy in the driving process 27

Figure 19: Annual travelled vehicle kilometres as function of vehicle age 40

Figure 20: Vehicles’ weight in pedestrian and animal accidents happened in 1999 – left:

passenger cars; right: heavy vehicles 44

Figure 21: Weight of passenger cars involved in animal and pedestrian accidents 1999 (left)

and vehicle weight of registered passenger cars in Sweden (right) 44

Figure 22: Age distribution of passenger cars involved in animal or pedestrian accidents and

age distribution of registered passenger cars in Sweden [age represented by year

model] 45

Figure 23: Air bag information (animal and pedestrian accidents, 1999) 46

Figure 24: Footprint of passenger cars in animal and pedestrian accidents by weight 46

Figure 25: Animal accidents 49

Figure 26: Pedestrian accidents 50

Figure 27: Driver injury level as function of average weight – single car accidents 51

Figure 28: KSI (absolute) and KSI rate (per 1,000,000 vkm) – single car accidents 52

Figure 29: Proportions of injury levels in light, medium, heavy cars – single car accidents 53

Figure 30: Number of fatal, seriously, slightly injured and not injured drivers in different

weight classes and injury rate per travelled distance – single car accidents 55

Figure 31: Fatal and severe injuries by vehicle weight [% of group’s total] – 407 single car

accidents 1999 meeting the requirements for the control variables 56

Figure 32: Proportions of injury levels in light, medium, heavy cars – single car accidents

(controlled) 57

Figure 33: Number of fatal, severe, minor and no injuries to drivers in different weight classes

and injury rate per travelled distance – single car accidents (controlled) 58

Figure 34: Injury levels of drivers involved in single car accidents 1999 60

Figure 35: Regression – classification by vehicle weight (single car accidents) 62

Figure 36: Regression – classification by vehicle length (single car accidents) 62

Figure 37: Driver injury level as function of avg. weight – frontal collisions 1999 (N=1370) 64

Figure 38: KSI in absolute numbers and KSI rate per 1,000,000 vkm – frontal collisions 65

Figure 39: Injury rates of both cars involved in a frontal collision 65

Figure 40: Number of fatal, seriously, slightly injured and uninjured drivers in different weight

classes in collisions with the same car type and injury rates per travelled distance 67

Figure 41: Number of fatal, seriously, slightly injured and not injured drivers in different

weight classes in collisions with cars of different weight and injury rates per travelled

distance 67

Figure 42: Injury proportions in the light car (car 1 – left / car 2 – right) in frontal collisions 70

Figure 43: Proportions of injury levels in five weight classes of the other car’s weight 72

Figure 44: Injury levels of drivers involved in frontal collisions 1999 (by weight/length) 73

Figure 45: Regression – classification by vehicle weight (frontal collisions) 76

Figure 46: Regression – classification by vehicle length (frontal collisions) 76

Figure 47: Relation between vehicle weight and length –classes by vehicle weight 77

Figure 48: Relation between vehicle weight and length – classes by vehicle length 77

Table of tables

Table 1: Causes of disease or injury worldwide 16

Table 2: Measures for influencing transport energy use 25

Table 3: Relative likelihood of driver fatality in a car of mass mi involved in a crash with a car

of mass mj 32

Table 4: Main accident databases 38

Table 5: Correlation factors for animal and pedestrian accidents – linear correlation 48

Table 6: Regression coefficients – single vehicle accidents 61

Table 7: Relative injury risk for minor, severe and fatal injuries in frontal collisions 68

Table 8: Interval boundaries for expected values of proportion of injury levels in light cars in

collision with another light car compared to collisions with other heavier cars 70

Table 9: Regression coefficients – frontal collision accidents 75

Abbreviations

CO2 carbon dioxide

mio pkm million passenger-kilometers

GDP Gross Domestic Product

EU European Union (before the enlargement in 2004)

IPCC Intergovernmental Panel on Climate Change

VOCs voltic organic compounds

NOx nitrogen oxides

CO carbon monoxide

PM particulate matters

CFCs chlorofluorocarbons

GHGs greenhouse gases

GNP Gross National Product

HC hydro carbon

KSI number of drivers killed or seriously injured

ppm particles per million

g grams

km kilometres

g/km grams per kilometre

LTV Light trucks and vans

SUV sport utility vehicles

KI drivers killed or injured (seriously/slightly) also referred to as INJ

vkm vehicle kilometres

N total number of cars (Figure 37)

FARS Fatality Accidents Reporting System

PART I QUALITATIVE ANALYSIS

Chapter 1 Introduction

1.1 Motivation

During the 20th century the transport sector played a vital role in the economic development

and improved the mobility in terms of travel time and travelled distance. Along with this, an

improved quality of life for citizens in the developed world can be seen. However since the

1980s the concern about the impact of especially road traffic on human health and the

environment have increased. Globally of great concern are carbon dioxide emissions from

road transport; they are seen as substantial contributors to global warming via an enhanced

greenhouse effect. Over the last two decades, CO2 abatement has been a factor of great

importance in international policy.

Another crucial issue for every society and its economy is road safety; especially accidents

causing fatal or severe injuries are of great concern. European transport policy promotes

sustainable mobility, which should conclude in a transportation that ensures economic

growth while restraining damages on health and environment. Measures to enhance fuel

efficiency, reduce pollution and to use the existing infrastructure in a highly effective and

safest possible way are requested by that policy.

Assuming that the vehicle industry adapted vehicle and engine design in order to serve the

CO2 reduction policy, this master’s thesis intends to investigate the effect this policy of the

last 20 years might have had on road safety.

The thesis will not elaborate on the questions whether climate changes are due to variations

of CO2 contents in the atmosphere or vice versa or due to any other reasons or if measures

to reduce CO2 emissions from vehicles will increase pollutants threatening human health and

the environment, such as NOx from Otto engines or particles from Diesel engines.

1.2 Approach and materials that are intended to be used

I will start my thesis describing the context of road traffic, environment and road safety. This

description includes also a view on the international and Swedish policy in the mentioned

areas. As the vehicle manufacturers have to obey these policies the vehicle engine and

design developments, which are meant to improve fuel economy and by that reduce the CO2

exhaust, are examined in the following sections. The qualitative part of the thesis will focus

on these introductive explanations and a discussion of safety side-effects of weight reduction

as a measure to reduce CO2 emissions. The latter aspect includes a literature review of

research done in the field of vehicle weight and road safety. The review serves as a basis for

the main part of my master’s thesis. This second part wants to answer the question, which

impact vehicle weight reduction has on road safety.

Statistical methods are applied to accident data, containing all police reported road accidents

that occurred on public roads in Sweden between 1994 and 1999. In order to evaluate the

effect of reduced vehicle weight single vehicle and two-vehicle accidents are analysed. In a

pre-study the variables are selected for which the analysis has to be controlled. For the

special cases of two-vehicle accidents with frontal impact and single vehicle accidents I will

attempt to quantify the change of accident consequences in relation to vehicle weight.

Chapter 2 Significance of the problem

2.1 Transport sector in general

Transportation is defined as the mean to satisfy the fundamental human need of mobility;

distances to destinations of interests are bridged by different means of transportation. While

our mobility has been more or less unchanged (approximately unchanged are the number of

trips per day and person and the trip purposes), the transportation systems became more

convenient, faster and we are able to overcome longer distances. As a result, energy

consumption increased and the structure of cities changed, further transportation became

more inefficient because of longer distances travelled to fulfil the same human needs. In

paragraph 2.2 the aspects of energy consumption and emissions from transportation will be

discussed further.

Since road transport dominates surface transportation in highly industrialised nations around

the world, and the car is the main mode of transporting passengers today (see Figure 1,

Figure 2), this thesis will focus on road safety.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

1970

1980

1990

1991

1995

1996

1997

1998

1999

2000 passenger cars

buses

tram/metro

railway

air

Figure 1: Passenger transport in EU15 – Modal split [%]1

0

300

600

900

1200

1500

1800

2100

2400

2700

3000

3300

3600

3900

1970 1980 1990 1991 1995 1996 1997 1998 1999 2000

passenger cars

buses

tram/metro

railway

air

Figure 2: Performance by mode of passenger transport EU15 [1,000 mio pkm]2

In the past, a growing road vehicle fleet often accompanied economic growth3; today’s

biggest industry nations have also the highest motorisation levels. A challenge for this

century will be to achieve economic growth without a further growing vehicle fleet.

The reasons for an increasing world’s vehicle fleet are population growth, urbanisation and

economic growth. Annual GDP growth rates over the coming years are estimated to be

highest in China, East Asia, Central and Eastern Europe and the former Soviet Union, which

1 source of data: Eurostat (2000)


3 OECD (2002), page 23

will also stimulate growth in vehicle populations in these regions. As a result, one can

anticipate a global vehicle population of approximately 2.65 billion by 2020.4

It is true that use of advanced pollution control technology, especially catalysts, have been

spreading, as has the use of unleaded gasoline, but road vehicles still cause – especially in

urban areas – air pollution problems. But beyond direct adverse health effects, there are

other concerns about vehicle emissions. Among those is the enhanced greenhouse effect,

which has been subject of scientific debates and international political meetings for the last

two decades. International policy sees in CO2 a substantial contributor to the enhanced

greenhouse effect, following assessments of the Intergovernmental Panel on Climate

Change (IPCC). As road transport is a major contributor to CO2 exhaust from transport

activities, fuel consumption of road vehicles is influenced by today’s climate policy.

Furthermore, weight reductions of road vehicles are seen as one major measure to reduce

fuel consumption and CO2 emissions. Therefore also safety issues have to be addressed by

political debates.

2.2 Energy consumption and emissions from transportation

As it can be seen in Figure 3, the transportation sector experienced a dramatic growth over

the last century. Above all, automobiles became the dominator of passengers’ transport and

even in freight transport road vehicles take a large share of the transportation work.

Figure 3: Worldwide per-capita movement of people and freight, 1850-19905

4 www.oecd.org

80%

85%

90%

95%

100%

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Gasoline Diesel Electricity Others

Figure 4: Passenger cars in Sweden by fuel used6

A growing transport sector has demanded a constantly increased amount of energy. Today’s

road transportation uses above all gasoline and diesel as sources of energy. In passenger

transportation gasoline plays the dominating roll (see Figure 4). Road vehicles are major

sources of voltic organic compounds (VOCs), nitrogen oxides (NOx), the precursors to both,

tropospheric ozone and acid rain, carbon monoxide (CO), toxic air pollutants such as diesel

particulate (PM), and chlorofluorocarbons (CFCs).

Unlike the emissions mentioned so far (and presented in Figure 5), which are pollutants and

can be reduced by technologies, carbon dioxide (CO2) emissions are not toxic but directly

proportional to the quantities of fuel consumed.7 Therefore, CO2 emissions of a road vehicle

can be controlled via fuel consumption. The amount of fuel consumed by an automobile is

determined by its design and the technologies applied, whether the car is used or not, driver

behaviour and the conditions under which it is used. Discussions of measures that address

energy reduction of the transportation sector as whole are part of Chapter 3.

5 source: Pastowski, Gilbert (2003), p. 2

6 source of data: BIL Sweden (2003)

7 gasoline vehicles emit 2.38 kg of CO2 per litre of fuel; diesel vehicles emit 2.66 kg of CO2 per litre of

fuel

0

5000

10000

15000

20000

25000

30000

35000

40000

Nox CO NMVOC SO2

1,0

00

to

nn

es

EU15 - total

of which transport

of which road transport

Figure 5: Main emissions from road traffic in the EU [1,000 tonnes]8

Some technical measures that reduce fuel consumption and CO2 emissions will increase

certain pollutions, such as nitrogen oxides. Though such side-effects may be highly relevant

for the environment, they are beyond the limits of this thesis.

Road traffic is by far not the only source of CO2 emissions, but in the EU transportation it is

besides electricity production and heating the most important one from fossil fuels (Figure 6).

Road traffic is the major source of CO2 emissions from transportation (Figure 7). As these

figures illustrate, CO2 emissions from transport and especially those from road transport are

still increasing in absolute numbers and also in proportion to other sectors. Another

contributor, the industry sector, reduced its CO2 emissions but a restructuring process during

the 1990s can explain most of this trend. During the same period the European road vehicle

fleet experienced a dramatic growth. As road traffic is the most important source for CO2

emissions from transportation, manufacturers of road vehicles made high fuel economy a top

priority - initiated by international policy, but also as selling factor - but all improvements,

which were made, are offset by a growing fleet and longer distances travelled.


0

500

1000

1500

2000

2500

3000

3500

1990 1995 2000

Electricity & heating Energy branch Industry Households, commerce Transport

Figure 6: CO2 from fossil fuels by sector [mio tonnes CO2]9

550

600

650

700

750

800

850

900

950

1990 1995 2000

road transport air transport inland navigation railways (without electricity)

Figure 7: CO2 emissions from transport in the EU [mio tonnes CO2]9

Even though an improved fuel economy of the single car is an important complement in a

CO2 reduction strategy the fact that it can only be the result from trade-offs has to be

considered. Trade-offs must be made with vehicle characteristics such as acceleration and

handling, comfort, reliability, size, style, low NOx or particle exhaust emissions, noise, costs

in use, and possibly safety as well.

Contrary to the goal to improve fuel economy Van den Brink et al (2002) shows that specific

fuel consumption of the Dutch car fleet has not shown any decrease since 1990. Even

though new very efficient car models enter the market, people seem to be willing to spend


more money on bigger, faster cars. As a result, already existing car models became heavier

and got bigger engines. Figure 8 shows that Van den Brink’s findings are also valid for the

rest of the EU.

Figure 9 reveals a trend to even heavier cars in Sweden, where one can find already today

one of the heaviest vehicle fleets in the EU. While in the beginning of the 1990s 10-20 % of

all new registered cars had a weight of 1,500 kg and more, 10 years later a share of almost

50 % weighs that much.

Figure 8: Trends in average CO2 emissions, power, weight and engine capacity for all new cars

in EU (base 100 = 1990)10

While new light cars came and are still coming into the market on one end of the mass scale,

the already existing cars move towards the other end of this scale, as a result the mass

range increases. This increased mass range can decrease the safety of a vehicle fleet. The

quantitative Part II will also address this problem.

10 source: ECMT (2000), p. 8

0%

20%

40%

60%

80%

100%

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

1700 kg -

1500-1699 kg

1400-1499 kg

1300-1399 kg

1200-1299 kg

1100-1199 kg

1000-1099 kg

900-999 kg

800-899 kg

-799 kg

Figure 9: Newly registered car in Sweden 1991-2002 by service weight [%]11

As already stated in the introductory chapter, it is out of the limits of this thesis to discuss

whether CO2 is a cause for currently observed tendencies of global warming. But politicians

follow the advices given by the IPCC in order to prevent climate change. Much of the work in

international policy and institutions has focused on the development and implementation of

regulatory regimes. Therefore, the theory of enhanced greenhouse effect as elaborated by

the IPCC has an influence on road vehicle fleet and eventually on its safety. The next

paragraph wants to present in short this theory of the enhanced greenhouse effect.

2.3 Enhanced greenhouse effect

The ‘greenhouse’ (or more properly ‘enhanced greenhouse’) effect and the arguments about

its contribution to global warming and the associated climate change have been studied in

detail and discussed for more than a decade.

Climate change is a change in the "average weather" that a given region experiences.

Therefore, the most noticeable effects of climate change are extreme events as storms, heat

waves, floods, and wildfires. Such extreme events have serious impacts on human health,

environmental, social and economic consequences, thus scientists are attempting to predict

changes in the frequency of extreme weather events. Nowadays, it is a common belief that

the earth’s climate is changing because human activities are altering the chemical

composition of the atmosphere through the build up of greenhouse gases – primarily carbon


dioxide, methane, and nitrous oxide. Therefore, it is seen as the key issue to measure

humanity’s effect on the concentration of greenhouse gases in order to understand global

climate change.

Under the Kyoto Protocol carbon dioxide, methane, nitrous oxide, halocarbons, HFCs, PFs

and SF6 are defined as greenhouse gases (GHGs). Although they are only trace elements in

the atmosphere they have the important property of interfering with the passage of energy.

The natural greenhouse effect maintains a stable mean global surface temperature, an

essential condition for the development of a stable ecology of the planet. The problem arises

if the greenhouse effect is enhanced by the changed composition of the atmosphere. Human

activities, and notably the generation of excessive amounts of carbon dioxides through the

combustion of fossil fuels, have increased and will continue to increase the atmospheric

concentration of GHGs.

Nonetheless, climate change is still an issue questioned by several scientists. While

stratospheric ozone depletion as an environmental threat of the 1980s had clearly occurred

and could be monitored, the phenomenon of climate change remains a hypothesis. It can

and will be disputed further on and will only be fully observed well by future generations.

During the last decade different evidences of warming were brought into discussion, but

there still does not exist reliable data confirming the climate change. But as said before, this

paper will not discuss the existence of the enhanced greenhouse effect, but wants to reflect

the fact that the measures to control climate and restrain global warming by reducing CO2

emissions may have influenced road safety.

It is true, that road traffic produces a huge share of the release of over 6 billion tonnes of CO2

per annum into the atmosphere. The IPCC estimates the contribution of CO2 emissions to

enhanced greenhouse effect as being 55 % of all the GHGs (since industrialised times).

Differential distribution of responsibility for GHG emissions on the one hand and probable

impacts on the other hand made it almost impossible in the past to reduce CO2 emissions. In

terms of carbon dioxide emissions there is an asymmetry between the contributions of

developed and less developed economies. It is believed that economic growth and transport

growth are linked, therefore developing countries try to reach higher levels of motorisation.

The future challenge is to assure economic growth without increased or only moderate

increased CO2 emissions.

After having shown the possible connection between road traffic and the climate change, I

will discuss in the next section road traffic safety as another societal problem.

