Road Safety Development Index: Theory, Philosophy and Practice

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Linköping Studies in Science and Technology

Dissertation No. 1100

Road Safety Development Index (RSDI)

Theory, Philosophy and Practice

Ghazwan Al-Haji

Department of Science and Technology

Linköping University, SE-601 74, Norrköping, Sweden

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Dissertation Number: 1100 ISBN: 978-91-85715-04-6 ISSN: 0345-7524

Printed by:

LiUTryck, Linköping, Sweden, 2007 Distributed by:

Linköping University

Department of Science and Technology (ITN) Campus Norrköping

SE-601 74, Norrköping, Sweden Tel: +46 11 36 30 00

Fax: +46 11 36 32 70 http://www.itn.liu.se

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ABSTRACT

This dissertation develops, presents and tests a new international tool, the so-called Road Safety Development Index (RSDI), which indicates in a comprehensive and easy way the severity of the road safety situation in a specific country and/or in comparison with other countries. There are three pillars of outcomes involved in the framework of RSDI. One pillar is the People focus (road user behaviour). The second is the System focus (safer vehicles, safer roads, enforcement, management, etc). The third is the Product focus in terms of accident death rates. This thesis analyses each of these pillars. In addition, RSDI links the key national practices of road safety to each other and to the end-results (accident death rates). The study suggests a master-list of performance indicators to be implemented for assessing road safety level in a country and for RSDI building. Based on the “master-list”, a short key list of performance indicators is chosen and classified into two primary categories that correspond to two groups of countries: LMCs “Less Motorised Countries” and HMCs “Highly Motorised Countries”. RSDI aggregates the key performance indicators into one single quantitative value (composite index). Four main objective and subjective approaches are used to calculate RSDI and determine which one is the best. One approach uses equal weights for all indicators and countries, whereas the other approaches give different weights depending on the importance of indicators. Two empirical studies were carried out, in different parts of the world, to determine the applicability of this tool in real world applications. The first empirical study comes from eight European countries (HMCs). The second empirical study comes from five Southeast Asian countries (LMCs). The RSDI results from this study indicate a remarkable difference between the selected countries even at the same level of motorisation and/or with close accident death rates. The unavailability of comparable and useful data are problems for deeper analysis of RSDI, especially the index should be as relevant as possible for different parts of the world. The empirical and theoretical assessments prove that RSDI can give a broader picture of the whole road safety situation in a country compared to the traditional models and can offer a simple and easily understandable tool to national policy makers and public.

Key Words: Road safety, RSDI, international benchmarking, national development, policy makers, ranking, composite indices, key performance indicators, macro- models.

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SAMMANFATTNING

Denna avhandling utvecklar, presenterar och testar ett nytt internationellt verktyg, det så kallade Road Safety Development Index (RSDI), vilket på ett begripligt och lättillgängligt sätt beskriver trafiksäkerhetsläget i ett visst land jämfört med andra länder. Resultatet av RSDI utgörs av tre grundpelare. Den första pelaren är Fokus på människor (vägtrafikbeteende). Den andra är Fokus på systemet (säkrare fordon, säkrare vägar, beivrande, management, osv). Den tredje pelaren är Fokus på produkten med avseende på antal döda per fordon och per invånare. Arbetet analyserar var och en av dessa tre pelare. RSDI kopplar dessutom samman de viktigaste nationella praxisarna och erfarenheterna med varandra och till slutresultaten (antal dödsfall). Studien föreslår en lista med de viktigaste indikatorerna på hur olika länder vidtar åtgärder för trafiksäkerheten. Grundat på denna “master-lista” kan en kort lista med de viktigaste indikatorerna skapas och klassificeras i två huvudkategorier för två typer av länder: LMC “länder med låg andel fordon” och HMC “länder med hög andel fordon”. RSDI aggregerar de viktigaste performance-indikatorerna till ett enda kvantitativt mått (ett sammansatt index). Fyra olika objektiva och subjektiva huvudangreppssätt används för att beräkna RSDI och bestämma vilket av dem som är det bästa. En metod använder sig av lika stora vikter för alla indikatorer och länder, medan en annan metod ger olika vikter beroende på indikatorernas betydelse. Två empiriska studier genomfördes i olika delar av världen för att bestämma tillämpligheten av detta verktyg i verkliga situationer. Den första empiriska studien kommer från åtta länder i Europa (HMC-länder). Den andra empiriska studien har gjorts i fem länder i Sydostasien (LMC-länder). Resultaten från detta RSDI tyder på en anmärkningsvärd skillnad mellan de valda länderna, också om andelen bilägare och/eller andra variabler för trafiksäkerhet hålls konstanta. Bristen på jämförbara och användbara data medför problem vid en djupare analys av RSDI för olika delar av världen. De empiriska och teoretiska skattningarna visar att RSDI kan ge en bredare bild av hela trafiksäkerhetssituationen i ett land jämfört med traditionella modeller och kan erbjuda ett enkelt och lättförståeligt verktyg för de nationella beslutsfattarna liksom för allmänheten.

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Acknowledgments

The dissertation has now reached the end of a long and enjoyable journey. On this occasion I would like to acknowledge many people for their help along the way.

First and foremost, my deepest gratitude is to my thesis advisor, Prof. Kenneth Asp, for his constant support, encouragement and advice during my doctoral studies. It was a privilege to work with you and benefit from your broad knowledge, management and international experience. Next, I would like to express my special thanks to Prof. Jan Lundgren who has reviewed my thesis and enriched it with his insightful comments and valuable advices. Thank you also for facilitating everything to finish my thesis.