2.4 Road traffic safety

Road traffic accidents are the leading cause of death by injury, the 10th leading cause of all

deaths and the 9th leading contributor to the burden of disease worldwide. They represent a

rapidly growing problem, with deaths from injuries projected to rise from 5.1 million in 1990 to

8.4 million in 202012.

Table 1: Causes of disease or injury worldwide12

In 2020 road traffic disability-adjusted life years lost is estimated to have moved from being

the 9th leading cause of disability-adjusted life years lost to the 3rd leading cause (see Table

1). The quantity of disability-adjusted life years is a measure representing the loss of years of

‘healthy life’. By that causes of diseases or injuries are comparable.

The absolute number of road traffic fatalities is on a relatively low level in Sweden and some

other western European countries, but still, road traffic accounts for more than 40,000

fatalities and for about 1,700,000 seriously injured every year in the European Union (EU).

It’s not only people, who suffer from traffic accidents, also the economy is damaged by that.

Road traffic accidents cost the EU every year € 160 billion, that equals 2 % of the EU GNP13.

Numerous factors influence a country’s safety level. These factors concern transport policy,

distribution and crashworthiness of the car fleet, road network characteristics, human

behaviour and attitudes, etc. A broad range of measures can be introduced to reduce road

traffic casualties. A model presented by Rumar (ETSC, 1999) uses three dimensions to

describe the road safety problem: ‘exposure’ (E), accident probability (‘accident risk’ A/E) for

a certain exposure, and ‘injury risk’ (I/A) when an accident has occurred. The magnitude of

the traffic safety problem (the ‘injuries’ I) is the product of these three factors

(I = [E] x [A/E] x [I/A], see Figure 10).

12 source: WHO (2001)

13 http://europa.eu.int/comm/transport/road/library/rsap/memo_rsap_en.pdf

Figure 10: Traffic injury volume model describing the traffic safety problem14

The first dimension of this model, the exposure to road traffic, could be the most effective

one reducing the safety problem. But we have seen an adverse trend, a growing vehicle

fleet, a road network that is getting longer and an increasing vehicle mileage. The other

countermeasure dimensions, accident probability and injury risk, were addressed by

developments of the car industry as manufacturers try to make modern cars safer.

In the past, improved safety was reached because of improved passive safety, also referred

to as crashworthiness, while recent developments aim to improve active safety of road

vehicles as well, such systems help the driver to avoid accident involvement. Broughton

(2003) investigated passive safety of the British car fleet. Road accident data from 1980-

1998 was used to develop a model to estimate the reduction in the number of occupant

casualties over this period due to improvements in passive safety. Broughton found that the

most modern cars produced less KSI (killed and seriously injured) than older cars. He

concluded that this benefit of more modern cars could be attributed to improvements in

passive safety.

The Swedish passenger car fleet is quite unique in Europe, as cars in Sweden get rather old.

Almost 50 % of the passenger cars registered in Sweden are 10 years old or even older (see

Figure 11). Such a high percentage of old cars could also have a negative effect on road

safety. The fleet benefits less from improvements in passive and active safety, as concluded

by Broughton (2003). The Swedish government intended to change the situation of old and

maybe unsafe cars in the Swedish vehicle fleet. A changed scrapping policy was already

14 source: ETSC (1999), p. 22

successfully introduced. The Swedish society profits from replacing these old cars from a

safety (e.g. airbag) and environmental (e.g. catalyst) point of view.

0%

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2002200120001999199819971996199519941993199219911990198919881987198619851984198319821981 and older

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Figure 11: Cars in use 1/1 2003 by year model (Sweden)15

Swedish car manufacturers have the reputation to produce very safe cars. And so is Sweden

known as a country with a rather small traffic safety problem. But still, every person seriously

injured or killed in traffic is one too much (see paragraph 2.5), so anything possible has to be

done in order to avoid serious and fatal injuries in road traffic.

Lots of measures reducing accident risk or consequences are not related to car design or

technology. Measures improving the infrastructure affect the whole fleet and can therefore be

highly effective. But since this thesis tries to compare risk of light and heavy road vehicles,

measures referring to other aspects than the vehicle weight will not be discussed further.


0

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Figure 12: Stock of cars with catalytic converter 1987-200316

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Figure 13: Injured and killed persons in road traffic [%], Sweden 1970-200217

Nonetheless, Sweden is one of the safest countries and has the ambitious goal that no one

will be killed or seriously injured within the Swedish road transport system. But lately also

Sweden has to struggle with an increasing number of killed or severely injured persons (see

Figure 14).


17 source of data: SIKA

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Figure 14: Injured and killed persons in road traffic, Sweden 1970-200217

2.5 Societal goals in the transport sector

The so-called “Vision Zero” passed the Swedish Parliament in 1997 and it is estimated that

the number of fatalities will have been reduced by quarter to one third during the first ten

years after that. The long-term goal of “Vision Zero” is that no one will be killed or seriously

injured within the Swedish road transport system.

The “Vision Zero” approach concentrates on the whole system and how it can operate safely

as a whole. Also, “Vision Zero” means moving the emphasis away from trying to reduce the

number of accidents to eliminating the risk of chronic health impairment caused by road

accident. The long-term objective is to achieve a road transport system allowing human

errors without leading to serious injuries.

While the concept of “Vision Zero” envisages responsibility for safety among the creator of

the system and its users, the creator has the final responsibility for "fail-safe" measures.

Therefore, all political action has to conform to objectives of “Vision Zero”.

Sweden is active in the field of climate policy. There are several points of uncertainty in the

climate change debate, but Swedish policy is based on the “precautionary principle”, which

means it is not acceptable to wait until it can be definitely confirmed that mankind activities

caused the enhanced greenhouse effect, because it will be too late to prevent serious effects

by then.

As mentioned before, the Kyoto protocol focuses the most on CO2 of the six greenhouse

gases listed in the protocol. Roughly 40 % of Sweden’s CO2 emissions have their origin in

transportation. Road traffic accounts for most of those emissions of transportation, which is

roughly one-third of Sweden’s CO2 overall emissions.18

The Swedish target concerning GHGs is to at least reduce GHG emissions by 4 % until 2010

compared to the 1990 level. The Swedish action is devoted to prevent the concentration of

GHGs exceeding the equivalent of 550 ppm CO2 equivalents. Under the Kyoto Protocol to

the United Nations Framework Convention on Climate Change Sweden became even

entitled to increase its emissions up to 4 %. But also other countries announced stricter

targets. The EU as a whole shall reduce its emissions by 8 % until 2010 based on the 1990

level.18

There is a diversity of policy instruments for CO2 abatement. Policy instruments reach from

economic and legislative ones to voluntary agreements and dialogue between the state and

business enterprises. The EU environment ministers have set 120 g CO2 per km as a target

that shall not be exceeded by new passenger cars in 2005, or at least 2010. A voluntary

agreement over 140 g/km is negotiated with the European motor industry. Therefore, fuel

efficiency became a major factor in car manufacturing.18

In its bill “transport policy for sustainable development”, the Swedish government judged that

by 2010 emissions of CO2 from transport should have been stabilised at the 1990 level. To

achieve this objective a strategy of improved efficiency of the transport system, coupled with

leaner vehicles and the introduction of renewable fuels has been applied. Policy and industry

have spent a great deal of time and resources on improved fuel economy of passenger cars.

Vehicle design is mostly about compromises of economic and policy demands and demands

from consumers. Since car selling is a replacement business, the car industry is under

constant pressure to find the optimum solution of all demands it is confronted with. An

increased fuel economy can only result from trade-offs that must be made among a variety of

vehicle characteristics, of which one is vehicle safety.

Chapter 3 Engine principles and CO2 reduction measures

in road traffic

In the Swedish passenger car fleet the gasoline engine is predominant. In 2002 just 4.86 %

of all registered passenger cars in use in Sweden were diesel vehicles. As it can be seen in

the figure below, the share of diesel vehicles in new registrations is higher and in the EU this

share of newly registered cars increased enormously between 1990 and 2000.

18 Ministry of the Environment Sweden (2000)

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Figure 15: Diesel share of newly registered passenger cars [%]19

In this thesis, the four-stroke spark ignited engine is referred to as gasoline engine. As

already said, this engine is the most commonly used engine for passenger cars in Sweden. It

is still often stated in the literature that it is more effective to modify the conventional gasoline

engine instead of a total change of technology in order to obey the tightened emission

standards and fuel economy requirements.

Up to and during the 1970s no concerns about the environment or a lack of resources were

expressed and affected the design process of a car. But oil crises in the 1970s and 1980s

and an increased concern about air quality especially in urban areas set new requirements

for car manufactures. Emissions like NOx, CO and HC depend on engine operation. CO2,

seen as a contributor to the enhanced greenhouse effect, is directly related to the fuel

consumption of a car. It is not possible to remove the latter from the exhaust of a diesel or

gasoline fuelled automobile. The only way to reduce CO2 emissions is to reduce the fuel

consumed, or in other words to improve fuel economy.

The challenge for car manufacturers is to increase an engine’s efficiency in converting fuel

energy to useful work. Figure 16 illustrates to the right the work an engine has to fulfil in

order to move the car, but the most influence on fuel economy have engine and car

components as mentioned on the left.


Figure 16: Energy use in vehicles20

Nowadays, more optional equipment is installed and used, which leads to more weight and

consequently to more fuel consumed not to forget the energy demand for operation. On first

sight, one could assume that if car manufacturers will not find possibilities to offset this

increased weight, modern cars would get even heavier and would consume even more fuel.

But the car industry develops new technologies that also increase system efficiency and can

keep fuel consumption at least stable. Some of these technologies will be presented in the

next sections after a short introduction into engine principles.

3.1 Engine principles

As mentioned before, a diesel engine is less used in passenger cars but is dominantly used

in such applications as trucking and farming, because of the high efficiency, high torque

output and durability. Like the most common engine in passenger cars - the spark-ignition

engine -, is the diesel engine an internal combustion engine and works in many ways similar

to the spark-ignition engine. Even though the share of diesel cars is rather small in Sweden,

the list below contains the most fundamental differences, because at least on EU level a

noticeable increase of diesel passenger cars can be seen (see also Figure 15).

20 National Research Council (2002), p. 3-3

Engine

Accessories

Transmission

System Efficiency Road Load

Aerodynamic Drag

Rolling Resistance

Inertia (Weight)

Final Drive

Fuel consumption

Before fuel is injected in a diesel engine air is already drawn into a cylinder and

compressed, while fuel is injected to the air as it is drawn into a cylinder in a spark-

ignition engine.

- As a result of compressing air high temperature ignites the fuel-air mixture in a diesel

engine instead of a spark.

- A diesel engine controls the power output by varying the amount of fuel injected into

the air, thereby varying the fuel-air ratio. This is one of the main reasons why diesel

engines are more efficient than spark ignition engines that throttle the air.

- Another reason why a diesel engine is more efficient is that it runs lean – there is

always more air than is needed to burn the fuel. The fuel-air ratio in a spark-ignition

engine is fixed to stoichiometric conditions (perfect conversion).

For both engine types the compression ratio is an important factor, since higher compression

ratios lead to higher thermal efficiencies and better fuel economies. The compression ratio is

defined as the ratio of the cylinder volume at the beginning of the compression stroke to the

volume at the end of the compression stroke. Diesel engines need high compression ratios

for fuel autoignition, while spark-ignition engines use lower compression ratios (to avoid

knock, i.e. premature/self ignition). A well-mixed air-fuel mixture is essential for complete

combustion. Fuel that does not burn completely will contribute to hydrocarbon and particulate

emissions. Because a diesel engine runs lean it can come close to a complete mixing of all

fuel and air in the cylinder.

Short comparison of exhaust emissions:

Like fuel-air mixtures differ in diesel and gasoline engines, do the exhaust emissions differ.

Diesel exhaust tends to be high in NOx and particulates. But unlike the exhaust of spark-

ignition engines, diesel exhaust contains much less unburned or partially burned

hydrocarbons (HC) and carbon monoxide (CO).

With a richer fuel-air mixture (lambda < 1) the engine has maximum power per displacement

volume and available air supply, which gives good acceleration (design until the 1970s).

Moderate lean mixtures (1 < lambda < 1.5) offer good fuel economy but high emissions of

NOx and were used until 1980, especially at medium loads. Lean mixtures (lambda > 1.5)

give high efficiency and reduce the NOx emissions. This technique is utilised in lean-burn

engines at low and medium loads.

Because diesel engines have access to oxygen, CO emissions are generally low, and they

also emit low levels of HCs. But diesel exhaust contains more NOx. Particulates emissions

are a problem of the exhaust of diesel engines. High emissions of particulates are likely to

occur at higher engine speeds and loads, because the total amount of fuel injected increases

and the time available for combustion decreases.

3.2 CO2 reduction measures

There are certainly several strategies that can be applied to reduce the CO2 emissions from

transportation. The first option is to reduce the volume of transportation. A society can

achieve less traffic with integrated planning for a traffic minimising land use planning. Also

pricing policy can control the traffic demand. But it seems rather impossible to achieve

reduction in road transport worldwide, since nations like China are already on their way to

higher motorisation.

As second option, alternative fuels and new engine technologies might allow transportation

growth with less CO2 emissions. But this second option cannot solve the problem in the near

future. Such a substitute to gasoline and diesel would have to provide resources to

guarantee fuel security for today’s world fleet.

The most realistic and promising way might be to improve fuel economy, since CO2 is a

direct product of the combustion process. In order to reduce fuel consumption land-use

planning can help to reduce travel distance, but in the short run, the fuel economy of every

single car has to be improved.

3.2.1 Reduce transport energy use – technical measures

Technical measures are definitely the most favoured to reduce transport energy use. But this

strategy of technical measures is not as successful as needed. To effectively reduce fuel

consumption and associated greenhouse gas emissions only a mix of different measures can

be effective. Other measures, as listed in Table 2, are regulatory measures, pricing-policies,

infrastructure (planning) and organisational measures. Table 2 also summarises possible

options for each type of measure.

Measures Options

Technical Engines, fuels, infrastructure, telematics Regulatory Emission limits, recycling targets, speed limits Pricing-policy Internalisation of external costs, taxation Infrastructure Infrastructure development, spatial structure / land use Organisational Integrated policy making and planning, assessment

Table 2: Measures for influencing transport energy use21

21 following: OECD (1995)

Solving such a problem as enhanced greenhouse effect becomes complicated not at least

due to the different actors causing it. Figure 17 demonstrates the main actors causing such

an environmental problem.

The Government provides infrastructure, which makes road transportation possible, which

again is harmful to the environment and human health (accident risk). Car design

characteristics and the production processes itself contribute to both the environmental as

well as the safety problem. Consumers could demand highly fuel-efficient cars to reduce CO2

for example by the manner in which they make use of the car. But of course industry also

influences consumer demand through the scope of supplied alternatives, price, marketing,

and public relations activities.

Figure 17: Main actors in the causation of environmental problems and policy22

But this thesis cannot make the effort to find the right mix of measures to reduce the CO2

emissions from transportation to a level, which is considered not harmful. Because one

strategy to achieve reduced specific fuel consumption of the individual road vehicle is the

reduction of vehicle weight, this thesis makes the attempt to evaluate if this strategy conflicts

with road safety. But some technologies will be mentioned here in order to give examples for

what the industry has done so far to reduce fuel consumption and CO2 emissions.

As car manufacturers intend to sell cars, fuel economy can only be one aspect in the

production process. But not only the consumer dictates the importance or unimportance of

22 source: Pastowski, Gilbert (2003), p. 15

improved fuel economy among factors like engine power, handling and driveability or

comfort, the car industry has to keep itself alive. This includes that the product car itself and

its use has to be affordable for the majority of people. Since only restricted oil resources can

be used for transportation, improved fuel economy contributes that these resources last

longer. Therefore, car manufacturers have a natural interest to minimise the fuel

consumption of the produced cars. Especially the oil crises in the 1970s and 1980s reminded

the society of the restriction of oil resources, and in the aftermath new technologies where

incorporated. But besides minimised fuel consumption, the engine management system must

meet and compromise between several goals, such as: produce good driveability, maximise

the engine performance, and give low emissions.

Figure 18 shows how the chemical energy of fuel is used in the driving process. Only a

portion of about one fifth is used for driving operation.23 The figure shows main potentials to

improve fuel economy that would also result in reduced CO2 emissions.

Figure 18: Usage of fuel energy in the driving process24

Technical measures are not likely to conflict with safety, but a short excurse of technical

measures is included here to show on the one hand possibilities to improve fuel economy

without increasing the safety problem. On the other hand I want to stress that measures like

the following have to be evaluated from an environmental point of view in order to assure that

23 National Research Council (2002)

24 source: National Research Council (2002), p. 3-3

CO2 reduction measures will not increase other emissions. But such an evaluation is out of

the limits of this thesis.

Corresponding to Figure 18, a range of different technologies can help to improve fuel

economy of a road vehicle.25 The following list gives some examples:

- Improved aerodynamics;

- New (and lighter) materials;

- Improved engine design;

- Transmission design;

- Lubricants;

- Low rolling resistance tyres.

Using new (lighter) material is an often-applied measure not at least to keep the mass stable

after adding additional accessories; aluminium is an often used material in this context.

Engine design tries to reduce engine friction related to the piston and bearings, which

contribute about 45 % of the engine frictional losses. One possibility to reduce pumping

losses is variable valve timing. A second alternative is exhaust gas recirculation (EGR). EGR

– originally used in NOx control management – can provide the same benefit as variable

valve timing together with improved combustion.

The third alternative is the lean engine, such as the GDI engine. These engines have a

higher thermal efficiency. Under light load does the GDI engine run at air fuel ratios similar to

a diesel engine. But this advantage is also a problem, because a new catalyst type is needed

which maintains operation under these lean conditions. Transmission design also provides

improvement potential. A continuously changing gear ratio allows the engine to operate at its

maximum power point. Continuously variable transmission (CVT) is in development in order

to allow more rapid acceleration and a higher efficiency.