My sincere appreciation goes to my projects members and colleagues. To Per Lindskog, Malin Eriksson, Ing-Marie Eriksson, Johanna Emilsson and Lars Ohlsson. We shared offices, discussions, projects, papers, conferences and success. Thank you for a great time and mutual experience! I am grateful to Prof. Kåre Rumar, for his inspiring ideas and fruitful discussions, especially during the RetsNet project work. To Arne Karyd, my office mate, and Somharutai Bootjan for our interesting discussions. To Di Yuan for your review and comments on my dissertation during slutseminarium “Final seminar”. To Åke Sivertun and Imad Ali from IDA department for our joint works within the projects Spider and Globesafe. My acknowledgement goes also to Prof Christer Hydén and Dr. Åse Svensson at Lund University for their feedback and constructive suggestions on my thesis work. I would also like to thank the administrators and directors of the ITN department for your great kind help in different matters.

This thesis has also profited from the collaboration with international institutions such for instance ADB (Asian Development Bank) and GRSP (Global Road Safety Partnership) during ASNet project. I would specifically like to mention the following consultants: Charles M. Melhuish (ADB), Alan Ross (ADB), Michael Goodge (ADB), David Silcock (GRSP) and Andrew Downing (GRSP). My thanks also go to several people from overseas whom I met through the projects: RetsNet, ASNet, TechTrans and Spider. Special thanks to Prof. Valentine Silyanov and Sr. Scientist Anatolyi Utkin at the State Technical University (MADI) in Moscow, Russia; to Prof. Heru Sutomo and Dr. Arif Wismadi at Gadja Mada University, Yogyakarta, Indonesia; and to Prof. Nguyen Xuan Dao and Ms. Trinh Thuy Anh at the University of Communication and Transportation, Hanoi, Viet Nam. Thank you for making our joint work and applications interesting and valuable.

My deepest gratitude and love go to my parents in Syria, my brothers and sisters and their families, for supporting me at all times. I express my sincere thanks to my older sister Nadia for her frequent contact. Last, but certainly not least, I would like to thank my beloved wife Mai for her encouragement and support, and our son Aghyad who has enriched my Swedish vocabulary with his new words he learned from Kindergarten and who often took my place in front of the computer.

Norrköping, Sweden, March 2007 Ghazwan Al-Haji

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List of Abbreviations

ADB Asian Development Bank

ASEAN Association of South East Asian Nations

ASNet Regional Traffic Safety Network to Ten South East Asian Countries

EU European Union

GDP Gross Domestic Product

Globesafe Global Road Safety Database GRSP Global Road Safety Partnership HDI Human Development Index HMCs Highly Motorised Countries KPIs Key Performance Indicators LMCs Less Motorised Countries NGO Non-Governmental Organisation

OECD Organisation For Economic Cooperation and Development RSDI Road Safety Development Index

SIDA The Swedish International Development Agency UN United Nations

UNDP United Nations Development Programme VRU Vulnerable Road Users

WB World Bank

WDI World Development Indicators, World Bank WHO World Health Organisation

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List of Figures

FIGURE 1.1:MOTORISATION, PERSONAL RISK AND TRAFFIC RISK IN DIFFERENT REGIONS...3

FIGURE 1.2:RESEARCH APPROACH FOR PROMOTING BOTH THEORY AND PRACTICE...8

FIGURE 1.3:STRUCTURE OF THE THESIS...11

FIGURE 2.1:EVOLUTION OF ROAD SAFETY BENCHMARKING TOWARDS INTEGRATED BENCHMARKING...21

FIGURE 2.2:THE INFLUENCING FACTORS ON THE DEVELOPMENT CURVE OF ROAD SAFETY...24

FIGURE 2.3:ILLUSTRATION OF ROAD SAFETY PROFILES...29

FIGURE 3.1:THE PYRAMID OF ROAD SAFETY INDICATORS AND LEVELS OF AGGREGATION...38

FIGURE 3.2:ROAD SAFETY PROBLEM DESCRIBED BY THREE-DIMENSIONAL CUBE...42

FIGURE 3.3:THE SELECTED DIMENSIONS IN ROAD SAFETY...51

FIGURE 3.4:CRITERIA TO IMPROVE INDICATOR QUALITY AND IMPORTANCE. ...57

FIGURE 4.1:RSDI CONCEPTUAL FRAMEWORK (OVERALL ROAD SAFETY PERFORMANCE)...64

FIGURE 4.2:THE INPUT-OUTPUT-OUTCOME-RSDI FRAMEWORK...67

FIGURE 4.3:SHARING THE LONG-TERM VISION OF RSDI...74

FIGURE 5.1:AN ILLUSTRATION OF THE COMBINED AGGREGATED RESULTS WITH WEIGHTS INTO RSDI...89

FIGURE 5.2:AN EXAMPLE OF ILLUSTRATION OF THE THREE RSDI LEVELS AND DIMESNIONS...91

FIGURE 5.3:AN EXAMPLE OF ILLUSTRATION OF THE THREE RSDI LEVELS AND DIMESNIONS...91

FIGURE 6.1:SCORE PLOT OF THE FIRST TWO PRINCIPAL COMPONENTS...103

FIGURE 7.1:SCORE PLOT OF THE NEW SCORES OF PC1 AND PC2 IN THE SAMPLE OF COUNTRIES...111

FIGURE 8.1:COMPARING THE RSDI SCORES BETWEEN THE SELECTED SAMPLES OF COUNTRIES...116

FIGURE 8.2:AN ILLUSTRATION OF THE DIFFERENCES BETWEEN VARIOUS PILLARS OF RSDI ...118

FIGURE 8.3:AN ILLUSTRATION OF THE DIFFERENCES BETWEEN VARIOUS DIMENSIONS OF RSDI ...119

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List of Tables

TABLE 1.1:THE RELATION BETWEEN THE RESEARCH QUESTIONS, STRATEGIES AND ANSWERS...9

TABLE 3.1:MACRO-FACTORS INFLUENCING EXPOSURE, RISK AND CONSEQUENCES...48

TABLE 3.2:THE MASTER LIST OF MACRO ROAD SAFETY INDICATORS AND DIMENSIONS...56

TABLE 4.1:APPLYING ECONOMIC PERFORMANCE PRINCIPLES TO RSDI ...69

TABLE 4.2:THE SUGGESTED LIST OF KEY PERFORMANCE INDICATORS FOR LMCS AND HMCS...77

TABLE 5.1:THE SUBJECTIVE WEIGHTING RESULTS IN A NUMERICAL SCALE OF RSDI ...87

TABLE 5.2:ALTERNATIVE CHOICES OF WEIGHTING THE PILLARS OF RSDI...88

TABLE 6.1:ROAD SAFETY DATA AND KEY INDICATORS FOR THE SELECTED HMCS IN EU...97

TABLE 6.2:SELECTING THE WEIGHTS OF THE INDICATORS OF RSDI...99

TABLE 6.3:RSDI SCORES USING SIMPLE AVERAGE TECHNIQUE AND SUBJECTIVE THEORIES...100

TABLE 6.4:THE EIGENVALUE ANALYSIS OF THE NORMALISED INDICATORS...101

TABLE 6.5:THE ADJUSTMENT PROCEDURES OF PCA WEIGHTS TO MATCH THE SCALE OF THE RSDI ...102

TABLE 6.6:THE RSDI SCORES AND RANKS FROM THE EMPIRICAL ANALYSIS (1) ...104

TABLE 7.1:THE KEY PERFORMANCE INDICATORS AND DIMENSIONS IN THE SELECTED COUNTRIES...107

TABLE 7.2:SELECTING THE WEIGHTS OF THE INDICATORS OF RSDI...109

TABLE 7.3:RSDI SCORES USING SIMPLE AVERAGE TECHNIQUE AND SUBJECTIVE THEORIES...110

TABLE 7.4:EIGENVALUE ANALYSIS OF THE COVARIANCE MATRIX OF NORMALISED VALUES...111

TABLE 7.5:THE ADJUSTMENT PROCEDURES OF PCA WEIGHTS TO MATCH THE SCALE OF THE RSDI ...112

TABLE 7.6:THE RSDI SCORES AND RANKS FROM THE EMPIRICAL ANALYSIS AND APPROACHES...112

TABLE 8.1:THE RSDI SCORES AND RANKS FROM THE TWO EMPIRICAL APPLICATIONS...114

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1 Chapter 1: Introduction

Chapter 1

Introduction

The purpose of this introductory chapter is to provide the reader with a brief background of this dissertation research and to define the purposes as well as the research questions. Furthermore, the outline of the dissertation is presented.

1.1 Background – the road safety problem

It is a common perception among the public that road accidents are a problem, resulting in death, injury or property damage. Unfortunately, many people do not fully realise the size of the problem. It is clear that while many people, especially in the developing countries, have the general idea that driver error is the main cause of the problem, they have no idea that a several causes and factors contribute to the problem as well. Their understanding, in most cases, is limited because they have no clear measurement(s) that can show them the size of the problem in a simple and adequate way.

When a policymaker decides which actions those need to be taken nationally, it has to be based on some sort of statistical measurements. When road users want to know their accident risks, this should also be based on understandable statistical measurements. Unfortunately, most present measurements that are used to address the scale of road safety problem in a country or city are mainly based on death rates (deaths per vehicle or per person). These rates are often too complex to be understood by ordinary people and, in some cases, by policy makers since the scale of these measurements is not uniform, and vary from one study to another and the results are mostly in a decimal number. Furthermore, the death rates say little about achievement of a country or its progress towards a certain goal. Therefore, it has always been of interest to researchers to develop a measurement that provides public and policy makers with a clear understanding of the causes and magnitude of traffic accidents in their countries.

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1.1.1 Road safety is a global problem

As the number of motor vehicles is continuously increasing globally, and road traffic accidents are causing more and more deaths and injuries. The World Health Organisation (WHO) statistics (Peden M et al., 2004) have shown that almost 1.26 million people are killed in road accidents each year worldwide and an additional 50 million people are estimated injured. Nearly half of them are seriously injured or disabled. Due to the unreliability and under-reporting of data in most countries, these figures are still under-estimated. Road accidents are the eighth leading cause of death in the world today, and the WHO (WHO, 2004) estimates they will become the world’s third leading cause of death by the year 2020 if no effective actions and efficient measures are taken.

Annually, the national cost of road accidents is estimated between one and three percent of a country’s Gross National Product (GNP). This cost is a considerable waste of resources and it also has negative effects on the development of every country, and especially in the low-income countries.

All counties suffer from the road accident problem. Yet the size of the problem is different from one country to another, because countries vary widely in their development levels, road safety systems and experiences. According to Jacobs et al. (2000), the majority of road deaths and injuries occur mostly in developing and transitional countries. Highly Motorised Countries (HMCs) have sixty percent of the total motor vehicle fleet but their contribution of the total global road accident deaths is only fourteen percent. Several studies (i.e. OECD, 2002a) have shown that the total number of road deaths in HMCs has been declining or stabilising during recent decades, whereas the situation in in Less Motorised Countries (LMCs) remains severe and the total number of deaths continues to increase.

Al-Haji (2001) has performed an international comparative study across different regions in terms of motorisation (vehicles per person), personal risk (deaths per person), and traffic risk (deaths per vehicle). There it was stated (Figure 1.1) that highly developed countries have the lowest risk records and high motorisation, while Africa has the lowest motorisation and a high traffic risk. Southeast Asia, Africa and the Middle East run the highest risk of being killed in terms of personal safety. However, the study recommended that such comparison should not be taken too seriously, since there are differences within the same region concerning for instance: motorisation, population, education, health, investment level on road safety measures, etc. In order to achieve adequate results in comparisons, international comparisons have to be carried

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out between similar countries or regions at the same level of development, motorisation and with similar type of transport system as much as possible.