Maybe the most important factor in energy losses is friction. Frictional losses in the sliding

components of an engine can be minimised by new lubricants, which reduce viscous forces

particularly under low temperatures (under cold start conditions). Furthermore rolling

resistance of tyres is a factor contributing to energy losses. But measures lowering rolling

resistance are limited because the tyres of a car represent also a significant safety feature of

the vehicle. This last technical measure already shows that environmental and safety goals

are sometimes conflicting, and car manufacturers have to evaluate the consequences to

25 detailed information about new technologies and their effectiveness for example in National

Research Council (2002)

safety while for example designing new, more efficient car models in order to serve the CO2

reduction goals. Therefore, in the next paragraph measures improving fuel economy will be

discussed in relation to road safety.

3.2.2 Measures to improve fuel economy in safety context

As the technical measures presented are assumed to be neutral towards safety, this

paragraph presents, starting from the traffic injury volume model to describe the road safety

problem (Figure 10, page 16), strategies that could increase road safety on the one hand and

reduce fuel consumption on the other hand.

The dimension of exposure is probably the dimension with the highest potential to influence

safety both from a quantitative point of view and from a time point of view. The general

problem is to find out how the traffic volume can be reduced without losing mobility. It is

obvious that less traffic would result in reduced transport energy use.

The general problem in reducing the accident risk as the second dimension of the model in

Figure 10 is to find measures that will reduce the accident risk in hazardous situations such

as darkness, fog, ice etc. and for high-risk groups such as for young drivers. The injury risk

mainly depends on the transportation mode that is used. Unprotected road users have a

much higher injury risk than car occupants. Private transport has a much higher accident risk

than public transport. This means that from a safety point of view, as well as from an energy

point of view, much is to be gained by a transfer from private transport to public transport

(neglecting the injury risk when walking to and from the stations of public transport).

Reduction of vehicle weight seems to conflict with the dimension of consequence in Rumar’s

model. Vehicle weight and size are important variables describing the crashworthiness of a

car26, which is related to the third model dimension, the injury risk.

As a conclusion three strategies can be applied to improve safety and fuel economy

simultaneously:

- reduce (more hazardous modes of) travel and transport (substitution of physical

communication, promoting low-risk modes, integrated urban planning),

- crash avoidance (maintenance, improvements of road environment, traffic

management for smooth traffic with less variances in speed), and

- behaviour modification (speed limits, eco-driving).

26 See also literature review in the next chapter.

But not only safety and fuel economy would benefit from these actions, the saved energy

would help to reach CO2 reduction goals and enhance national fuel security. Evaluating

whether CO2 reduction measures could have a negative side-effect on other emissions is not

part of this thesis.

Chapter 4 Side-effects from improved fuel economy

The last paragraph could show, that there are several strategies serving safety and

environmental goals. Road traffic causes several environmental problems and this thesis

focuses on CO2 emissions. The mainly technical measures incorporated to improve fuel

economy have to be evaluated according to possible environmental and safety side-effects

during the design process. One of the most direct ways available to improve vehicle fuel

economy and by that possibly reduce CO2 emissions is to reduce a vehicle’s weight.

Therefore, I want to focus on side-effects from improved fuel economy on safety. A literature

review based on scientific publications shows how scientists define the weight-safety

relationship.

Literature Review

In literature several approaches are used to describe the relationship between vehicle weight

and safety. In general, one can distinguish between three approaches: a statistical view, a

mechanical view with in-depth accident analyses and crash tests and computer simulation. In

several scientific articles and conference proceedings the attempt was made to quantify the

safety-weight relationship, but the definition of what this relationship describes differs. As an

example, a general problem is to distinguish between weight effects on safety and size

effects. Often weight and size are treated interchangeably, and only the weight variable has

been used in calculations. By that a researcher may assign a positive size effect to weight,

which may have no influence at all.

Another aspect is the definition of safety. While it should be understood from a societal point

of view, it is often discussed from the individual point of view only. This means that instead of

safety, only crashworthiness is analysed and for example the compatibility aspect remains

not discussed.

Wood (1997) discusses safety in such an individual sense. As it is expressed there “this

paper examines the influence of car size and structural crush behaviour on safety of car

occupants in frontal collisions”27. Wood presents fundamentals of car size effects on safety.

27 Wood (1997), p. 139

As acceleration is directly correlated to injury severity, Wood stated that size or length effect

is dominating in theory. But nonetheless, he uses weight to replace size. This use of weight

to describe the effect of size seems problematic. Wood sacrifices validity of his results not

necessarily quality, because he concludes that mass ratio and the absorbing properties of

two cars in a frontal crash are the two fundamental parameters influencing relative injury

severity. But he justifies the replacement with correlations found between mass and overall

length of the car population of the ending 1980s in Great Britain and the USA28.

When it comes to crashes between vehicles with similar masses, Wood sees the risk related

to collision speeds and to the deformation of the cars. Furthermore, collision speed

distribution is considered to be independent of car size, which results in dynamic crush

displacements as the only factor for the relative injury risk. Wood finishes with some

suggestions to improve the safety of small cars. In a 2002 published study Wood et al

emphasise the dominant size effect on safety.

Neither Wood (1997) nor Wood et al (2002) analyse accident data, but results of model

calculations and Monte-Carlo simulation are compared with several field studies. Since I

attempt to use Swedish accident data to reveal effects weight reduction can have on road

safety, primarily literature representing the statistical approach are discussed in the following.

Using statistical methods involves two main problems. The first one is the difficulty of

isolating weight from other confounding variables. The second problem is to choose

exposure for risk calculations. Of course, the availability of accident or crash test data is a

crucial fact for every study of statistical nature.

Evans and Frick (1993) used data of two-car crashes from the Fatal Accident Reporting

System (FARS). A relative risk of a driver fatality in the lighter in respect to the risk in the

heavier car is determined as a function of the mass ratio. The researchers base their

analysis in Newtonian mechanics and by using ratios they avoid the difficulty of finding

adequate exposure data, but unfortunately the size factor is excluded. But on the other hand

attempts are made to determine the influence of safety belt use, model year, absolute mass

of the involved cars, impact modus (e.g. front or rear end impact) and driver factors as well.

Evans and Frick conclude that mass is the dominant factor on relative driver fatality risk

when two vehicles of different mass crash into each other. In 1992 Evans and Frick

investigated whether car mass or car size is the causative factor for injury risk. They

analysed crashes between cars of similar wheelbase and different mass and crashes

between cars of similar mass and different wheelbase and revealed the mass as the

28 Evans (1994), Kahane (1991)

dominant factor. However, in my opinion car design especially of smaller and lighter cars

changed, so that one can doubt how valid these results for today’s car population are.

Evans and Wasielewski (1987) analysed already the relative likelihood of driver fatality in a

car of certain mass involved in a crash with a car of a different or same mass. Table 3 below

presents the results of their work, which led to the following conclusions:

- The lighter the vehicle, the less risk to other road users.

- The heavier the vehicle, the less risk to its occupants.

Evans (1991) evaluates the net number of fatalities and concludes that substituting a lighter

car with a heavier car nearly always reduces the system-wide harm from two-car crashes.

Mass category car j Mass (kg) car i m1 m2 m3 m4 m5 m6 500-900 m1 7.04 12.12 15.15 16.05 16.86 16.51 900-1100 m2 5.06 9.78 11.88 13.38 14.58 14.68 1100-1300 m3 3.50 5.33 7.79 9.48 9.30 9.36 1300-1500 m4 2.14 2.67 4.83 6.06 6.94 7.12 1500-1800 m5 0.98 2.04 2.57 3.56 4.34 5.01

Table 3: Relative likelihood of driver fatality in a car of mass mi involved in a crash with a car of

mass mj29

An early and widespread model of the relationship between fuel efficiency and fatalities goes

back to Crandall and Graham (1989). The model estimates that a 14 % reduction in vehicle

average weight during the sample period (1970-1985) has resulted in 14 % to 28 % increase

in single-vehicle highway fatalities in the USA. But this model also did not include any

measure of car size and only weight is used as explanatory variable of fatalities.

Green et al (1993) present a cost benefit analysis of automobile fuel economy improvement

and evaluate safety impacts and emissions of criteria pollutants as less important. But

uncertainties of several costs and benefits let me question this evaluation. Anyhow, this

analysis contains a list of so many areas that are affected by fuel economy improvements,

which makes this paper an important basis for any discussion about how far fuel economy

improvements should go. An analysis of market sales revealed shifts among vehicle classes,

but 96 % of weight reduction in cars between 1975-82 is attributable to reductions within the

same size class. Green et al (1993) see this as evidence for that weight reduction has

probably little to do with an increase in fatalities.

Broughton (1995) found out that the injury frequency varied more between various mass

classes. He agrees here with Green that downsizing consequences for traffic safety may not

29 source: Evans, Wasielewski (1987)

be as significant as other factors, like vehicle protection or driver behaviour. Broughton’s

analysis compares injury ratios (e.g. killed driver per injury accident) instead of using

exposure data like travelled km on certain roads of certain vehicle classes. This fact can

additionally bias Broughton’s results.

Mizuno et al (1996) investigate Japanese conditions. Their work is an in-depth accident

analysis, actual crash partners are analysed in head-on collisions. Their results confirm the

already by Evans formulated “law” that a lighter vehicle means less risk to other road users,

but heavier vehicle can protect the own occupants better. Side-collisions are additionally

studied with the help of simulations. They conclude that in side-collisions the mass of the

striking vehicle is important, but the mass of the struck vehicle is rather unimportant.

Hertz (1997) studies the effect of changes in vehicles size on fatal and incapacitating injuries.

Changes in vehicles size are defined as a reduction of one hundred pounds in vehicle

weight. The effect is examined as change in crash rates of incapacitating driver injuries.

Crash data from Florida and Illinois are used. Collisions with fixed objects, with heavy trucks,

and with other passenger cars are included. As potential confounding variables only driver

age and accident site in terms of urban or rural (as a surrogate measure for crash speed) are

chosen. As statistical method logistic regression is applied. As result it is presented that a

reduction of vehicle weight results in an increase in driver incapacitating injury rates for the

different analysed accident types. But Hertz’ conclusion seems less convincing, since weight

and size are used interchangeably and only driver age and accident cite are included as

potential confounding variables.

Buzeman et al (1998) discuss compatibility of cars in frontal crashes and mass

incompatibility is one aspect of their investigation. A new mathematical model is presented to

estimate average injury and fatality rates in frontal car-to-car crashes for changes in vehicle

mass, impact speed distribution, and inherent vehicle protection. Several safety strategies

were evaluated including downsizing, impact speed and inherent vehicle protection, to

address the discussion of vehicle downsizing in Europe. The mass factor showed not as

strong effects as expected. Buzeman et al showed that the mass range is more important

than the weight of the single vehicle. But since there are more vehicles in the upper mass

classes, it is easier to reduce the number of lighter ones to get a smaller and safer mass

range. Also in Buzeman-Jewkes et al (1999) it is deduced that it is possible to maintain

vehicle safety while downsizing. Especially speed is mentioned as a more important factor

than the mass of a car. But it is added that removing heavier cars, to reduce the range of

weight in vehicle fleet, would affect the economy of a nation, because these cars are often

used for business.

Khazzoom (1994) limits the investigation to single vehicle highway fatalities, but included

both weight and size as explanatory variables. He found that car size does enhance safety,

but could find that weight has the same effect. Downweighing without downsizing is

according to Khazzoom not likely to reduce road safety (at least in single vehicle accidents).

Kahane (1997, 2003) involves besides crashworthiness also crash involvement and

addresses highway accidents among all types of highway users. But also Kahane was not

able to separate weight from size and the first study contains lots of assumptions that seem

not always plausible. These uncertainties were also reason for a second study. The author

gave three reasons for revising his study, which was seen as the most comprehensive one

so far30:

“The most important reason for a new study is to take a good, hard second look at the

methods of the 1997 report and to revise or supersede them with techniques that more

accurately fit the data. Another reason for a new study is that the vehicle, crash and driver

environment has changed in six years. […] The third motivation is to expand the analyses.

The 1997 report estimated two numbers: the effect of a 100-pound reduction in LTVs of any

weight, and in passenger cars. The new study separately estimates the effects of 100-pound

reductions in heavy LTVs, light LTVs, heavy cars and light cars.”31

Kahane (2003) compares crash fatality rates per billion vehicle miles for different road users

during the calendar years 1995-2000. Basis for this analysis is FARS data, he spends a huge

effort on determining the explanatory variable. He adjusted the fatality rates for driver age

and gender, urban/rural and others.

While Kahane found in his first study 1997 that vehicle weight reductions do not increase

fatality risk in car-to-car or LTV-to-LTV crashes and even reduce the fatality risk in pedestrian

crashes, he concluded in 2003 that fatality rates of lighter LTV, heavier cars, and lighter cars

increased as weights decreased. Pickup trucks and SUVs had, on the average, higher

fatality rates than passenger cars of comparable weight. His results confirm that the size-

safety effect is not uniform across all weights, other factors have to have an influence as

well.

30 National Research Council (2002), Appendix A

31 Kahane (2003), pp. 1-2

Chapter 5 Conclusion of PART I

Both, road safety and the quantity of CO2 emissions are important topics in Sweden’s policy.

For both were societal goals defined, but on the way to achieve these goals they could

conflict with each other. Because of this an analysis of the relation of road safety to weight is

needed.

A literature review by the National Research Council's (1992) of safety literature found that

the total number of injuries in a two-car collision could increase, decrease, or remain

unchanged when both cars are made lighter.32 Like the National Research Council, for two-

car collisions the literature review presented in the last chapter could not find out how

reduced weight will affect road safety. Researchers often limit their work to a certain crash

type and/or vehicle type, because differences between different types seem too big. A

common problem is also the interchangeable use of weight and size. Of course statistical

methods depend on the available data. The structure of these data differs from country to

country. The choice of explanatory variables differ largely, it seems to be unclear which

variables have a confounding effect on the weight-safety relationship. So many differences

make it difficult to compare the findings of different statistical studies, which could help to

answer the question if we have to sacrifice road safety when the vehicle fleet is

downweighed in order to reduce fuel consumption and by that also CO2 emissions.

What can be learned from the qualitative analysis of the problem is that it is important to

distinguish two different points of views of road safety. The individual safety refers to the

safety of the driver and occupants of a single car. In contrast to this individual safety, net

safety describes the safety level of the entire vehicle fleet. Especially since the “Vision Zero”

was introduced, net safety has to be investigated in order to determine a mix of measures,

which help to achieve safety and environmental goals simultaneously.

From the different approaches to investigate safety-weight relationship the statistical one,

accompanied by in-depth accident studies and completed with before-after studies of certain

car models, seems to be the appropriate one to analyse the relation of road safety and

vehicle weight. Crash tests, as objects of analysis, do not consider vehicle safety in terms of

net safety. In paragraph 3.2.2 different strategies were presented that help to achieve CO2

reduction goals without conflicting with safety or even improve road safety. These measures

concerning technologies applied to the single car, integrated planning and transport policy

were not evaluated here and do not stand in focus of this thesis.

32 National Research Council (1992), pp. 6, 51, 57 and Appendix D

PART II QUANTITATIVE ANALYSIS – WEIGHTPROBLEM

Part I discussed several studies that addressed the weight-safety relationship. As the

findings were discussed it became obvious that this relationship is still not fully understood

and very complex (Chapter 4, Chapter 5). Furthermore, only a single investigation

concerning the Swedish car fleet was found (Buzeman et al, 1998), but that work is far from

being as complete as Kahane’s investigations, which are seen as the most comprehensive

so far.33

Part II of the thesis investigates, if car weight has an influence on injury severity. Historical

accident data is reviewed in order to reveal the relationship of size and weight of a vehicle to

driver injury severity. Was and is it possible to gain further fuel economy improvements by

reducing vehicle weight without sacrificing safety? In order to answer this question the

analysis will be performed in several steps according to the formulated hypotheses in

Chapter 6.

The first step is to analyse single vehicle accidents, this should reveal how cars of different

weight classes can protect their drivers. The single vehicle accident analysis is followed by

an investigation of two-vehicle frontal accidents, where vehicles of the same weight class

collided. To evaluate safety from a societal point of view as well, frontal two-car accidents of

vehicles of different weight classes will be analysed. And finally the question should be

answered if a reduction of vehicle weight would conflict with road safety. To give a quantified

answer regression analysis will be applied. The analysis has to be controlled for confounding

variables like driver age, year model, road class (width, speed limit), and others. A discussion

about what the most influence has on better protection in a larger car (weight or size)

completes this section.

To eliminate changes of driver attitude over time (speeding, alcohol and driving, seat belt

usage) accident data from a short time period is used. The data includes all police reported

accidents occurred in Sweden in the year 1999.

Chapter 6 Hypotheses

From the view of individual safety, Newtonian’s physics, and following the conclusion of the

majority of literature the hypothesis has to be that a large car offers better protection to its

occupants than a small car in any accident mode. This would mean that weight reductions

would reduce the safety on Swedish roads. Often one can read in scientific literature that

33 National Research Council (2002), Appendix A

Newton dictates that if two cars collide (frontal) occupants in the heavier car are better

protected (see e.g. Evans). The lighter car will experience a far greater impact energy and its

occupants a higher acceleration (if the same restraint devices are used). Experiencing higher

accelerations results in higher injury severity. But injury severity cannot be described by

basic physics (Newton’s law of motion) alone. Newtonian physics have to be coupled with

the crashworthiness of the vehicles involved and usage of seat belts and other passive

protection. However, following the results of the literature review given in Part I, the following

hypotheses are formulated:

1. in order to reduce CO2 emissions the Swedish passenger car fleet changed in mass

distribution, it was downweighed.

The process of downweighing has had an influence on road safety, so the second

hypothesis is:

2. downweighing of the car fleet in a country will increase its safety problem – the

outcome of road traffic accidents will get more severe.