0 10 20 30 40 50 60 70

Motorisation Personal Risk Traffic Risk

Developed Countries (5) Eastern Europe (2) South East Asia (5) Middle East (5) Latin America (3) Africa (6)

Notes: Motorisation is taken in this figure as the number of vehicles per 100 persons. Personal Risk is deaths per 100,000 inhabitants.

Traffic Risk is deaths per 10,000 vehicles. ( ) = Number of selected countries.

Figure 1.1: Motorisation, personal risk and traffic risk in different regions in 1995 (Al-Haji, 2001)

Additionally, the characteristics and nature of the road safety problem differs between countries. For instance, the majority of road accident injuries and deaths in LMCs are the vulnerable road users (pedestrians, cyclists, motorcyclists), whereas, due to the high number of car owners, car occupants account for most of the victims in HMCs (Asp et al., 1998). Therefore, road safety priorities are different from country to country and from region to region in accordance to their accident characteristics, nature, causes and challenges.

1.1.2 International benchmarking of road safety

International benchmarking as a term has widely been used in many fields of research. There are many examples of applications of international benchmarking and ranking between countries, ranging from health care (i.e. NHS, 1999), higher education (i.e. CHEMS, 1998), innovation (i.e. IRE, 2005), business management (i.e. Spendolini, 1992), management in government (Pollitt et al, 1994), public sector (i.e. Dorch & Yasin, 1998), and many more. The main idea of benchmarking is to compare achievements between countries (or organisations) and to learn from each other.

There are several definitions of benchmarking, depending on the type of activity and the target group. In general, these definitions deal with some common topics such as:

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comparison, sharing information and best practices. Two examples of definitions are highlighted below:

“Benchmarking is a powerful technique that provides practical learning through comparing measurements, policies or outcomes, across industries, sectors, policies, products or services. The essence of benchmarking is the process of identifying the highest standards of excellence for products, services or processes and then making the improvements necessary to reach those standards.” (IRE, 2005).

“A continuous systematic process for evaluating the products, services and work of organisations that are recognised as representing best practices for the purpose of organisational improvement” (Spendolini, 1992).

Similarly, in terms of road safety, many countries recognise the importance of international benchmarking to measure their own achievements with similar countries or countries that have already passed through similar stages of challenges and development. This comparison allows countries to identify their problems and improve their performance in road safety based on existing practices and lessons in other countries. In general, these benchmarking models intended to answer:

• Which country performs better than another?

• Why is a specific country more successful than others?

• How and what measures a successful country has used to improve its road safety work.

• What actions have to be taken to improve road safety performance in a country in future?

A number of benchmarking models in road safety has already being developed and they range from relatively simple to highly complex models depending on the number of indicators involved, details of data and complexity of methods used in calculations and analysis. These benchmarking models in road safety can be classified into four broad categories as follows (Al-Haji & Asp, 2006b):

1. Product Benchmarking is used to compare death rates.

2. Practices Benchmarking is used to compare activities related to human-vehicle-road performance (e.g. seat belts use, crash helmets use, motorways level, etc.) 3. Strategic Benchmarking is used to compare National Road Safety Programme

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4. Integrated Benchmarking is used to compare countries in terms of the three previous types of benchmarking altogether. Road safety performance in a country is seen and perceived within a holistic context.

Although numerous research and applications have been carried out in the first three types in macro benchmarking of road safety, little research has investigated the fourth type (Integrated Benchmarking). The major possible obstacles in constructing an integrated benchmarking model are:

• Misunderstanding what integrated benchmarking means. • The lack of data from different countries, especially in LMCs.

• May be very expensive in terms of money and the time taken to collect and analyse data from many countries.

However, today data is more accessible. Faster computers are developing rapidly as well, which simplifies the work and analysis of a large amount of road safety data that was not available before. This development makes the work in the integrated benchmarking easier, bringing it closer to reality.

Additionally, the term “Sustainable Development” has become more popular and applied in different sectors of research. This term simply means integrating several efforts at the same time for maximising the development of a specific sector. Examples are “Sustainable Transport”, “Sustainable Environment” and “Sustainable Health Care”. To date, little research has reported on “Sustainable Road Safety Development”, which needs to be emphasised in research. One of the major challenges to sustain road safety in a country is that the traditional measurements used regarding the first three types of benchmarking are not powerful enough to model the complexity of road safety situation in a country. Therefore, an integrated benchmarking tool can contribute to a sustainable improvement in road safety in the country and bring all relevant concepts together. Van Vliet & Schermers (2000) is one of few studies that examined the issue of sustainable road safety by developing an integrated national strategy in the Netherlands.

The benchmarking models in general are mostly based on two types of measurements, from a statistical point of view, for the overall performance of a country. The first type is to develop a set of national Key Performance Indicators (KPIs) that measure the country performance from different aspects; and the second type is the composite indices that combine many key indicators KPIs into a single value.

There is a considerable number of studies (beyond road safety sector) that has highlighted the importance and usefulness of having composite indices as a tool for

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making integrated benchmarking, (see the review of Saisana & Tarantola, 2002 and Andrew, 2004). Many composite indices have been developed internationally and used in different aspects of life to indicate progress or achievements between countries. Such examples are: Human Development Index (HDI), which was developed by the United Nations and the Overall Health System Index by the World Health Organisation. Business, in general, has also recognised the importance of having a multi-dimensional index for managing, assessing, controlling and sustaining the performance of the company/organisation (Ahmed & Rafiq, 1998). This has led to the development of quality benchmarking and excellence models, which are today quite popular and are widely accepted and used in modern economic and business benchmarking research at national and international levels. Such examples are Total Quality Management and the European Foundation for Quality Management (EFQM, 2006).

To date, no similar multi-dimensional (composite) index has been developed and used for benchmarking road safety issues. Most attempts in past research have focused on improving and implementing KPIs in road safety (i.e. ETSC, 2003). Simple safety indicators have been used and developed by a number of international institutions and databases such International Road Federation (IRF) and International Road Traffic and Accidents Database (IRTAD). However, unfortunately, many databases overlap to a large extent and they are not so detailed enough since many important indicators and data are not available for a large number of countries in the world. One reason for the lack of data, among many others, is that there is no single list of performance indicators in road safety, universally accepted, collected and used.

1.2 Purposes and research questions

This dissertation develops an integrated benchmarking model called Road Safety Development Index (RSDI). As the background has pointed out that road safety represents complex phenomena where a high number of accident factors of human, vehicle, road, environment, regulations are involved. There is a need for developing an easy understandable tool for policy makers and public that quantitatively measures road safety. Therefore, the main overall goal of this dissertation is summarised as follows:

The overall goal:

To create an international benchmarking model that indicates and communicates in a comprehensive and easy way the severity of the road safety situation in a specific country and/or in comparison to other countries in time.

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As one can see, the overall purpose is quite broad where it is not possible to collect data from all countries and for each key indicator. This needs enormous work and research. To narrow the research, this dissertation seeks to contribute to the initial research purpose by answering seven general research questions:

The first research question is:

What are the most commonly used benchmarking models for road safety? The second research question is:

What are the most commonly used performance indicators for benchmarking road safety internationally?

The third research question is:

What are the key performance indicators in road safety that can be applied uniformly for most countries?

The fourth research question is:

What are the knowledge, criteria and methodologies that must be addressed when aggregating the indicators into one single index?

The fifth research question is:

Do death rates have to be replaced with the new index, or should it be a supplementary part, or be part of the new index?

The sixth research question is:

How can the new index be applied internationally for a sample or more of countries?

The seventh and final research question is:

How can the applicability (usefulness) of this index be checked and evaluated? 1.3 Research Methods

There are many ways to design research. The best research approach for this study would mix quantitative and qualitative methods by combining a qualitative philosophy together with quantitative data and statistical procedures, in order to find whether the quantitative results hold true with qualitative philosophy or vice versa.

Due to the absence of earlier models and methods in road safety in the area of international aggregated tools, this study relies on knowledge and theories from other

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sectors of science. This type of research method can be seen as deriving concept hierarchies or as a part of Hermeneutics Cycle “art of interpretation” (Routio, 2006). However, we have to be aware that applying any outside philosophy for the purpose of research, may lead to better analysis, but not necessarily to deeper understanding if there is no clear interpretation and analysis. Thus, the imported concepts must be translated to road safety language and actual data of this study.

Furthermore, the dissertation attempts to develop a new theory and apply it to real world applications on the basis of previous models and actual data. Therefore it is necessary to choose a suitable research strategy that can promote both theory and practice. One example of this type of work is the approach shown in Figure 1.2.

Figure 1.2: Research approach for promoting both theory and practice (Routio, 2006)

Following the above discussion, the process of the research strategies can be organised into four practical stages: theory development, model building, empirical studies and model assessment. These four stages correspond to the Deming PDCA cycle (e.g. Watson, 1993): plan, do, check, and act; which are widely applied as a research model of benchmarking process (e.g. between organisations). As a result, the research questions can be now addressed in relation to these four research strategies as Table 1.1 shows.

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Table 1.1. The relation between the research questions, strategies and answers:

Research strategies Research questions Research answers are addressed in

Theory development Question 1, 2 and 3 Chapter 2 and 3

Model building Question 3, 4 and 5 Chapter 4 and 5

Empirical studies Question 6 Chapter 6 and 7

Model assessment Question 7 Chapter 8

1.4 Scope and limitations

Answering the research questions, especially in building a new international concept with a vision of real world applications are restrained by several limitations which are summarised as follows:

• The scope of thesis is carried out on a macroscopic level (national level) and is not applied on a microscopic level (local level). Therefore, the distinction between macro and micro performance indicators has to be described clearly. • This thesis will clearly describe the linkage between the performance indicators

(e.g. between practices and safety product in terms of death rates or between process indicators and end-results indicators) in order to eliminate misunderstandings.

• The area of research is wide and it needs to focus on a narrow scope (e.g. small number of countries), which may skew the results and will not show the full usefulness of methodologies and the obtained index. Therefore, we must be cautious in interpreting the final results.

• Selection of interesting performance indicators is the most important step for any benchmarking. However data is not always available or reliable for the chosen indicators in a large number of countries.

1.5 Thesis structure

The dissertation consists of nine chapters, as illustrated in Figure 1.3. The structure follows from the purposes of this study and from the stated research questions in Section 1.2 and Table 1.1. First part of the thesis is more theoretical, particularly Chapter 2, 3, 4 and 5, describes the conceptual framework and design of RSDI with support from literature review. The second part is more practical, particularly Chapter 6, 7 and 8, which aims to put RSDI into practice and to find some answers to the usefulness of RSDI in real applications of international benchmarking and ranking between countries.

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The purpose of Chapter two, Literature Review, is to explore what research has been done in international benchmarking of road safety and what research needs to be done in the same area. This chapter will also review the literature, which has already undertaken the challenge of aggregating performance indicators for international benchmarking (e.g. international composite indices, business management models). This discussion provides answers to the first research question and partly to the second and fourth question as well.