The second hypothesis will be subdivided. According to the steps of analysis the

following hypotheses will be tested:

a. heavier passenger cars provide better protection in single vehicle accidents

b. in a collision with a car of approximately the same weight heavier passenger

cars provide better protection than lighter ones in a collision with a car of

approximately the same weight

c. in a collision with a car of a different weight class (heavier or lighter) is the

driver of the heavier car better protected than the one of the lighter car

d. the introduction of lighter passenger cars resulted in an increased safety

problem on Swedish roads and counteracted “Vision Zero”

Van den Brink et al raised the question why fuel economy did not improve in the Netherlands

and found that passenger cars got heavier over the years. This leads to the question whether

the first hypothesis already has to be rejected. Figure 8 (page 13) shows the European trend

of heavier cars. Nonetheless, it is true that new high efficient cars came into the market,

which were lighter than the average. In that sense there is a downweighing trend, but not the

whole population of passenger cars is affected. The downweighing of a certain share of cars

makes the mass range broader and can decrease road safety.

Chapter 7 Data material

The number of accidents and its characteristics is retrieved from the register of police-

reported accidents. Also, information about involved vehicles, driver injuries and road

environment is obtained from this source provided by the Swedish National Road

Administration (Vägverket).

The accident data is organised in separate databases that can be linked using a unique

accident number and the given number of each vehicle, animal or pedestrian involved in a

certain accident. The databases can be grouped according to the information they contain

into five different database groups. The first, represented by the basic accident database and

a database containing the course of events, describes the accident with 54 parameters in

olycka.dat, including for example information on accident type, width of road, speed limit,

traffic volume, and accident. The databases of the second group contain information about

the involved vehicles and trailers; some examples of the 42 parameters presented in the

vehicle database are service weight, year model, cylinder volume, vehicles type, and

dimensions of the vehicle. The third group describes the involved persons. The group of

vehicle drivers, which this thesis will focus on, is described by 19 variables. In the

corresponding database detailed information on driver age, driving license information or a

driver’s injury level is included. The police define personal injuries at the accident site

according to a scale including: fatal injury, severe injury, minor injury, and no injury.

Information included in the databases of the fourth and the fifth group describe the location of

an accident, unprotected road users involved in a road accident as well as animal accidents.

1. Accident information

2. Vehicle information

3. Driver and passenger information

4. Location 5. Other information

olycka.dat motorf.dat foerare.dat y_oly.dat gaaende.dat fordonsh.dat slaep.dat passager.dat z_oly.dat djur.dat oevrigf.dat Vben.dat cy_moped.dat Fordonsu.dat

Table 4: Main accident databases

The original accident database consists of all police reported accidents in Sweden during the

years 1994 to 1999. The basic accident database consists of 444,596 entries for these years;

596,368 motor vehicles are registered to be involved in these accidents. The driver database

consists of 549,469 drivers involved in accidents of the years from 1994 to 1999.

The accident database for the year of interest 1999 contains 68,887 records. For the same

period 86,134 drivers and 88,249 vehicles are registered, which were involved in those

accidents. The database contains several different crash types, but single vehicle accidents

and frontal collision accidents are dominating, as the accidents get more severe.34 Therefore,

out of the database the population of “case” accidents is selected. “Case” accidents are all

police reported single vehicle and frontal collision accidents in 1999 where only passenger

cars, trucks, light trucks, buses, and cross-country cars were involved. This definition of

“case” accidents leaves 11,635 accident, 13,790 driver and 13,614 vehicle records. Of these

“case” accidents 9,685 are single vehicle accidents and 1,950 are frontal collisions.

Out of the information given, the following data was chosen describing accident, vehicle and

driver:

crash characteristics

ID # elements veh./24h heavy veh./24h on a bridge?

# fatalities speed limit community # minor inj. light conditions

time accident site reg. date week day accident date

accident type # severe inj. rural/urban? in a tunnel? weather

construction? road type road width province maintenance class

acc. severity month year winter class

driver characteristics

ID element ID age license date license class

gender confiscated license injury level tractor license exch. foreign license

summary fined taxi license zip seating position instructor

approved instr. 1rst license class age of license road user category

vehicle characteristics

ID element ID year model # persons # trailers

vehicle width max length max weight car body code vehicle length

engine power stolen service weight total weight road user category

cylinder volume driver air bag

This selection of data does not represent the chosen set of variables. The set of variables

used as explanatory variables will be determined in a pre-study (Chapter 9).

34 Johansson (2001)

Additionally to the described accident data set a data source is necessary providing

exposure data. The most common and suitable exposure is annual vehicle kilometre

travelled. It is important that not the exposure data itself is confounded with curb weight to

avoid a biased result.

One can think of three possible sources to obtain vehicle kilometres travelled annually as

exposure data:

- aggregate estimates based on fuel consumption and road counts,

- detailed road counts on a sample of roads and

- information from each vehicle’s road distance meter from annual inspections conducted

by the Swedish Motor Vehicle Inspection Company (Bilprovningen).

The most suitable source for the purpose of this thesis is data from the inspections, since it

can be classified by vehicle weight. Unfortunately, data obtained from mandatory

roadworthiness inspections conducted by the Swedish Motor Vehicle Inspection Company

were not available. But according to the Swedish National Road Administration, annual

vehicle kilometres are estimated as function of vehicle age (Figure 19). Unfortunately, no

statement is made to what extent these distances also depend on driver age, gender or

vehicle weight.

0

2500

5000

7500

10000

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17500

20000

22500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20vehicle age in years

annu

al tr

avel

led

vehi

cle

km

Figure 19: Annual travelled vehicle kilometres as function of vehicle age35

Furthermore, the distance estimations do not provide the possibility to identify road type or

geographical area where the vehicle was driven. Another data limitation is that no information

35 according to the EMV-model version 2.0 taken from Johansson (2002)

is given about the specific injury or the point of impact in the provided data material.

Otherwise, side protection levels and individual types of injuries could be studied. The point

of impact is assumed to affect injuries to drivers and occupants significantly.

The vehicles involved in the analyses presented here are vehicles of the classes passenger

cars and light trucks; heavy vehicles and buses had to be excluded most importantly due to

the missing exposure data.

Chapter 8 Methodology

Politicians spend great efforts on reducing especially fatal and serious injuries. But

nonetheless, minor injuries contain some injuries that generate long term health losses. This

is especially relevant to neck injuries (so called whiplash) in rear end and frontal collisions,

that traditionally are defined as minor injuries. Therefore, the data is often analysed in two

groups: one containing only severe and fatal injuries; and the other containing fatal, severe

and minor injuries together. The analysis should, as much as possible, remove confounding

factors and compare the injury rates of passenger vehicles of different weight adjusted for

such confounding variables. In order to detect factors that are related to mass or injury

severity a pre-study will be performed, it includes animal and pedestrian accidents.

The main part includes single vehicle accidents and frontal collisions. Before regression

analyses are performed, a set of simple graphs, including total number of injuries,

proportions of the different injury levels and fatality rates by vehicle weight, should help to

reveal basic trends in the data. These simple graphs help to get an idea of what the

regression coefficients ought to be. Analyses of single vehicle accidents and frontal collisions

of vehicles comparable in weight should help to describe individual safety in relation to

vehicle weight. To answer also the question about net safety frontal collisions of vehicles of

different weight will follow. The goal of the last step (regression analysis) is to determine

injury rates as a function of vehicle weight. The exposure is always measured in annually

travelled vehicle kilometres.36

Naturally, vehicle size and weight are related, but to isolate the size from the weight effect is

a very complex problem. The literature reviewed showed, that a method to integrate both

size and weight in such an analysis is not documented yet. I will attempt to address this

problem by using both weight and size as explanatory variables in regression analysis in

order to evaluate which variable affects safety the most.

36 The annually travelled vehicle kilometres are always an estimate according to the relation presented

in Figure 19.

Some important definitions

Outcome of an accident can be described as severity of occupant injury. Here, only driver

injury severity is taken into account. To include passenger injuries as well seems not

suitable, since a set of data with a comparable number and seating position of car

passengers would be too small for a statistical analysis.

Crash severity, which is the predominant factor influencing driver injuries, is in the scientific

world expressed as velocity change (deltaV). This measure is not available in the provided

data. But it helps to understand why an investigation of vehicle weight is of such an interest,

because the velocity change of a lighter object will always exceed that of a heavier one in

proportion to the relative weight, when these objects collide. The higher velocity change can

result in more severe injuries for vehicle drivers. The question is if vehicle design could

reduce these deceleration forces in order to outweigh the disadvantages a lighter vehicle

might has.

A vehicle’s weight is to be understood as service weight throughout this thesis. Service

weight describes the actual weight of the vehicle with a full tank of fuel and other fluids

needed for travel, but no occupants or cargo.

It is important to distinguish two different points of views on road safety. Individual safety

refers to the safety of the occupants of a specific car. The second definition of safety used is

net safety as the safety level of the entire car fleet. Current crash testing does not consider

vehicle safety in terms of the occupants of an opposing vehicle; therefore only investigations

of real world accidents can give information about both individual and net safety.

But of course it is not enough to analyse multi-vehicle (here two-vehicle) accidents, because

heavy vehicles are also able to knock down, displace or brush aside fixed objects that lighter

vehicles could not. Often also doors, and pillars etc. are thicker and stronger in heavier

vehicles and they provide more room to be deformed than lighter vehicles. Therefore, an

analysis of single vehicle accidents is included.

Chapter 9 Pre-study

In order to reveal variables that show a connection to either the dependent variables fatality

or injury risk per vehicle km driven or the key independent variable vehicle weight, pedestrian

accidents and accidents with animals observed in 1999 are analysed.

First results are presented in Table 5, as this table contains correlation factors for linear

correlation of potential control variables and vehicle weight. The analysis is performed for

passenger cars and a group of light trucks, heavy goods vehicles and busses separately.37

Also, animal and pedestrian accidents are analysed separately. Nevertheless, both analyses

follow the same procedure.

The group of drivers involved in a pedestrian accident or in an accident with animals are

subdivided into 28 class intervals of service vehicle weight. Each interval is bounded at the

top by the following percentiles of service vehicle weight: the 1st, 2nd, 4th, 6th, 8th, 10th,

15th, 20th, 25th, 30th, 35th, 40th, 45th, 50th, 55th, 60th, 65th, 70th, 75th, 80th, 85th, 90th,

92nd, 94th, 96th, 98th, 99th, and maximum weight.

Since service vehicle weight spreads more at the low and high percentiles and these

percentiles are especially important in computing correlation coefficients the class intervals at

the ends are chosen to contain fewer percentiles than in the middle. In each of these 28

groups, the weighted (by vehicle kilometres travelled annually) average is computed for

service vehicle weight and each of the potential control variables. The values for the group of

“heavy vehicles” could not be weighted since no information about annual distances were

provided or could be estimated. The average values are linear, continuous variables, even if

the original potential control variable can only have two different values (e.g. driver gender).

The product-moment correlation r of service vehicle weight with each of the potential control

variables can be computed across the 28 class intervals and tested for significance.

9.1 Data in the pre-study

Aim of the pre-study is to reveal confounding variables of a safety-weight relationship.

Included are animal and pedestrian accidents. Figure 20 shows the weight distribution of all

vehicles involved in these accident types.

Even though trucks and buses should take part in this and the analyses documented in the

next sections, these vehicle types have to be excluded. First of all, the annually travelled

distance could not be estimated and as it will be shown in this chapter control variables

cannot be defined using such a small amount of case accidents within these vehicle classes.

37 Vehicle types were coded in the accident data.

Figure 20: Vehicles’ weight in pedestrian and animal accidents happened in 1999 – left:

passenger cars; right: heavy vehicles

Figure 21: Weight of passenger cars involved in animal and pedestrian accidents 1999 (left)

and vehicle weight of registered passenger cars in Sweden (right)

Figure 21 shows that passenger cars involved in an accident of the chosen accident types

represent Sweden’s passenger car fleet, because the weight distributions presented to the

left and to the right are very similar. Therefore, studying properties of these accidents can

provide a selection of control variables for a safety-weight analysis. The figures below

contain a comparison of weight and age distribution of vehicles involved in animal or

pedestrian accidents 1999 and the registered passenger cars for the year 199938.

Figure 22: Age distribution of passenger cars involved in animal or pedestrian accidents and

age distribution of registered passenger cars in Sweden [age represented by year model]

The following potential control variables are included in the data set created for animal and

pedestrian accidents happened on Swedish roads in 1999:

driver age vehicle age (year model) male/female driver

speed limit rural/urban driver air bag

Driver air bag availability has to be dropped from the list immediately, since for only 3.19 %

of all animal and pedestrian accidents happened in 1999 is information about air bag

availability given (Figure 23).

38 registered vehicle 01/01 2000, source of data: BIL Sweden (2000)

Figure 23: Air bag information (animal and pedestrian accidents, 1999)

9.2 Potential control variables

Heavier cars have historically longer wheelbases, which can make them less crash prone,

their structures are often stronger, which makes them physically safer in case of an accident.

This means, that a vehicle’s outer dimensions should not be ignored in a safety-weight

analysis.

Figure 24: Footprint of passenger cars in animal and pedestrian accidents by weight

Figure 24 illustrates the relation of a vehicle’s outer dimensions and its weight. Vehicle length

will be taken into account in the regression analysis. This dimension is assumed to affect

injury severity the most of both size variables, length and width. A longer vehicle often

provides a longer crush zone, which results in lower injury severities.

The measure of change in velocity, which is the predominant factor of crash severity, is not

available in the accident data. But assumed that on roads with higher speed limits accidents

get more severe, the speed limit of the road is chosen as a potential control variable. The

same reason has to be given, why to include where the accident happened: on a rural or an

urban road. Another vehicle factor that influences a driver’s injury level is vehicle age. Safety

equipment is less common in older car models, but also material can get tired and a car’s

structure can lose its ability to absorb energy in case of an accident. Therefore, older cars

might offer their occupants less protection than newer models.

Some human factors of drivers can also be confounded with vehicle weight and have to be

chosen as control variables. Drivers’ age and gender are included in order to control for

human factors. Young and inexperienced drivers but also old drivers are known to have

higher fatality and injury rates than middle-aged drivers. Especially female middle-age drivers

are known as safe drivers. But of course, it can also be that these safe driver groups pick

especially safe cars. Since additional safety equipment is introduced in more expensive cars

first, younger drivers can often not afford such safer cars. The higher vulnerability of older

drivers, which often pick bigger cars, can increase fatality and injury rates for heavy cars

compared to lighter cars. But to control for such effects seems impossible, so drivers’ age

and gender have to represent a broad range of human factors that are confounded with

vehicle weight.

9.3 Correlation analyses

Animal accidents are more likely to happen in rural areas. In order to analyse observations

that can represent the Swedish vehicle fleet as a whole, pedestrian accidents are included in

this pre-study. The analyses of both accident types follow the same methodology. But I want

to stress that this accident type is not as independent from driver skills as animal accidents.

The results of the (linear) correlation analysis39 retrieved from animal and pedestrian

accidents are presented in Table 5. Figure 25 and Figure 26 illustrate correlation analyses for

the group of passenger cars. These figures contain polynomials that fit the data, taken from

the accident files, in a least-squares sense. In contrary to the results presented in the table

below, for driver age, gender and the variable rural/urban quadratic correlation is presented.

39 made use of the corrcoef function in MATLAB

animal accidents pedestrian accidents

variables unit r p r p

driver age years 0.3616 0.0587 -0.4792 0.0099

vehicle age years -0.9067 0.0000 -0.8489 0.0000

driver gender 1:male/2:female -0.9259 0.0000 -0.7474 0.0000

speed limit km/h 0.6478 0.0002 -0.3860 0.0425

urban/rural 1:urban/2:rural -0.0249 0.9000 -0.4171 0.0272

injury level (1-4) 0.4188 0.0265 0.1648 0.4022

passenger car group

injury level (1-3) -0.1919 0.3280 -0.4275 0.0233

driver age years 0.4419 0.0186 0.3939 0.0381

vehicle age years -0.2328 0.2333 -0.0660 0.7388

driver gender 1:male/2:female -0.3622 0.0582 0.1036 0.5998

speed limit km/h 0.8327 0.0000 0.2542 0.1918

rural/urban 1:urban/2:rural 0.6566 0.0001 0.1267 0.5205

injury level (1-4) 0.2188 0.2633 0.3024 0.1178

truck group

injury level (1-3) NaN NaN -0.0240 0.9035

Table 5: Correlation factors for animal and pedestrian accidents – linear correlation

The correlation coefficients of two different injury level variables are given as well in the

table. The first variable contains all levels, from killed to not injured drivers, while the second

variable excluded drivers who were not injured. These coefficients might give a first glance at

the safety-weight relationship. But as it can be seen in Figure 25 and Figure 26 for the first

injury variable, the classification according to the vehicle weight results in average values of

injury levels, which cannot be used to retrieve any information about the safety-weight

relationship.

Figure 25: Animal accidents

The group of “heavy vehicles” contains very few case vehicles, therefore only the results of

passenger cars will be discussed and be presented in the figures. Trucks have to be

excluded from further analyses because no information about annually travelled distances

could be found and also the correlation analysis with simple mean values cannot reveal any

ideas of variables, which has to be chosen as explanatory variables.

Vehicle age, driver gender, and speed limit all have a statistically significant (p < .05)

correlation with service vehicle weight for the group of passenger cars in animal and

pedestrian accidents. Driver age and the variable urban/rural show only in one accident type

such a significant linear correlation.

In animal accidents driver age and speed limit have positive correlation, which means that

heavier passenger cars have relatively older drivers, and are more used on high-speed

roads. But at least for passenger cars the heaviest models seem to be driven by younger

drivers (Figure 25).

The variables vehicle age and driver gender have shown negative correlations. This reflects

that heavier passenger cars tend to have also more male drivers and are of more recent year

model then light passenger cars. These trends can be seen in both accident types. The

driver age has shown for animal accidents a positive correlation in animal accidents but for

pedestrian accidents a negative correlation.

Figure 26: Pedestrian accidents

As already seen in Table 5, no statistically significant (p < .05) correlation can be found for

the variable urban/rural. This result is not surprising since animal accidents are known to be

more likely to happen in rural areas and pedestrian accidents are more likely to happen in

urban areas. Thus, different trends for speed limit in pedestrian accidents and in animal

accidents are found.

9.4 Result of the pre-study

The analyses of the weight-safety relationship should be at least controlled for the following

variables:

driver age driver gender vehicle age (YM) speed limit size

In case of two-car accidents, the variables size (length) and weight of the other car should be

added to the mentioned list of control variables. The group of “heavy duty vehicles” produced

just a small number of observations. The vehicles of the group “heavy duty vehicle” will not

take part in the further analyses.

Chapter 10 Results of the quantitative analyses

10.1 Single car accidents

Although a group of control variables was chosen in the last paragraph, the first graphs

represent trends of uncontrolled analysis of driver injuries in single vehicle accidents. To start

with, the proportion of the four different injury levels – killed, severe injuries, minor injuries,

no injuries – were plotted over vehicle weight. A simple linear trend in addition illustrates the

first results in Figure 27. The proportions of severe and fatal injuries remain rather

unchanged with increasing vehicle weight. But as the proportion of minor injuries decreases,

the proportion of no injuries increases. A view on KSI accidents in Figure 28 shows a

different aspect. The absolute number of KSI diminishes with increasing weight, and even

more the rate of KSI per annual travel distance of heavier cars is reduced in contrast to the

rate of lighter cars. The annual travel distance was estimated according to the age-mileage

relationship presented in Chapter 7 and given by the Swedish National Road Administration.

0%

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50%

60%

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90%

846

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2076

average weight of each weight class

driv

er in

jury

leve

l

killed severe injuries minor injuries no injuries

trend (killed) trend (severe injuries) trend (minor injuries) trend (no injuries)

Figure 27: Driver injury level as function of average weight – single car accidents

0

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KS

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I (ab

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KSI

KSI/1,000,000 Vkm

trend (KSI/1,000,000 Vkm)

Figure 28: KSI (absolute) and KSI rate (per 1,000,000 vkm) – single car accidents

The classification in 20 weight classes (which is comparable to the 28 weight classes defined

in the pre-study but with fewer intervals) will be replaced by a classification in three vehicle

types: the light, medium, and heavy passenger car. The group of light cars includes the 20 %

lightest cars involved in single vehicle accidents on Swedish roads in 1999. The group of

heavy cars represents the 20 % heaviest cars in these accidents. And the class of medium

cars includes cars that are heavier than 40 % but also lighter than 40 % of all cars involved in

single vehicle accidents40. As before, I want to start with a comparison of injury level given in

proportions (Figure 29). The proportion of accidents with KSI is highest for the light cars and

lowest for the group of heavy cars. Furthermore, 54 % of the drivers in light cars remain

uninjured after single vehicle accidents, while this is the case for 63.8 % of the drivers in

heavy cars. This picture is confirmed by a comparison of injury rates for these three types of

vehicles.

40 light car: < 1,080 kg; medium car: > 1,240 kg and < 1,370 kg; heavy car: > 1,480 kg

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

light car medium car heavy car



The presented injury proportions are further analysed in order to find out if the presented

trends so far are a coincidence or not. The following equations try to answer this question

within a 95%-confidence interval (assuming that the number of accidents in 1999 were

Poisson distributed):

( ) KSI

medium

KSI

light

KSI

medium

KSI

light

KSI

medium

KSI

light PPPP +⋅±−=− 96.1µµ

( ) KSI

heavy

KSI

light

KSI

heavy

KSI

light

KSI

heavy

KSI

light PPPP +⋅±−=− 96.1µµ with

KSI

lightµ expected value of fatal or seriously injured drivers in a light car

KSI

mediumµ expected value of fatal or seriously injured drivers in a medium car

KSI

heavyµ expected value of fatal or seriously injured drivers in a heavy car

KSI

lightP proportion (data 1999) of fatal or seriously injured drivers in a light car

KSI

mediumP proportion (data 1999) of fatal or seriously injured drivers in a medium car

KSI

heavyP proportion (data 1999) of fatal or seriously injured drivers in a heavy car

The difference in expected values that an accident in a light car results in fatal or severe

injuries and that an accident in a medium car results in such injuries is with 95%-confidence

determined as (-7.55,11.32). The interval for a comparison between a light car and a heavy

car can be stated as (3.31,21.77). The result for the comparison between light and medium

cars can be interpreted that both vehicle groups can have the same or a different expected

value of KSI accidents, because the interval includes zero as the value representing no

differences in both groups. But the comparison with heavy cars gives a result that indicates a

difference in the expected values of KSI probabilities. The difference of the expected values

is with 95%-confidence positive between the mentioned values, indicating that accidents in

light vehicles are more likely to result in fatal and severe injuries.

On condition that an accident has already occurred the method considering the probability of

a certain injury severity measures only differences in crashworthiness. Furthermore, these

proportions are also confounded with vehicle weight. But the use of a measure like

kilometres travelled annually accounts for differences in crash involvement of different car

types. Figure 30 shows, besides the absolute numbers of involved and injured drivers in

single vehicle accidents, the rate of fatal and severe injuries per annually travelled distances

as well as the rate of fatal, severe and minor injuries.

The additional use of an exposure confirms the already seen trend that drivers of lighter

vehicles are at greater risk to be seriously injured or killed in a single vehicle accident. The

rate drops from 12.12 drivers with fatal or severe injuries resulting from single vehicle

accidents (of 1999) per 1,000,000 kilometres travelled to 6.52 fatal or severe injuries per

1,000,000 kilometres travelled in heavy passenger cars. And also the rate of fatal, severe

and minor injuries drops from 43.54 drivers per 1,000,000 kilometres annually travelled to

25.49 drivers per that distance.

These first results seem to prove the often-stated better crashworthiness of heavier cars. But

of course, these first graphs were not adjusted for any differences in any of the control

variables.

0

100

200

300

400

500

600

700

800


num

ber

of d

river

s

0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,00

40,00

45,00

KS

I per

1,0

00,0

00 V

km /

INJ

per

1,00

0,00

0 V

km


no injuries KSI/1,000,000 Vkm INJ/1,000,000 Vkm

Figure 30: Number of fatal, seriously, slightly injured and not injured drivers in different weight

classes and injury rate per travelled distance – single car accidents

10.1.1 Controlled analysis for single vehicle accidents

In contrast to Figure 28 does Figure 31 present the percentage of drivers seriously injured or

killed by weight classes of the total of single vehicle accidents happened in 1999, which meet

the requirements for driver age, gender, vehicle age and speed limit41. This quantity contains

only 407 records, which is a rather small number to analyse. Nonetheless, Figure 31

confirms the already discussed trend to less severe injuries at higher vehicle weights. Each

weight class contains approximately the same number of drivers involved in a single vehicle

accident. Drivers of a car of the lightest class with an average weight of 1,083 kg account for

14.3 % of all such injuries in these 407 case accidents and drivers of vehicles of the heaviest

group account only for 5.7 %. But also the group of vehicles with an average weight of

1,496 kg account for 14.3 % of all severe and fatal injuries. One might bring up as an

argument that these cars probably are more exposed to road traffic. But the figure shows

also that the vehicles of each group account for approximately one-tenth of the annual

distance travelled by the 407 case vehicles. The distance was estimated according to the

relation presented in Figure 19.

41 male drivers; age 30-55; model years1995, 1996, 1997, 1998, 1999; speed limit > 50 km/h

0%

3%

6%

9%

12%

15%

18%

21%

1083

1230

1366

1439

1496

1546

1592

1657

1772

2144


pers

enta

ge o

f all

KS

I

0%

3%

6%

9%

12%

15%

18%

21%

perc

enta

ge o

f all

Vkm

trav

elle

d

% of all KSI

% of all Vkm

trend (% of all KSI)

Figure 31: Fatal and severe injuries by vehicle weight [% of group’s total] – 407 single car

accidents 1999 meeting the requirements for the control variables

Considering the small number of case accidents, controlled for the chosen variables, ten

weight classes, as analysed in the preceded figure, seem not appropriate. But to use the

same weight boundaries as before to form the three groups (light, medium and heavy car) is

not possible either. Such a group of light cars, including all cars not heavier than 1,080 kg,

would consist of only 15 case accidents. In contrast, the group of cars heavier than 1,480 kg

would contain 231 cases. Therefore, new weight boundaries are chosen according to the

same procedure as before. Light cars are the 20 % lightest cars of all 407 case accidents,

heavy cars are the 20 % heaviest and medium cars are heavier than 40 % but also lighter

than 40 % of all case cars42. 242 of the case accidents are classified in these three groups,

and the average weight in these groups is far higher than in the uncontrolled analyses. But

this should not surprise since only cars are included that are from 1995 or younger and in

paragraph 2.2 it was shown that Sweden’s fleet of passenger cars is getting heavier.

In Figure 32 injury proportions in each weight group are presented, similar to the already

presented uncontrolled analysis in Figure 29. The conclusion that the driver of a heavier car

is better protected than the one in a lighter car is supported, 12.2 % of the drivers in light cars

get killed or seriously injured in a single vehicle accident, while 3.8 % of the drivers in heavy


cars experience such injuries. However, I want to stress again that the number of

observations is too small for a statistical analysis.

In order to reach a higher quantity of case accidents in each of the three weight groups,

accidents from other calendar years might be included. But the number of possible calendar

years is limited. To eliminate changes of for instance driver attitude over time (speeding,

alcohol and driving, seat belt usage) accident data from only one calendar year was chosen.

Anyhow, a selection of more years is also limited by the control variable vehicle age. The

oldest vehicle selected is from 1995, so the earliest possible calendar year that could be

included is 1995. But the number of cars of year model 1995 registered is rather small in the

end of that year and so is their occurrence in the accident database of the same year.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%




(controlled)

0,0

5,0

10,0

15,0

20,0

25,0

30,0

35,0

40,0

45,0

50,0

55,0


num

ber

of d

river

s / I

NJ

per

1,00

0,00

0 V

km

0,0

1,0

2,0

3,0

4,0

5,0

6,0

7,0

8,0

KS

I/1,0

00,0

00 V

km

killed

severe injuries

minor injuries

no injuries

KSI/1,000,000 Vkm

INJ/1,000,000 Vkm

Figure 33: Number of fatal, severe, minor and no injuries to drivers in different weight classes

and injury rate per travelled distance – single car accidents (controlled)

Additional information for the control analysis for calendar year 1999 is illustrated in Figure

33. The annually travelled distance is included as exposure to calculate the rates of killed

and seriously injured drivers and the rates of killed, seriously or slightly injured drivers for

each weight category. A substantial decrease of the KSI per 1,000,000 kilometres travelled

from light to heavy cars can be seen. But the values for light and medium cars are quite

comparable. Reductions of the second injury rate, to get killed, seriously or slightly injured,

are not that big either.

Certainly, a driver of a car is interested how well he is protected in this car. After this analysis

of single vehicle accidents it seems appropriate to advise any driver to chose the heaviest

possible car in order to be protected in case of an accident. But no conclusion about net

safety can be drawn.

Such a choice would lead to a heavier fleet than we have today and to an increased fuel

consumption, which means that both societal goals, improvements in road safety and

reductions of CO2 emissions, couldn’t be achieved, but just one of them.

10.1.2 Regression of single vehicle accident data

The purpose of regression is to find a quantitative description of a relationship between a

group of explanatory variables and a response. The group of explanatory variables vehicle

weight, vehicle age, driver age and gender, and speed limit will be used. The driver injury

level, registered by the police at the accident site, is known to every set of explanatory

variables. To find a relationship between explanatory variables and injury level can help to

understand which variables have the greatest effect. And of course the direction of the effect

is of interest, whether an increase in vehicle weight for instance leads to a higher injury level

(representing, on the scale from 1-killed to 4-uninjured, less severe injuries).

For single vehicle accidents only the influence on individual safety can be evaluated. In

Figure 34 are driver injuries of all single vehicle accidents included in the data set for 1999

plotted against the vehicle weight. The data set contains only passenger cars as discussed

before (Chapter 7). Only four injury levels are distinguished and so an attempt to quantify this

relation of vehicle weight and injury severity level seems not promising. Nonetheless, I

attempted to perform such a regression analysis according to the multi-linear regression

equation:

6655443322110 xaxaxaxaxaxaay ⋅+⋅+⋅+⋅+⋅+⋅+=

The coefficients a0-6 can be calculated by doing a least squares fit. This method minimizes

the sum of the squares of the deviations of the data from the model.43 Which variables the

values x1-6 represent, can be seen in Table 6. The blue line in Figure 34 illustrates the results

of a regression where y is the driver injury level (killed, seriously, slightly injured, uninjured).

43 The MATLAB backslash operator is used to compute these coefficients efficiently.

Figure 34: Injury levels of drivers involved in single car accidents 1999

As already said, injury levels are discontinuous, which leads to a bad fit of the regression

model (Figure 34). In order to get continuous values for the response variable of the multi-

linear regression model, the single vehicle accidents will be classified according to vehicle

weight or size comparable to the method used in the pre-study. In each of the 20 groups44,

the weighted (by vehicle kilometres travelled annually) average is computed for the number

of drivers killed or injured (KI) per 1,000,000 vehicle kilometres travelled (vkm) and each of

the explanatory variables. These average values are the input variables for the regression.

The calculated coefficients are presented in the first of the result columns in Table 6. Also

given is the value of the maximum difference between the calculated and the observed

values. With help of this maximum error (MaxErr) it can be evaluated how well the model fits

the observed reality. As it also can be seen in Figure 35 and Figure 36, the linear regression

model could not produce a satisfactory fit to the data.

44 The intervals of service vehicle weight are bounded at the top by the following percentiles of service

vehicle weight: the 5th, 10th, 15th, 20th, 25th, 30th, 35th, 40th, 45th, 50th, 55th, 60th, 65th, 70th, 75th,

80th, 85th, 90th, 95th, and maximum weight.

variables unit

KI/ 1,000,000 vkm log(P/(1-P)); P: proportion of drivers

killed or injured

a0 -10.1840 1.6638

a1 X1 length (case car) cm - -

a2 X2 weight (case car) kg -0.0021 0.0001

a3 X3 vehicle age years 2.1254 -0.0150

a4 X4 driver age years -0.3941 0.0172

a5 X5 driver gender 1:male/2:female 29.4030 -1.4336

a6 X6 speed limit km/h 0.0328 -0.0003

classified

by weight of

the case

car

(Figure 35)

MaxErr 2.6374 0.1390

a0 16.5330 0.5430

a1 X1 length (case car) cm -0.0422 0.0020

a2 X2 weight (case car) kg - -

a3 X3 vehicle age years 1.4562 0.0167

a4 X4 driver age years 0.6494 -0.0358

a5 X5x driver gender 1:male/2:female 0.7355 -0.0890

a6 X6 speed limit km/h -0.0429 0.0031

classified

by length of

the case

car

(Figure 36)

MaxErr 5.2521 0.2497

Table 6: Regression coefficients – single vehicle accidents

Since the first regression analysis did not result in a satisfactory fit, another regression was

performed in order to reveal the weight/size-safety relationship. Instead of injury rate per

distance injury proportions are used as response variable y. The logistic model is suited to

deal with proportion data.

66554433221101log xaxaxaxaxaxaa

P

P ⋅+⋅+⋅+⋅+⋅+⋅+=

−

P is the proportion of drivers killed or injured. To use the computed coefficients the logistic

relationship has to be inverted.

( )6655443322110exp11

xaxaxaxaxaxaaP

⋅−⋅−⋅−⋅−⋅−⋅−−+=

The computed coefficients are presented in the second result column in Table 6.

Calculations with these coefficients and the observed values for the explanatory variables

are illustrated as blue line in Figure 35 and Figure 36.

Figure 35: Regression – classification by vehicle weight (single car accidents)

Figure 36: Regression – classification by vehicle length (single car accidents)

The second regression also could not provide significant results. Nonetheless I want to

interpret the found coefficients. Both regressions were performed with two different data sets.

In Figure 35 a regression based on average values of weight classes is illustrated. Figure 36

contains observations and calculations of a regression based on average values of length

classes. It was of interest to analyse these different data sets in order to evaluate whether

the size (here length) or the weight effect is more important to road safety.

The calculations resulted in coefficients for vehicle length and weight that can be interpreted

as the following: an increase in either weight or length reduces the risk of driver fatalities and

injuries per distance and the proportion of killed or injured drivers. But the coefficients for

length and weight differ in their magnitude. To evaluate this difference in order to answer the

question, which variable is more important to safety, the units of both variables have to be

taken into account. Vehicle length is given in centimetres, therefore the coefficient of –0.0422

in the regression for killed and injured drivers per travelled distance could mean that an

increase of 10 cm in vehicle length results in a decrease of 0.422 killed and injured drivers

per travelled distance. On the other hand, an increase of 100 kg in vehicle weight could

result in a decrease of 0.21 killed and injured drivers per travelled distance. Of course, both

interpretations are made with the assumption that all other factors remain unchanged. Since

such a comparison for changes in proportion of killed or injured drivers results also in a

higher reduction for changes in length, it is concluded that vehicle length has a bigger impact

on the safety a car offers its occupants in case of an single vehicle accident. After taking a

closer look at Figure 36, it seems that a value for vehicle length exists that represents a

certain safety standard for its occupants. Observations of vehicle lengths of about 450 to

500 cm seem to result in injury rates and injury proportion of comparable magnitude.

10.2 Frontal collision accidents

Again, simple plots of injury level by vehicle weight should help to discover first trends.