Chapter three aims to identify a master-list of performance indicators, which can be useful for international benchmarking purposes in road safety as well as for RSDI building. This work will be the starting point for finding a key list in the next chapter. This chapter includes elaboration of the answers to the second research question and partly to the third question.

The aim of Chapter four, The Conceptual Framework and Design of RSDI, is to introduce, develop and describe the conceptual framework of RSDI including the key concepts and philosophy behind RSDI that describes the overall performance of road safety in a country. For instance, any successful international benchmarking in road safety has to link practices to the end results. This chapter attempts also to identify a short-term list of key performance indicators for road safety that can be applied uniformly for most countries in each of LMCs and HMCs. This chapter does not only provide a strong evidence of the necessity to have such a tool, but it also demonstrates the mechanism of how to design such tool and use it. This chapter deals mainly with the research questions 3, 4 and 5.

Chapter five discusses different methods that can be used for normalising the indicators, weighting them and combining the weighted indicators into the index (RSDI). This chapter provides answers to the fourth research question.

The purpose of Chapters six and seven, Empirical Studies, is to present two empirical studies from LMCs and HMCs (answer to the sixth research question). The first empirical study comes from eight European countries. The second empirical study comes from five Southeast Asian countries. The collected data and selected indicators are presented; also, some background about road safety situation from the selected countries is presented.

Chapter eight discusses the results, outcomes and experiences, with focus on the results from the two applications that have been conducted in Chapters 6 and 7 (empirical assessment). In addition, there is a summary of the Strengths, Weaknesses,

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Opportunities, and Threats of RSDI by using the SWOT analysis (theoretical assessment). This chapter gives answers to the seventh and final research question. In Chapter nine, a short summary of the study is presented. The conclusions are drawn by summing up the answers in relation to the defined research questions at the beginning of the study. At the end, the limitations of the present research will be discussed and finally, an indication of the need for further work is given.

Figure 1.3: Structure of the thesis.

Further, for any successful benchmarking research, the repetition of the whole process of research strategies (four stages: plan, do, check, and act) is quite necessary to keep a sustainable development. Thus, it is necessary to link the conclusions (Chapter 9) from this research to the conceptual framework (Chapter 4) for possible continuous improvement in the future.

Empirical Studies

The RSDI Theoretical foundation, Design and Methodologies

Chapter one: Introduction Chapter two: Literature Review Chapter three: Macro-performance Indicators Chapter four: The Conceptual Framework of RSDI Chapter five: RSDI Methodological Approaches Chapter six: Applying RSDI to HMCs Chapter seven: Applying RSDI to LMCs Chapter eight: Discussion Chapter nine: Conclusions

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1.6 Contributions, publications and significance of this study

This dissertation work provides four major contributions. The first contribution is the design and development of the RSDI, which assesses and compares the road safety achievements in different countries in a comprehensive and easily accessible form. The second contribution is the development of a master list of macro performance indicators that can be used for different purposes of international benchmarking in road safety. The third contribution of the thesis is the development of a key list of applicable performance indicators, categorised into three pillars and nine dimensions. This key list can be used for assessing road safety in most countries worldwide based on data availability, quality and importance. The fourth main contribution is an in-depth analysis of the knowledge and criteria that are required in the selection and aggregation of performance indicators in road safety, supported by a literature review.

These contributions are useful to national policy-makers and the general public in helping them to make the magnitude of the problem easy to understand and to increase their awareness of this phenomenon. In addition, these contributions can be useful for researchers and traffic engineers who have an interest in collecting and analysing traffic accident data.

The dissertation is designed as a monograph; however some parts of this thesis have been published in other publications. The papers listed below are given in chronological order, the earliest first. These papers are also presented in the reference list.

Paper 1: Traffic Accidents Reduction Strategy, Best Practices from European States, in Proceeding of the International Conference in Traffic & Its Contemporary Issues, Kuwait, May 12-14, 2007.

(with K. Asp)

Paper 2: New Tools for Assessing and Monitoring National Road Safety Development, in Proceeding of the 2nd International Road Safety Conference, pp.31-34, Dubai - United Arab Emirates, November 6 -7, 2006.

(with K. Asp)

Paper 3: The Evolution of International Road Safety Benchmarking Models: Towards a Road Safety Development Index (RSDI), The International Journal “Science & Technology for Highways”, 2006, Vol.3, pp.74-83.

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Paper 4: Road Safety in Southeast Asia- Factors Affecting Motorcycle Safety, in Proceeding of the ICTCT international workshop on measures to assess risk in traffic, Campo Grande, Brazil, March 21- 23, 2005.

(with P. Lindskog)

Paper 5: Applying Road Safety Development Index (RSDI) for Big Cities, in Proceeding of the 6th International Conference: Traffic Safety Management for Big Cities, pp. 218-222, St. Petersburg, Russia, September 14 - 15, 2004.

(with K. Asp)

Paper 6: Developing Road Safety Development Index, in Proceeding of the ICTCT international workshop on improving safety by linking research with safety policies and management, Soesterberg, the Netherlands, October 30.-31, 2003.

This paper was the point of departure of the development of RSDI, where the first version of the framework of RSDI was first presented and published.

Paper 7: Road Safety Perspective in Arab Countries- Comparative Study and Analysis of Progress, in Proceeding of the SORIC’ 02 Conference (Safety on Roads), pp. 116-121, Bahrain, October 21-23, 2002.

(with K. Asp)

In addition, there are two papers under submission for possible publication in an academic journal.

Paper I: A Composite Index for International Road Safety Benchmarking (RSDI): A New Tool for the 21st Century.

(with K. Asp)

Paper II: Road Safety International Benchmarking: Results of Applying the Road Safety Development Index (RSDI) to Less-Motorised Countries in Southeast Asia. (with K. Asp)

Further, parts of this dissertation were published in the author’s Licentiate thesis: Towards a Road Safety Development Index (RSDI) - Development of an International Index to Measure Road Safety Performance. Licentiate Thesis No. 1174, ISBN 91-85299-70-7, Linköping University, Sweden, 2005.