Figure 37 contains values of the proportion of four different injury levels – killed, severe

injuries, minor injuries, no injuries – in 20 weight categories as result of frontal collisions. The

values are plotted over vehicle weight, additionally simple linear trends of each injury level

are plotted. In contrary to the analysis of single vehicle accidents the injury severity of the

drivers depend not only of crashworthiness of the own car, but also on the properties of car it

collided with. But in this first figure no relation to the actual accident opponent is taken into

account. The proportions of fatal, severe, and minor injuries are decreasing with increasing

vehicle weight. The proportion of fatal injuries decreases the most. An increasing trend

shows the proportion of no injuries.

These proportions of the different injury levels can be confounded with vehicle weight. For

instance, heavy cars are often stated to have lower accident rates per travelled distance.

Thus, the number of police-registered accidents of a certain car group is no appropriate

measure of exposure. Therefore, differences in crash involvement of different car types are

also addressed in the analyses.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

853

953

1010

1048

1105

1154

1197

1231

1275

1309

1339

1361

1387

1413

1437

1460

1490

1548

1643

2074


killed severe injuries light injuries

no injuries trend (killed) trend (severe injuries)

trend (minor injuries) trend (no injuries)

Figure 37: Driver injury level as function of avg. weight – frontal collisions 1999 (N=1370)

To focus more on severe and fatal injuries a rate of such injuries and the annual travel

distance is calculated and presented in Figure 38. The decreasing trend, already seen in

Figure 37, is confirmed. But the variations around the linear trend are larger than in Figure 28

illustrated for single vehicle accidents. The reason for these variations could be differences in

weight and construction of the collision partner. Therefore, with the help of the unique

accident identification number and the identification number of each traffic element

involved45, vehicles that collided where identified and written in a matrix. Figure 39 contains

the rate of accidents with killed and seriously injured drivers per 1,000,000 kilometres

annually travelled for both vehicles involved.

45 The police give every traffic element involved in an accident a number.

0

5

10

15

20

25

30

853

953

1010

1048

1105

1154

1197

1231

1275

1309

1339

1361

1387

1413

1437

1460

1490

1548

1643

2074


KS

I/1,0

00,0

00 V

km

0

5

10

15

20

25

30

KS

I (ab

solu

te n

umbe

rs)

KSI

KSI/1,000,000 Vkm

trend (KSI/1,000,000 Vkm)

Figure 38: KSI in absolute numbers and KSI rate per 1,000,000 vkm – frontal collisions

0

5

10

15

20

25

853

953

1010

1048

1105

1154

1197

1231

1275

1309

1339

1361

1387

1413

1437

1460

1490

1548

1643

2074

weight classes (CASE car, average weight)

KS

I(C

AS

E c

ar)/

1,00

0,00

0 V

km

0

5

10

15

20

25

1302

1301

1286

1321

1286

1321

1320

1278

1337

1256

1294

1330

1385

1321

1318

1286

1304

1323

1314

1398

average weight of the OTHER car

KS

I(O

TH

ER

car

)/1,

000,

000

Vkm

KSI(CASE car)/1,000,000 Vkm KSI(OTHER car)/1,000,000 Vkm

trend (KSI(CASE car)/1,000,000 Vkm)

Figure 39: Injury rates of both cars involved in a frontal collision

The rates for the group of case cars are shown according to the case cars’ weight (Figure

39). The rate of killed or seriously injured drivers in the case car decreases with increasing

weight of these cars. But also the decreasing weight of the group of the opponent cars

seems to be a reason for this trend. The rate for the other cars is increasing since the

masses of both cars involved in the frontal collision mismatch more and more.

In order to investigate the influence of the other car on road safety further, both vehicles, the

case car and the other car it collided with, are classified according to their weights in the

following groups: light, medium, and heavy car. The group of light cars contains the 20 %

lightest cars of all in frontal collision accidents involved cars. The 20 % heaviest cars are

combined to a group of heavy cars. A medium car is a car that is heavier than 40 % of all

cars involved in frontal collisions but also lighter than 40 % of all cars involved in that

accident type46. If cars of the same group collide the case vehicle is always the lighter one.

Figure 40 contains both, the number of drivers killed, seriously or slightly injured and not

injured at all and injury rates – KSI and minor injuries per 1,000,000 km, and KSI per

1,000,000 km – in frontal collisions between cars of approximately the same weight. The

largest number of observations can be found in frontal collisions of two light cars. Of these

collisions between cars with comparable weight, the drivers in collisions of two heavy cars

seem to have the lowest risk to get killed or in any sense injured. Not light car-light car

accidents seem to be the most severe to the drivers, but collisions of two cars of medium

weight, although the rates of the car entitled as car number one in the police protocols and

the car entitled as number two differ considerably and differ the most for collisions of medium

cars. A simple comparison should show if the weight of the second car is often larger and

could be the reason for the lower injury risk. But the comparison could not confirm this

speculation. A plausible reason could be that the police at the accident site often register the

more seriously damaged car as car number one – but this cannot be proven.

Figure 41 compares the same values as the last figure but for collisions of cars of different

weight classes. Values for both cars involved in the accident are shown, and so are all

possible accident combinations for vehicles of different weight. These two figures, comparing

the injury rates for collisions of cars with comparable weight and for collisions with heavier or

lighter cars, could not fully support the conclusion that the heavier a car, the safer it is in case

of an accident. The case of medium cars was discussed for collisions within the weight

group, but also in Figure 41 a medium car in collision with a heavy car accounts for the

highest KSI rate. Anyhow, the small number of accidents included in this comparison

opposes a final conclusion.


0

5

10

15

20

25

30

35

40

1 2 3 4 5 60

10

20

30

40

50

60

70

80

KS

I/1,0

00,0

00 V

km I

NJ/

1,00

0,00

0 V

km

killed severe injuries

minor injuries no injuries

KSI/1,000,000Vkm INJ/1,000,000 Vkm

light vs. light medium vs. medium heavy vs.

Figure 40: Number of fatal, seriously, slightly injured and uninjured drivers in different weight

classes in collisions with the same car type and injury rates per travelled distance

0

5

10

15

20

25

30

35

40

light medium medium light light heavy heavy light medium heavy heavy medium0

10

20

30

40

50

60

70

80

KS

I/1,0

00,0

00 V

km I

NJ/

1,00

0,00

0 V

km

no injuriesminor injuriessevere injurieskilledINJ/1,000,000 VkmKSI/1,000,000Vkm

Figure 41: Number of fatal, seriously, slightly injured and not injured drivers in different weight

classes in collisions with cars of different weight and injury rates per travelled distance

Nonetheless, I will attempt to draw a conclusion, but an analysis with paired comparison

method will illustrate the problem of the small number of observations even more. The

calculation – its results are presented in Table 7 – of relative injury risk brings no certain

results, since so little observations could be used in the calculations according to the given

equation:

31

21

xx

xxR

++

=

other vehicles LIGHT other vehicles MEDIUM other vehicles HEAVY Relative injury risk for minor,

severe and fatal injuries KI Not injured KI Not injured KI Not injured

KI (x1) 4 (x2) 4 (x1) 3 (x2) 7 (x1) 1 (x2) 10 LIGHT

Not injured (x3) 3 R=1.14 (x3) 0 R=3.33 (x3) 1 R=5.50

KI (x1) 5 (x2) 1 (x1) 4 (x2) 4 (x1) 4 (x2) 3 MEDIUM


KI (x1) 3 (x2) 1 (x1) 8 (x2) 1 (x1) 3 (x2) 5 HEAVY


Table 7: Relative injury risk for minor, severe and fatal injuries in frontal collisions

From the table one could conclude that the risk to get injured or killed in the lighter car of a

frontal collision seems to be higher than for the driver in the heavier car it collided with (see

row ‘LIGHT’: Rlight vs. light = 1.14 -> Rlight vs. medium = 3.33 -> Rlight vs. heavy = 5.50). At the same time,

one can see the very few observations which take part in this paired comparison analysis.

But a further analysis of injury proportions47 (Figure 42) might give an idea if the observed

trends are coincidence or not. The following equations try to answer this question within a

95%-confidence interval (assuming the number of accidents in 1999 were Poisson

distributed):

( ) KSI

mediumvs

KSI

lightvs

KSI

mediumvs

KSI

lightvs

KSI

mediumvs

KSI

lightvs PPPP ...... 96.1 +⋅±−=− µµ

( ) INJ

mediumvs

INJ

lightvs

INJ

mediumvs

INJ

lightvs

INJ

mediumvs

INJ

lightvs PPPP ...... 96.1 +⋅±−=− µµ

47 The proportion of an injury level in a weight group is calculated as number of drivers’ injuries of a

certain level divided by the total number of drivers in that weight involved in frontal collisions.

( ) KSI

heavyvs

KSI

lightvs

KSI

heavyvs

KSI

lightvs

KSI

heavyvs

KSI

lightvs PPPP ...... 96.1 +⋅±−=− µµ

( ) INJ

heavyvs

INJ

lightvs

INJ

heavyvs

INJ

lightvs

INJ

heavyvs

INJ

lightvs PPPP ...... 96.1 +⋅±−=− µµ

with:

KSI

lightvs.µ expected value of fatal or seriously injured drivers in a light car after a

frontal collision with a light car

KSI

lightvsP . proportion (data 1999) of fatal or seriously injured drivers in a light car

after a frontal collision with a light car

INJ

lightvs.µ expected value of fatal, seriously or slightly injured drivers in a light car

after a frontal collision with a light car

INJ

lightvsP . proportion (data 1999) of fatal, seriously or slightly injured drivers in a

light car after a frontal collision with a light car

KSI

mediumvs.µ expected value of fatal or seriously injured drivers in a light car after a

frontal collision with a medium car

KSI

mediumvsP . proportion (data 1999) of fatal or seriously injured drivers in a light car

after a frontal collision with a medium car

INJ

mediumvs.µ expected value of fatal, seriously or slightly injured drivers in a light car

after a frontal collision with a medium car

INJ

mediumvsP . proportion (data 1999) of fatal, seriously or slightly injured drivers in a

light car after a frontal collision with a medium car

KSI

heavyvs.µ expected value of fatal or seriously injured drivers in a light car after a

frontal collision with a heavy car

KSI

heavyvsP . proportion (data 1999) of fatal or seriously injured drivers in a light car

after a frontal collision with a heavy car

INJ

heavyvs.µ expected value of fatal, seriously or slightly injured drivers in a light car

after a frontal collision with a heavy car

INJ

heavyvsP . proportion (data 1999) of fatal, seriously or slightly injured drivers in a

light car after a frontal collision with a heavy car

The intervals calculated for the differences in expected values according to the equation

above are presented in Table 8. The calculated values represent a comparison of drivers’

injury levels in light cars after a collision with another light car and after collision with medium

or heavy cars. Thus, the change of the injury level in the light car according to the weight of

the accident opponent is documented in the table.

0

10

20

30

40

50

60

light vs.light light vs. medium light vs.heavy light vs.light medium vs. light heavy vs.light

% o

f driv

ers

in th

e lig

ht c

ar

% of drivers KSI in the light car

% of drivers KSI+minor inj. in the light car

Figure 42: Injury proportions in the light car (car 1 – left / car 2 – right) in frontal collisions

Like in Figure 42, two cases are distinguished. In the first row, the injury level of the as car 1

registered light vehicles is analysed. The second row contains the results for calculations for

cases, in which car number 2 is the light car.

light car vs. MEDIUM car light car vs. HEAVY car

heavymediumvslightvs /.. µµ −

lower boundary upper boundary lower boundary upper boundary

KSI -9.10 15.81 -12.02 13.66

LIGHT car1 all injuries -31.34 7.73 -28.12 10.36

KSI -20.49 4.12 -8.61 12.59

LIGHT car2 all injuries -20.14 15.68 -33.04 5.20

Table 8: Interval boundaries for expected values of proportion of injury levels in light cars in

collision with another light car compared to collisions with other heavier cars

Only if both boundaries of the 95%-confidence interval are negative an indication is given

that with increasing weight of the accident opponent also drivers’ injuries get more severe.

This condition is never met. So the proportion of injured drivers can be higher, lower or

unchanged with increasing weight of the accident opponent.

Certainly, the small number of observations in each weight category is a problem for any

statistical analysis. But also confounding variables can hide the real relation of safety and

weight. Thus, an attempt to control for such variables has to be made.

10.2.1 Controlled analysis of frontal two-car collisions

The control variables were chosen according to a pre-study, which was described in Chapter

9. Out of 1999’s accident data 107 accidents were selected according to the chosen control

variables48.

But these accidents include just 2 fatally injured drivers. To use the rate of serious and fatal

injuries per travelled distance is under such condition not suitable. Anyhow, Figure 43

illustrates the proportions of the four different injury levels in relation to the opponent cars’

weight. The data is classified according to that weight in five categories in order to identify a

trend of injury severity by weight of the other car. As said before, the number of killed drivers

is not suitable for a comparison. But the proportions of uninjured drivers should be

discussed. These proportions decrease with increasing weight of the other car. Additionally,

the proportions of minor and severe injuries tend to increase. The speculations about a lower

safety level in lighter vehicles induced by heavier cars seem to be confirmed. But does this

offset the assumed higher safety in heavy cars? Regression analyses try to find an answer to

that question.

48 male drivers; age 30-55; model years1995, 1996, 1997, 1998, 1999; speed limit > 50 km/h

0%

20%

40%

60%

80%

100%

962 1157 1292 1416 1693

average weight of the OTHER car (in weight classes)

no injuries

light injuries

severe injuries

killed

Figure 43: Proportions of injury levels in five weight classes of the other car’s weight

Further analyses of these selected accidents in light, medium and heavy cars will not be

made, because the set of data is too small.

10.2.2 Regression of frontal collision accident data

Like for single vehicle accidents, multiple regression is used in order to quantify the influence

vehicle weight has on road safety. In principle the same steps are performed as for single

vehicle accidents. But in case of frontal collisions the problem is even more complex and it

involves also the aspect of net safety. The injury level is assumed to depend beside others

on the vehicle weight of the own car and the weight of the other car. Also both cars outer

dimensions might have an influence on the injury level of the driver of the case car. Thus, the

result of the first regression, with the injury severity of the driver of the case car as y-value, is

plotted against the weight of the case car and against the weight of the other car in Figure

44. The regression analyses are all performed according to the equation below, and the

variable description can be seen in Table 9.

776655443322110 xaxaxaxaxaxaxaay ⋅+⋅+⋅+⋅+⋅+⋅+⋅+=

Figure 44: Injury levels of drivers involved in frontal collisions 1999 (by weight/length)

It was already discussed for single vehicle accidents that such a regression, as presented

above, which is supposed to predict the injury level of the driver in the case car, does not

result in a suitable model. Thus, other possibilities to quantify the safety-weight/size relation

have to be found.

The same attempts as for single vehicle accidents will be made. These include regression

analyses with drivers killed or injured per travelled distance and injury proportion in different

weight or length classes as y-values. Also in regression analyses of frontal collisions

problems are induced by the classification in 20 groups. Such a classification leads to a

relatively small quantity of data points, which might prevent the regressions from resulting in

reasonable coefficients. But even if the regression models fit the data rather well, they can

never be used to predict future safety levels of Sweden’s passenger car fleet.

Besides that, the distinction between weight and size effect is even more difficult for frontal

collisions. Both variables could influence injury severity, but in case of frontal collisions these

variables of both, the case car and the other car, have to be taken into account. Therefore,

results for more regression analyses than for single vehicle accidents are presented in Table

9.

The results of the first regressions, which are also illustrated in Figure 45 und Figure 46,

could be interpreted as the following: The regression of a weight-classified dataset results in

a coefficient for the own weight of 0.0054 in case of KI/1,000,000 vkm as y-values, a by

100 kg increased weight might result in an increase of 0.54 KI/1,000,000 vkm. This seems to

contradict the conclusion drawn from the single vehicle analysis that an increase of vehicle

weight leads to less injuries in that car. The same regression provided a coefficient for the

weight of the other car of –0.0659, an increased weight of the other car might improve safety.

From the net safety point of view one could say that the reduction in injury rate through

increased weight of the other car also offsets increase of the rate through increased weight

of the case car.

These results are quite different from all the conclusions drawn before. The preceding

analyses evaluated an increase in weight of the own vehicle as positive for individual safety,

but it was assumed that a higher weight of the other car could reduce net safety.

The regression of the length-classified dataset does not support those results from weight-

classified data analysis. An increasing length of the own car would result in reductions of

injury rate and proportion, and the weight of the other car increases these rates. These

figures support the conclusions drawn earlier.

In order to reveal the reasons for such contrary results additional regression analyses were

performed. The weight- and the length-classified data are input of regressions, which use

both weight and length of the own and the other car as explanatory variables. But to begin

with, the length of the other car is excluded from the set of explanatory variables. As the y-

value is chosen to be injury rate, the weight of the own car and the one of the other car have

a decreasing effect on injury rate if analysed for weight-classified data.

On the contrary, the classification by the case car’s length leads to coefficients, which could

be interpreted in a way that an increase in the case car’s weight might lead to reductions in

injury rate but an increase of the other car’s weight might increase the rate. And since the

coefficient of the other car’s weight is in absolute numbers larger than the coefficient of the

weight of the own car, an increase in car fleet’s weight could reduce road safety.

If weight as well as length of the other car is taken into account (third result column in Table

9), the results of the regression of the data classified by length support that conclusion. An

additional conclusion is that an increase in length of the other cars reduces accident

consequences measured in drivers killed or injured per travelled distance. But the regression

of data classified by weight still shows other tendencies. The weight of both, the case and the

other car, has a reducing effect on that injury rate. On the other hand, for both data sets the

length of the case car seems to have an increasing effect on the injury rate. When injury

proportions are used as y-values, the same tendencies as for injury rate can be seen (Table

9). A vehicle’s length seems to have the more important effect on road safety than car weight

of the own or the other car.