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Since the RSDI was first published in year 2003 (see paper 5), there has been an increasing international interest in reviewing and testing RSDI through different studies, projects and international bodies, for instance:

Study I: Planzer, R., (2005). Traffic Safety in Latin America and the Caribbean, Actual Situation and Challenges, UNECLAC ‘The United Nations Economic Commission for Latin America and the Caribbean’, ISBN: 92-1-322783-3, pp 27 & pp 28.

This study is a comparative research in road safety in Latin America and the Caribbean, the full RSDI conceptual framework and its methodology were reviewed, presented and translated to Spanish.

Study II: SafetyNet, (2005b). State of the art Report on Road Safety Performance Indicators, European Commission, SWOV, Netherlands, pp16 and pp80

The SafetyNet is a major European project. It started year 2004 and is sponsoring by the European Commission. This project aimed to collect, harmonise and analyse traffic safety data in EU including the 10 new European member countries. The SafetyNet study referred to RSDI within the context of developing road safety performance indicators.

Study III: Capitulo, A. (2005), Cooperation Agreement in the framework of the project “Observatory for road safety”, OROS, R8-B2-04, pp 9.

This European project acknowledged RSDI within the context of improving road safety in towns.

Study IV: Fang, S (2006), Research on the safety evaluation index system of road networks, Journal of Safety Science and Technology, Vol.2 No.2, P.34-38

Moreover, some international organisations have shown considerable interest in this RSDI tool. For instance, the concept of RSDI was first introduced in 2002 in a seminar hosted by the Swedish International Development Agency (SIDA) and attended by representatives of the World Health Organisation (WHO) and Karolinska Institute (one of the leading medical universities in Sweden and Europe). Likewise, the EMBARQ organisation of the WRI Centre for sustainable Transport at the World Bank has acknowledged RSDI as a benchmarking tool of interest for the development of Bus Rapid Transit (BRT) systems in different cities, mainly in developing countries. More recently, the RSDI has inspired the development of similar tools, for instance the ETSC

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(European Transport Safety Council) has launched the EU Road Safety Performance Index (PIN) at the EU summit of transport ministers in Brussels on June 2006.

Nevertheless, the author has been involved in a variety of international research projects that contributed directly or indirectly to the development of this dissertation (e.g. data selection, data collection, country road safety profile, etc.). These projects are:

Project 1: RetsNet ‘Regional Traffic Safety Network’, started year 2000. It aimed to strengthen the cooperation and technology transfer in road safety between five southern African countries (Botswana, Malawi, Namibia, South Africa and Zimbabwe) and Sweden.

Project 2: ASNet project ‘The ASEAN Road Safety Network’, started in October 2003. It is designed as an Internet networking system to strengthen the regional cooperation related to traffic safety in the ten ASEAN countries: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand and Vietnam.

Project 3: TechTrans project started in December 2003. It aims to establish a sustainable virtual resource centre at the State Technical University (MADI) in Moscow, Russia. This project developed e-learning courses and applications in the field of road safety to Russian universities.

Project 4: SPIDER project, ‘The Swedish Program for ICT in Developing Countries’ started in 2004. This project aims to create programmes and applications for higher education adapted to the needs of developing countries. The project focuses on three developing countries for cooperation with: Burkina Faso, Sudan and Vietnam.

Project 5: Globesafe ‘Global Road Safety Database’. It is an Internet-based tool that collects, harmonises and analyses the road safety data for the purpose of global comparisons.

1.7 Definitions of terms

There are several terms concerning road safety issues and their applications. It is not possible to give a precise definition of all the terms used in the following chapters, because it would run to many pages and discussions. It may be useful however, to briefly discuss the key terms and their meanings in this study. Readers should bear in mind that the meaning of each term depends on the context and the subject of discussion.

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In this thesis, the term "Road Safety" is often used instead of "Traffic Safety" because this thesis focuses on road traffic safety only including road user safety and vehicle safety. The term "Traffic Safety" is a general term and could refer to the safety of all traffic modes: air traffic, rail and road.

Some studies are not comfortable with the term "accidents" in the sense of describing road safety problems as accidents that happen by chance. They prefer to use the term "crash" instead of accidents. However, this thesis keeps using the term accidents because it is still widely used in most current literature. Accidents can be simply classified as fatal, serious, slight and damage only. The term "casualties" means both deaths and injuries.

The term "macroscopic data" is used to describe the data from a national level e.g. number of road accident deaths in the country, whereas "microscopic data" refers to detailed information at local level e.g. number of deaths by location of accident, number of deaths by type of vehicle, time, etc.

The distinction between developed and developing countries is a difficult matter. The term "developing countries" is rather misleading because all countries are developing today. Developing countries are officially classified in a human development index (according to the United Nations) or on an economic basis. Whether we call this group developing countries, low-income countries, less developed countries, underdeveloped countries, third world, south, or other names; there is no precise definition of the term "developing countries". Many international studies in road safety, e.g. Jacobs et al. (2000), consider "vehicle ownership" as the most appropriate criteria to classify countries. However, one has to note that the number of vehicles cannot be considered as a sign of road safety development in a country as this depends on the whole transport system and not only on the number of vehicles. To give an example, many European countries are currently discussing whether to stop or reduce the increasing level of motorisation. Similarly, for the purpose of this study and because of the lack of something better, the study primarily deals with this issue by using vehicle ownership (motorisation) as the most appropriate criterion to classify countries internationally. The term Less Motorised Countries (LMCs) refers to countries with low and medium rates of vehicle ownership (e.g. less than 500 vehicles per 1,000 population), while Highly Motorised Countries (HMCs) refers to countries with high rates of vehicle ownership (e.g. more than 500 vehicles per 1,000 population). The HMCs are mainly located to countries in North America, Western Europe and Japan; while LMCs are the remaining countries.