KI/1,000,000 vkm ln(P/(1-P)); P: driver in case

variables unit Figure 45/

Figure 46 Figure 45/

Figure 46

a0 137.5800 117.6300 117.7200 -5.8505 -4.5907 -2.3597

a1 X1 length (case car) cm - 0.0450 0.0450 - -0.0028 -0.0014

a2 X2 weight (case car) kg 0.0054 -0.0007 -0.0008 -0.0001 0.0002 0.0000

a3 X3 vehicle age years 2.4438 2.3471 2.3470 -0.0260 -0.0199 -0.0234

a4 X4 driver age years -1.5346 -1.5409 -1.5408 0.0782 0.0786 0.0802

a5 X5 driver gender male/ female

31.6020 34.3950 34.3920 -1.4951 -1.6715 -1.7428

a6 X6 speed limit km/h -0.2994 -0.3069 -0.3069 0.0140 0.0144 0.0138

a7 X7 weight (other car) kg -0.0659 -0.0616 -0.0615 0.0032 0.0029 0.0044

a8 X8 length (other car) cm - - -0.0004 - - -0.0096

classified by

weight of the

case car

MaxErr 10.593 10.7500 10.7500 0.58534 0.6079 0.5859

a0 69.9590 20.5830 89.5120 -2.0540 0.8323 -2.5986

a1 X1 length (case car) cm -0.1109 0.0639 0.1405 0.0055 -0.0047 -0.0085

a2 X2 weight (case car) kg - -0.0330 -0.0456 - 0.0019 0.0026

a3 X3 vehicle age years 0.0083 -0.9669 -1.1398 0.0987 0.1558 0.1644

a4 X4 driver age years -0.9985 -1.2347 -1.1213 0.0475 0.0613 0.0557

a5 X5 driver gender male/ female

-13.4600 -9.6488 -8.6358 0.6686 0.4458 0.3954

a6 X6 speed limit km/h -0.1196 -0.1553 -0.1352 0.0061 0.0082 0.0072

a7 X7 weight (other car) kg 0.0629 0.0861 0.1350 -0.0033 -0.0047 -0.0071

a8 X8 length (other car) cm - - -0.3455 - - 0.0172

classified by

length of the

case car

MaxErr 6.6937 7.0434 7.0634 0.34976 0.3439 0.3449

Table 9: Regression coefficients – frontal collision accidents

But which method, classification by weight or length, results in the most realistic coefficients.

These coefficients give information, which effect the variables weight and length have on

accident consequences and how big these effects are. A look at Figure 45 and Figure 46

should help to detect the differences of both methods that could result in these contrary

results. The first of these figures, representing a regression of the weight-classified data set,

shows that the data is more scattered and also that the model fits the data less well than the

model calculated for the data classified by length. Only the groups of very short or very long

cars give observation of injury rates and proportions that vary a lot.

Anyways, the maximal errors of all regressions presented in Table 9 are rather high so the

coefficients found do not represent the reality on Swedish roads well.

Figure 45: Regression – classification by vehicle weight (frontal collisions)

Figure 46: Regression – classification by vehicle length (frontal collisions)

Figure 47: Relation between vehicle weight and length –classes by vehicle weight

Figure 48: Relation between vehicle weight and length – classes by vehicle length

Figure 47 and Figure 48 illustrate the relation between weight and length of the chosen

weight and length groups. Both figures show an almost equal development. After an

approximately linear increase vehicle weight (Figure 48) as well as vehicle length (Figure 47)

remain almost on the same level, but the group of the longest or heaviest cars represents the

highest value in weight or length. For further discussion about the effect of a car’s outer

dimensions on injury severity the direction of impact has to be known.

Chapter 11 Conclusion of Part II

Heavy passenger cars, especially from Swedish manufacturers have the reputation to be

safe cars. It is therefore not surprising that safe conscious drivers chose these cars.

Conversely, high performance cars, vans and sport utility cars are heavy cars as well but

attract drivers of a more risk taking character. These cars often have high injury rates.

Therefore, the safety effect might be not attributable to a car’s weight or outer dimensions,

but the higher fuel consumption correlates well with a car’s weight. Additionally, newer car

models, equipped with more safety devices, are heavier but also more expensive. Thus,

buyers of such cars are normally experienced older drivers, maybe with a higher education.

So several reasons, besides a car’s construction and weight, come into play when analysing

why heavy cars have lower injury rates. These lower injury rates for heavier and larger

(longer) cars could be found when analysing data for single vehicle accidents and frontal

collisions, although the regressions of frontal collision data have shown also a quite contrary

result. But this is only the case when classifying the data by vehicle weight. Thus, the choice

of input data is important for the quality of the results. No matter if the data was classified by

weight or length, the maximum error between regressions model and observed data is far too

large to speak about a fitted model. A reason could be the usage of groups as input data. By

doing that, only few observation data points could be used, and it was necessary to calculate

average values. But on the other hand, by using classified data, an injury rate by travelled

distance could be used as y-value of the regression. But the question that has to be

answered here is, do I have to reject the hypotheses (Chapter 6)?

The first hypothesis was already discussed. Contrary to the need to save fuel in order to

increase fuel security and to reduce CO2 emissions, the analysis of accident data from year

1999 did show that new car models got heavier. And at least in Sweden is the proportion of

rather light cars small. However, the investigations, what effect vehicle weight on road safety

has, have to go on.

The analysis of single vehicle accidents showed that heavier passenger cars provide better

protection than lighter cars. But it could not be settled, whether collisions between cars of

approximately the same weight are safer than collisions between cars of different weight. All

passenger cars involved in frontal collisions were classified in the groups of light, medium,

and heavy cars. Collisions between medium cars tend to have more injuries per travelled

distance than collisions between light or heavy cars, although the rate of severe and fatal

injuries is highest for collisions between light cars.

Furthermore, in collisions between passenger cars of different weight, the driver of the

heavier car seems to be better protected than the one of the lighter car. Regression analyses

were conducted in order to evaluate the effect weight has on safety and to quantify the

already identified relation. In contrary to single vehicle accidents also the weight and size had

to be taken into account. The coefficients for those variables varied a lot in different

regressions. The chosen regression model could not fit the data well. The best fit was

reached for length-classified accident data. And even if the fit of the model would have been

exceptional, it could have never been used for prediction of future developments.

First of all, only passenger cars could be included in these analyses, for truck and light truck

for instance were no exposure data available. No estimate of this measure could be done, as

the function provided by the Swedish National Road Administration49 was only valid for

passenger cars. Pedestrians and other vulnerable road users should have been included in

such an analysis. And naturally, one could not predict the effect new car models will have.

New models do not only differ in weight or length, new materials and construction principles

and not at least new developments in active safety systems will alter the injury risk and the

risk of being involved in accidents. With those new car models also other accident patterns

could occur.

49 See Figure 19

PART III FINAL CONCLUSION

Both, reducing CO2 emissions and fatalities and seriously injured persons in road traffic, are

high priority goals of Swedish and international policy. As one measure to reduce fuel

consumption and by that reducing CO2 emissions my master’s thesis focused on weight

reduction of road vehicles, although the European and Swedish passenger car fleets tend to

get heavier. Only passenger cars have been included, because for no other vehicle type

exposure data could be estimated.50 To include non-motorised road users is another complex

problem.

A literature review revealed a quite contrary view of the scientific world on the relation of fuel

consumption and weight on road safety. The main problem seems to be the confusion of

weight and size and limitations in data availability. The literature on subjects like multiple-

vehicle accidents is fragmented, partly because of the large number of configurations

associated with these accidents. The amount of data that would be needed in order to

conduct a complete analysis of the weight-safety relationship, including all vehicle types and

accident types, is enormous. In the first place, all different variables the accident

consequences depend on have to be found. These variables probably differ between the

vehicle and accident types. Besides that, a suitable measure of exposure has to be found to

put for instance the injury risk of different vehicle types “on a level playing field”.

The attempt to quantify the effect weight has on safety on Swedish roads produced mixed

results. But even though the regression models for single vehicle accidents and frontal

collisions fit the data rather badly, basic trends could be found. The analyses of single

vehicle accidents and frontal collisions indicated advantages of heavier cars in protecting

their occupants, but these advantages seem to be offset because heavier vehicles tended to

increase the injury risk of the drivers of the cars they collided with.

The correlation between weight and length could be shown, and both affect safety. But the

weight effect could not successfully be isolated from the effect of length. And for further

analysis width should be included, when also the direction of impact is known more qualified

conclusions could be drawn. Another aspect of further analysis is to use more detailed

exposure data. The used exposure data was only chosen according to vehicle age. But in the

long run in order to include light and heavy trucks a better data basis is needed. Also better

50 The function provided by the Swedish National Road Administration estimating vehicle kilometres

travelled annually is valid for passenger cars only (Figure 19).

data on variables, like seat belt use or air bag deployment should be available in order to

analyse weight and size effects on road safety.

Anyhow, even if assumed that weight reductions would be made in order to serve CO2

reduction goals and if assumed this would have an adverse effect on safety, other measures

could accompany weight reductions and neutralise any adverse effect that CO2 reduction

policy might have on safety. In paragraph 3.2.2 different strategies were presented that

would help to achieve CO2 reduction goals without conflicting with safety or even improve

road safety.

To summarise the results of this master’s thesis the following can be said. The connection of

CO2 reduction and safety is not entirely understood, which also opposes a policy that serves

both goals, reduced CO2 emissions and improved road safety. The confusion of size and

weight effects remains problematic. There is a need to develop a measure of overall car

safety. With such a measure politicians could specify a level of car safety. “Vision zero” and

the definition of a certain number of fatalities and severe injuries in road traffic in general

have not shown to be as effective as the politicians intended them to be.

Even if a measure of overall car safety would be available and suitable data would be

analysed with a suitable method, the results could never predict future developments. The

impact of new designs or technologies in more recent vehicles will be revealed as they enter

the market. This on-the-road experiment has to be observed. Therefore, important future

investigations have to look more closely at “before vs. after” injury rates of specific make

models that were redesigned, with important changes in materials or structure.

It is possible that car manufacturers like Volvo and Saab already conduct such studies. To

get this kind of information a survey including manufacturers could be conducted, which may

include also the following:

- How does/did Swedish and international policy change car design?

- How are CO2 reductions and safety evaluated during the design process? Do they

have a standard method? Which measures are used for car safety?

- Is either CO2 or road safety considered to be most important during the car design

process in the last two decades?

These questions remain open for future research, which can build up on the methods and

findings of this thesis. Especially, cooperation with road vehicle manufacturers, to include all

road users and the availability of exposure data are important to find the true weight-safety

relationship.

References

BIL Sweden (2000): Bilismen i Sverige 2000, Fakta om bilbeståndet,

Nyregistreringar,Produktion, Import & Export, Bilskatter, Internationella översikt. BIL Sweden,

Stockholm, Sweden

BIL Sweden (2003): Bilismen i Sverige 2003, Fakta om bilbeståndet,

Nyregistreringar,Produktion, Import & Export, Bilskatter, Internationella översikt. BIL Sweden,

Stockholm, Sweden

Broughton, Jeremy (2003): The benefits of improved car secondary safety. Accident Analysis

and Prevention, Vol. 35, No. 4, pp 527-535, 2003

Broughton, Jeremy (1995): The likely effects of downsizing on driver casualties in two-car

accidents. Transportation Research Laboratory, TRL report 171, Crowthorne

Bunch, David S., et. al (1993): Demand for clean-fuel vehicles in California: a discrete-choice

stated preference pilot project. Transportation Research Part A, Vol. 27A, No. 3, pp 237-253,

1993

Buzeman, Dagmar G., Viano, David C., Lövsund, Per (1998): Car occupant safety in frontal

crashes: A parameter study of vehicle mass, impact speed, and inherent vehicle protection.

Accident Analysis and Prevention, Vol. 30, No. 6, pp 713-722, 1998

Buzeman-Jewkes, Dagmar, Lövsund, Per, Viano, David C. (1999): Safety of a downsized

vehicle fleet: Effects of mass distribution, impact speed and inherent protection in car-to-car

crashes, in Vehicle safety structure. SAE special publication SP-1442, Warrendale, PA, USA

Crandall, R.W., and Graham, J.D. (1989): The Effect of Fuel Economy Standards on

Automobile Safety. Journal of Law and Economics, 1989

DeLuchi, Mark (1993): Greenhouse-gas emissions from the use of new fuels for

transportation and electricity. Transportation Research Part A, Vol. 27A, No. 3, pp 187-191,

1993

DeLuchi, Mark, Ogden, Joan M. (1993): Solar-hydrogen fuel-cell vehicles. Transportation

Research Part A, Vol. 27A, No. 3, pp 255-275, 1993

ECMT (2000): Monitoring of CO2 Emissions from New Cars, Updated with Data to 1999.

European Conference of Ministers of Transport, Paris, France

Elvik, Rune (2000): How much do road accidents cost the national economy?. Accident

Analysis and Prevention, Vol. 32, No. 6, pp 849-851, 2000

ETSC (1993): Reducing traffic injuries through vehicle safety improvements, the role of car

design. European Transport Safety Council, Brussels, Belgium

ETSC, (1999): Intelligent transport systems (ITS) and road safety. European Transport

Safety Council, Brussels, Belgium

Eurostat (2000): EU transport in figures, statistical pocket book. European Commission,

Energy and Transport DG, Brussels, Belgium

Evans, Andrew W. (2003): Estimating transport fatality risk from past accident data. Accident


Evans, Leonard (1994): Driver injury and fatality risk in two-car crashes versus mass ratio

inferred using Newtonian mechanics. Accident Analysis and Prevention, Vol. 26, No. 5, pp

609-616, 1994

Evans, Leonard, Frick, M.C. (1992): Alcohol’s influence on fatality risk, given that a crash has

occurred. Transportation Research Board, 71st annual meeting, Washington, USA

Evans, Leonard (1991). Traffic Safety and the Driver. Van Nostrand Reinhold, New York,

USA

Evans, Leonard, Frick, M.C. (1987): Seating position in cars and fatality risk. General Motors

Research Laboratories, Research publication 5911, Warren, USA

Evans, Leonard, Wasielewski, P. (1983): Serious or fatal driver injury rate versus car mass in

head-on crashes between cars of similar mass. General Motors Research Laboratories,

Research publication 4480, Warren, USA

Evans, Leonard, Wasielewski, P. (1981): Risky driving related to driver and vehicle

characteristics. General Motors Research Laboratories, Research publication GMR-3897,

Warren, USA

Faiz, Asif (1993): Automotive emissions in developing countries, relative implications for

global warming, acidification and urban air quality. Transportation Research Part A, Vol. 27A,

No. 3, pp 167-186, 1993

Gilbert, R. (2001), Sustainable Transportation, in Gilbert R, Sustainable Transportation. In

Munn T (ed.), Encyclopedia of Global Environmental Change, Volume 5, Wiley, UK.

Green, David L. (1993): Transportation and energy: the global environmental challenge.

Transportation Research Part A, Vol. 27A, No. 3, pp 163-166, 1993

Green, David L., Duleep, K.G. (1993): Costs and benefits of automobile fuel economy

improvement: a partial analysis. Transportation Research Part A, Vol. 27A, No. 3, pp 217-

235, 1993

Hackney, James R., Kahane, Charles J. (1995): The new car assessment program: five star

rating system and vehicle safety performance characteristics. SAE technical paper series

950888, presented at the SAE International Congress and Exposition, Detroit, Feb./March

1995

Hertz, Ellen S. (2002): A note on the variance of paired comparisons estimates. Accident


Hertz, Ellen S. (1997): The Effect of Decreases in Vehicle Weight on Injury Crash Rates.

NHTSA Technical Report, DOT HS 808575, Washington, USA

Johansson, Bengt, Åhman, Max (2002): A comparison of technologies for carbon-neutral

passenger transport. Transportation Research, Part D, Vol. 7, pp 175-196, 2002

Johansson, Östen (2001): Analys av mötesolyckor med dödlig utgång. working paper,

Vägverket, Borlänge, Sweden

Johansson, Östen (2002): Skaderiskbedömning vad gäller nya personbilar vid olyckor i olika

trafikmiljöer 1997/98. working paper, Vägverket, Borlänge, Sweden

Kaeser, Robert, Walz, Felix H., Brunner, Anton (1994): Collision safety of a hard-shell low-

mass vehicle. Accident Analysis and Prevention, Vol. 26, No. 3, pp 399-406, 1994

Kahane, Charles J. (1991): Effect of car size on frequency and severity of roll over crashes.

13th E.S.V. Conference, Paris, France

Kahane, Charles J. (1997): Relationships between Vehicle Size and Fatality Risk in Model

Year 1985-93 Passenger Cars and Light Trucks. NHTSA Technical Report No. DOT HS 808

570, Washington, 1997

Kahane, Charles J. (2003): Vehicle Weight, Fatality Risk and Crash Compatibility of Model

Year 1991-99 Passenger Cars and Light Trucks. NHTSA Technical Report No. DOT HS 809

662, Washington, 2003

Khazzoom, J. Daniel (1994): Fuel Efficiency and Automobile Safety: Single Vehicle Highway

Fatalities for Passenger Cars. The Energy Journal, Vol. 15, No. 4, pp 49-101, 1994

Khazzoom, J. Daniel (1997): Impact of Pay-at-the-Pump on Safety through Enhanced

Vehicle Fuel Efficiency. Energy Journal, Vol. 18, No. 3, pp 103-134, 1997

Ministry of the Environment Sweden (2000): Förslag till Svensk Klimatstrategi. SOU 2000:23,

Nordstedts Tryckeri AB, Stockholm, 2000.

Mizuno, Koji, Yonezawa, Hideki (1996): The effect of vehicle mass in car-to-car collisions.

SAE technical paper series 960441, presented at the SAE International Congress and

Exposition, Detroit, Feb. 1996

National Research Council (1992): Automobile Fuel Efficiency. How Far Should We Go.

National Academy Press, Washington, D.C., USA

National Research Council (2002): Effectiveness and Impact of Corporate Average Fuel

Economy (CAFE) Standards. National Academy Press, Washington, D.C., USA

OECD (1995): Urban Travel and Sustainable Development. European Conference of

Ministers of Transport (ECMT), Paris, France

OECD (2002): Strategies to Reduce Greenhouse Gas Emissions from Road Transport:

Analytical Methods. OECD Publication Service, Paris, France

Page, Yves (2001): A statistical model to compare road mortality in OECD countries.

Accident Analysis and Prevention, Vol. 33, No. 3, pp 317-385, 2001

Pastowski, Andreas, Gilbert, Richard (2003): Civil Aviation and Climate Policy: Status,

Challenges, and Policies from a Trans-Atlantic Comparative Perspective. STELLA Focus

Group 4 Meeting Quebec 26-27 May, 2003

Schimkat, H., Weissner, R. (1978): Passive vehicle safety as cars grow smaller. SAE

technical paper series 780282, presented at the SAE International Congress and Exposition,

Detroit, Feb./March 1978

Shladover, Steven E. (1993): Potential contributions of intelligent vehicle/highway systems

(IVHS) to reducing transportation’s greenhouse gas production. Transportation Research

Part A, Vol. 27A, No. 3, pp 207-216, 1993

Trawén, Anna, Maraste, Pia, Persson, Ulf (2002): International comparison of costs of a fatal

casualty of road accidents in 1990 and 1999. Accident Analysis and Prevention, Vol. 34, No.

3, pp 323-332, 2002

Van den Brink, Robert M.M., van Wee, Bert (2001): Why has car-fleet specific fuel

consumption not shown any decrease since 1990? Quantitative analysis of Dutch passenger

car-fleet specific fuel consumption. Transportation Research Part D, Vol. 6, pp. 75-93, 2001

Watterson, W.T. (1993): Linked simulation of land use and transportation systems:

developments and experience in the Puget sound region. Transportation Research Part A,

Vol. 27A, No. 3, pp 193-206, 1993

WHO (2001): A 5-Year WHO Strategy for Road Traffic Injury Prevention. World Health

Organisation, Geneva, Switzerland

Wood, Denis P. (1997): Safety and the Car Size Effect: A Fundamental Explanation.

Accident Analysis and Prevention, Vol. 29, No. 2, pp.139-151, 1997

Wood, Denis P., Simms, Ciaran K. (2002): Car size and injury risk: a model for injury in

frontal collisions. Accident Analysis and Prevention, Vol. 34, No. 1, pp 93-99, 2002

Internet sources:

European Commission, Energy and Transport DG: Halving the number of road accident

victims in the EU by 2010: A shared responsibility, European road safety action programme,

Memo: http://europa.eu.int/comm/transport/road/library/rsap/memo_rsap_en.pdf

SIKA: http://www.sika-institute.se/statistik_fr.html

Personal contacts:

Östen Johansson, Lennart Larsson employees at Sweden’s Road Authority (Vägverket) in

Borlänge

Appendix A Written MATLAB scripts

import_a_p.m import_acc.m import_driv.m import_veh.m

matrices.m

plot_1.m

control_var_vkm.m

import of raw data from CD

pre-

stud

y matrix of animal and pedestrian accidents is formed; first plots show comparisons to Sweden’s car registrations

correlation analysis

imp2_acc.m imp2_driv.m imp2_veh.m

reductions.m

import of raw data from CD

final

ana

lysi

s

matrices containing single vehicle accidents and frontal collisions of passenger cars (1999) are formed

matrices_sm99.m

Ana_single.m

Ana_single_reg.m

Ana_front.m

Ana_front_reg.m

analyses of single vehicle accidents (simple figures and regression)

analyses of frontal collision accidents (simple figures and regression)

Appendix B Variables in the accident data base

(list provided by Lennart Larsson Vägverket, Borlänge, Sweden)

Uttag av trafikolycksdatabasen som textfil

Uttag av databas kan göras via program oly204 i vägverkets ärendehanterare Nedan visas vilka fält som finns på de olika filerna, som tagits fram till externa användare. Urvalet avser ALLA FÄLT. Sidorna är avsedda att följa med databasuttaget. Alla fält på filerna är deklarerade som alfanumeriska oavsett fältutseende i databasen. Mellan varje fält finns en avskiljare i form av semikolon (;). Samtliga termer (utom polisområde och hemvist) avser förhållandet vid olyckstillfället. I förteckningen nedan är fälten sorterade efter hur de är lagrade i databasen. --------------------------------------------------------------------------------------------------------------------- FIL : OLYCKA.DAT TABELL: OLYCKA Olycka --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet ANTELEMENT 4 antal trafikelement ATRAF_AXP 9 trafikflöde [axelpar per årsdygn] ATRAF_FORDON 9 " --- [fordon per årsdygn] ATRAF_TUNG 9 " --- [tunga fordon per årsdygn] BELYSNING 4 vägbelysning BROOLYCKA 4 broolycka DELSTRAAKNR 4 delstråknummer DOEDADE 4 antal dödade personer EJSVAENG 4 vänstersvängsförbud FOELJDOLYCKA 4 följdolycka HASTIGHET 4 hastighetsbegränsning [km/h] KOMMUN 4 kommun [kod] KONFLIKTTYP 4 konflikttyp KORSNTYP 4 korsningstyp KVAEGKAT 4 vägkategori för anslutande väg LISKADADE 4 antal lindrigt skadade personer LJUS 4 ljusförhållande MBREDD 4 mittremsebredd [dm] OLANDRDATUM 6 ändringsdatum, ÅÅMMDD OLKLOCKSLAG 4 klockslag OLPLATSTYP 4 platstyp OLREGDATUM 6 registreringsdatum, ÅÅMMDD OLVECKODAG 4 veckodag OLYCKSDATUM 9 olycksdatum, ÅÅÅÅMMDD OLYCKSTYP 4 olyckstyp ORT 25 ort / stadsdel POLISDNR 12 polisens diarienummer POLISDISTR 4 polisdistrikt SLITLAGER 4 slitlager

STOPP 4 stoppskyldighet STRAAKNUMMER 4 stråknummer STRAAKTYP 4 stråktyp SVSKADADE 4 antal svårt skadade personer TRAFIKBEBYGG 4 bebyggelsetyp TRAFIKSIGNAL 4 trafiksignal TUNNELOLYCKA 4 tunnelolycka VAEDER 4 väderlek VAEGARBETE 4 vägarbete VAEGHAALLARE 4 väghållare VAEGKAT 4 vägkategori VAEGLAG 4 väglag VAEGNR 9 vägnummer VAEGTYP 4 vägtyp VAEJNING 4 väjningsskyldighet VBREDD 4 vägbredd [dm] VILTSTAENG 4 viltstängsel LAEN 4 län REGION 4 väghållningsregion SVAARIGHET 4 svårhetsgrad OLMAANAD 4 månad OLAAR 4 år POLISOMRÅDE 4 polisområde (vid uttagstillfället) VINTVAEGHALL 4 vinterväghållningsstandardklass --------------------------------------------------------------------------------------------------------------------- FIL : OLYCKSU.DAT TABELL: OLYCKSUPP Olycksuppgift ---------------------------------------------------------------------------------------------------------------------

Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet UPPGIFTNR 4 olycksuppgiftsnummer KODF 9 olycksomständighet OUPPGIFTTERM 4 olycksuppgift --------------------------------------------------------------------------------------------------------------------- FIL : GAAENDE.DAT TABELL: GAAENDE Gående --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet ANTPERS 4 antal personer i trafikelementet FRVBNR 4 frånvägbensnummer OEVERGAANG 4 gång- / cykel- / mopedanläggning PRIMELEMENT 4 primär- / sekundärelement ROERELSETYP 4 rörelsetyp TIVBNR 4 tillvägbensnummer TRAFELEMENT 4 trafikelementnummer TRAFELEMTYP 4 trafikelementtyp

--------------------------------------------------------------------------------------------------------------------- FIL : Y_OLY.DAT TABELL: Y_OLY Olycksplats – ej vdb --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet AVST 9 avstånd från referensort / -gata A [m] REFPA 25 referensort / -gata A REFPB 25 referensort / -gata B OLPLATSUPPG 25 platsuppgift OLVG 25 olycksväg / -gata ADRESSNR 4 adressnummer å olycksväg / -gata X_KOORD 9 x-koordinat Y_KOORD 9 y-koordinat Z_KOORD 9 z-koordinat --------------------------------------------------------------------------------------------------------------------- FIL : Z_OLY.DAT TABELL: Z_OLY Olycksplats - vdb --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet DATF 9 olyckans datum KNP 9 knutpunkt för olycka i knutpunkt KNPA 9 knutpunkt A för olycka på länk KNPB 9 knutpunkt B för olycka på länk LDATF 9 länkens födelsedatum SEKT 9 sektion från A [m] LROLL 4 länktyp OLVG 25 olycksväg/-gata X_KOORD 9 x-koordinat Y_KOORD 9 y-koordinat Z_KOORD 9 z-koordinat --------------------------------------------------------------------------------------------------------------------- FIL : PASSAGER.DAT TABELL: PASSAGERARE Passagerare / instruktör --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet TRAFELEMENT 4 trafikelementnummer PASSAGERARNR 4 person nummer AALDER 4 ålder [år] INSTRUKTOER 4 instruktör vid övningskörning KOEN 4 kön PASSPLATS 4 placering på/i fordon SKADEGRAD 4 skadegrad HANDLEDGOD 4 handledargodkännande TRAFKAT 4 trafikantkategori

Har 'N' angivits för körkortsuppgifter finns enbart följande fält:

OLYCKSID 9 olycksidentitet TRAFELEMENT 4 trafikelementnummer PASSAGERARNR 4 person nummer AALDER 4 ålder [år] INSTRUKTOER 4 instruktör vid övningskörning KOEN 4 kön PASSPLATS 4 placering på/i fordon SKADEGRAD 4 skadegrad TRAFKAT 4 trafikantkategori --------------------------------------------------------------------------------------------------------------------- FIL : FOERARE.DAT Rev. 1998-12-10 TABELL: FOERARE Förare --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet TRAFELEMENT 4 trafikelementnummer AALDER 4 ålder [år] BEHOERDATUM 9 ursprungligt utfärdandedatum för körkortsklass vid olyckstillfället BEHOERIGHET 4 körkortsklass vid olyckstillfället KOEN 4 kön KOERKORTINDR 9 körkort indraget, datum SKADEGRAD 4 skadegrad (TRAKTORKORT 4 traktorkort – termen har utgått) UTBYTTUTL 4 utbytt utländsk körkort VARNING 9 körkortsvarning, datum TAXIBEHOER 9 taxiförarlegitimation utfärdad, datum FOERARHEMV 9 förarhemvist [postnummer] (vid hämtningstillfället) PASSPLATS 4 placering - för förare alltid förarplats (1) INSTRUKTOER 4 instruktör HANDLEDGOD 4 handledargodkännande U_BEHOERIG 4 1:a körkortsklass exklusive traktorbehörighet U_BEHOERDAT 9 1:a körkort ursprungligen utfärdat, datum INNEHAVSTID 9 körkortsinnehavstid [månader] TRAFKAT 4 trafikantkategori Ł does it equal trafikelementtyp? Har 'N' angivits för körkortsuppgifter finns enbart följande fält: OLYCKSID 9 olycksidentitet TRAFELEMENT 4 trafikelementnummer AALDER 4 ålder [år] KOEN 4 kön SKADEGRAD 4 skadegrad FOERARHEMV 9 förarhemvist [postnummer] (vid hämtningstillfället) PASSPLATS 4 placering – för förare alltid förarplats TRAFKAT 4 trafikantkategori

--------------------------------------------------------------------------------------------------------------------- FIL : DJUR.DAT TABELL: DJUR Djur --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet PRIMELEMENT 4 primär- / sekundärelement TRAFELEMENT 4 trafikelementnummer TRAFELEMTYP 4 trafikelementtyp --------------------------------------------------------------------------------------------------------------------- FIL : VBEN.DAT TABELL: VBEN Vägben --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet VBENNR 4 vägbensnummer ANSLTYP 4 anslutningstyp KNPA 9 knutpunkt A för länk KNPB 9 knutpunkt B för länk LDATF 9 länks födelsedatum RIKTNING 4 riktning med / mot länk SEKT 9 sektion från A [m] REFPA 25 referensort / -gata A REFPB 25 referensort / -gata B LROLL 4 länktyp --------------------------------------------------------------------------------------------------------------------- FIL : MOTORF.DAT Rev. 1998-12-10 TABELL: MOTORFORDON Motordrivet fordon --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet TRAFELEMT 4 trafikelementnummer AARSMODELL 4 årsmodell ANTPERS 4 antal personer ANTSLAEP 4 antal släp BESIKTNDATUM 9 besiktningsdatum BESIKTNSTAT 4 besiktningsstatus BREDD 4 fordonsbredd [cm] DRIVMEDEL 4 drivmedel EFFEKTNORM 4 motoreffektnorm EKIPAGELGD 4 ekipagelängd [cm] EKIPAGEVIKT 9 ekipagevikt [kg] FABRIKAT_TYP 24 fabrikat och typ FAERG 4 färg FOERSBETALD 4 försäkring betald FORDONAEGARE 4 fordonsägare FORDONNATION 4 fordonsnation FRVBNR 4 frånvägbensnummer

KAROSSERIKOD 4 karosserikod KOPPLINGAVST 4 kopplingsavstånd (cm) LAENGD 4 fordonslängd [cm] LEASINFORDON 4 leasingfordon MAXPASS 4 maxantal passagerare MODELLKOD 6 modellkod MOTOREFFEKT 4 motoreffekt [kW] ROERELSETYP 4 rörelsetyp PRIMELEMENT 4 primär- / sekundärelement STULET 4 stöldanmält fordon TIVBNR 4 tillvägbensnummer TJVIKT 9 tjänstevikt [kg] TOTALVIKT 9 totalvikt [kg] TRAFELEMTYP 4 trafikelementtyp UTRYCKNING 4 utryckningsfordon / taxi VAEXELLAAD 4 växellåda YRKESTRAFKOD 4 yrkestrafiktillstånd FORDONSTATUS 4 fordonsstatus CYLINDERVOLYM 4 cylindervolym (cm3) AXELANTAL 4 axelantal EKIPAGEAXL 4 ekipageaxlar fordon + släp GRUPPKOD 9 gruppkod KROCKKUDDE 4 krockkudde för framsätespassagerare HANDIKAPPANP 4 handikappanpassat fordon Har 'N' angivits för fordonstekniska uppgifter finns enbart följande fält: OLYCKSID 9 olycksidentitet TRAFELEMENT 4 trafikelementnummer ANTPERS 4 antal personer ANTSLAEP 4 antal släp FORDONNATION 4 fordonsnation FRVBNR 4 frånvägbensnummer ROERELSETYP 4 rörelsetyp PRIMELEMENT 4 primär- / sekundärelement TIVBNR 4 tillvägbensnummer TRAFELEMTYP 4 trafikelementtyp --------------------------------------------------------------------------------------------------------------------- FIL : OEVRIGF.DAT TABELL: OEVRIGFORDON Övriga fordon --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet ANTPERS 4 antal personer i trafikelementet FRVBNR 4 frånvägbensnummer ROERELSETYP 4 rörelsetyp PRIMELEMENT 4 primär- / sekundärelement TIVBNR 4 tillvägbensnummer TRAFELEMENT 4 trafikelementnummer TRAFELEMTYP 4 trafikelementtyp

--------------------------------------------------------------------------------------------------------------------- FIL : CY_MOPED.DAT TABELL: CYKEL_MOPED Cykel och moped --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet ANTPERS 4 antal personer i trafikelementet FRVBNR 4 frånvägbensnummer OEVERGAANG 4 gång- / cykel- / mopedanläggning ROERELSETYP 4 rörelsetyp PRIMELEMENT 4 primär- / sekundärelement TIVBNR 4 tillvägbensnummer TRAFELEMENT 4 trafikelementnummer TRAFELEMTYP 4 trafikelementtyp --------------------------------------------------------------------------------------------------------------------- FIL : FORDONSH.DAT TABELL: FORDONSHAEND Fordonshändelser --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet TRAFELEMENT 4 trafikelementnummer HAENDNR 4 händelsenummer HAENDELSE 4 händelse SIDA 4 händelseriktning / -sida / -läge --------------------------------------------------------------------------------------------------------------------- FIL : SLAEP.DAT TABELL: SLAEP Släpfordon --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet SLAEPNR 4 släpnummer AARSMODELL 4 årsmodell TRAFELEMENT 4 trafikelementnummer BESIKTNDATUM 9 besiktningsdatum BESIKTNSTAT 4 besiktningsstatus FABRIKAT_TYP 24 fabrikat KAROSSERIKOD 4 karosseri KOPPLINGAVST 4 kopplingsavstånd [cm] MODELLKOD 6 modellkod TJVIKT 9 tjänstevikt [kg] TOTALVIKT 9 totalvikt [kg] AXELANTAL 4 axelantal GRUPPKOD 9 gruppkod Har 'N' angivits för fordonstekniska uppgifter finns enbart följande fält: OLYCKSID 9 olycksidentitet TRAFELEMENT 4 trafikelementnummer

--------------------------------------------------------------------------------------------------------------------- FIL : FORDONSU.DAT TABELL: FORDONSUPPG Elementuppgift --------------------------------------------------------------------------------------------------------------------- Fältnamn Längd Klartext OLYCKSID 9 olycksidentitet UPPGIFTNR 4 elementuppgiftsnummer TRAFELEMENT 4 trafikelementnummer KODF 9 elementomständigheter UPPGIFTTERM 4 elementuppgift --------------------------------------------------------------------------------------------------------------------- (Slut på listan)

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