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The term "composite index" is used throughout this thesis to mean the combination of several indicators. The "indicator" itself is not any measurement and it is normally used to show the level of performance in the country.

Terms such as "performance", "continuous improvement" and "success" are crucial words and should be better defined in order to be easily understood by the readers. The term performance in this dissertation refers to the end results that can be benchmarked against similar end-results from other countries. The term "continuous improvement" focuses on improving activities and practices (i.e. road user behaviour). The word "success" refers to countries that fulfilled their performance targets. For further reference to these quality terms, see for instance (Tangen, 2005).

The term "Master-list of performance indicators" refers to a long list of desired indicators which can be useful for international benchmarking purposes in road safety, whereas the term "Key list of performance indicators" is used to refer to a short list of realistic indicators that have acceptable level of availability and quality worldwide.

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2 Chapter 2: Literature Review

Chapter 2

Literature Review

The purpose of this chapter is to explore what research has been done in international benchmarking of road safety and what research needs to be done. This review will also move beyond the road safety sector through literature that has already undertaken the challenge of aggregating performance indicators for international benchmarking (e.g. international composite indices, business management models).

The literature review will be discussed in two parts as follows:

The first part (Section 2.1), aims to provide an overview of the major work related to international benchmarking of road safety, which has been done in the past and very recently, and outlines the evolution of road safety benchmarking models towards today’s understating of integrated benchmarking approach. This review also attempts to address the limitations of the traditional models of measuring the overall performance of a country. Also, it will assess the first research question (stated in previous chapter): What are the most commonly used benchmarking models for road safety?

The second part (Section 2.2), deals with the question of how the study has chosen certain types of methodologies and framework for the purpose of building a composite index. There are many models, in different aspects of science, which have addressed and designed interesting conceptual frameworks, concerning aggregated indices, in which some of them can be translated for the purpose of this study and RSDI building. This part also will review literature related to the fourth research question (as stated in previous chapter):

What are the knowledge, criteria and methodologies that must be addressed when aggregating the performance indicators of a country into one single model for the purpose of international benchmarking?

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Each part concludes with a brief summary, and the end of the chapter gives a general summary of the entire review.

2.1 International benchmarking of road safety

Road safety models can be microscopic (disaggregated level), macroscopic (aggregated level) or mesoscopic (include some disaggregated variables on aggregated level). At all levels, road safety models can be either explanatory (explain the development) or forecasting (for short or long-term predictions). A review of these studies is reported, among others, by (Hakim, 1991; Hakkert & McGann, 1996; OECD, 1997; Van den & Wets, 2003, Turner et al., 2004). The focus of this literature review is on the major macroscopic models that used for benchmarking road safety development internationally.

The road safety situation is a complex issue and there is high number of accident factors involved. A large amount of research has investigated the characteristics of these factors and road safety outcomes such as Haddon's Matrix (1972), which aimed to identify the basic road safety elements: driver, vehicle, road design, environment and their interrelation between each other. Rumar (1999) has described the road safety problem and factors as a function of three basic dimensions: exposure, accident risk and consequences. These research activities have resulted in a large number of applications and analysis at both micro and macro-level.

Work on international benchmarking has found significant interest and inspiration among both researchers and practitioners. The major objective of such work is to encourage countries to assess their development with other countries and learn practices from each other. A number of benchmarking models are already being developed and they range from relatively simple models to highly complex ones depending on the number of indicators involved, details of data and complexity of methods used in calculations and analysis. In road safety benchmarking between countries, four types of models, are generally used (Al-Haji & Asp, 2006b):

1. Product Benchmarking is used to compare road accident death rates.

2. Practices Benchmarking is used to compare activities related to human-vehicle-road performance (e.g. seat belts use, crash helmets use, motorways level, etc.) 3. Strategic Benchmarking is used to compare National Road Safety Programme

(NRSP), management, enforcement and organisational framework.

4. Integrated Benchmarking is used to compare countries in terms of the three previous types of benchmarking altogether.

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Based on the above classifications, the evolution of road safety benchmarking models can be divided into four stages or generations, which are simplified in the following description and illustration of “generations” in figure 2.1:

• The first generation is characterised by models that compare countries’ road safety performance in terms of risk and exposure indicators such as accident death rates and motorisation (Product Benchmarking). These models are cross-sectional models, where international data were observed in the same year.

• The second generation takes time into account. Theses models benchmark the road safety product over time series. These models are useful in monitoring the trends in road safety in countries and indicate the direction of progress ahead.

• The third generation has realised the need for increased integration between product (accident death rates) and other indicators in the same model (e.g. key practices and strategies).

• The fourth generation focuses on the three types of benchmarking: Product, Practices and Strategic Benchmarking together.

Most of the early benchmarking models are still in use and being applied in different studies. However, today computers are developing rapidly, which simplifies the analysis of a large amount of road safety data that was not available before. This development has made the work in the third and fourth generations easier and bringing it closer to reality. Furthermore, picking up ideas (i.e. performance indicators) from the first three generations was useful in reaching the fourth generation of integrated benchmarking.

Integrated benchmarkin Product Benchmarking Strategic Benchmarking Practices Benchmarking Time Product Benchmarking Practices Benchmarking Strategic Benchmarking

The fourth generation The first, second and third generation

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2.1.1 The first generation: Linking motorisation, traffic risk and personal risk

An early study in 1949, R. J. Smeed compared twenty countries, mostly European for the year 1938, where the author developed a regression model (log-linear model) and found an inverse (or negative) relationship between the traffic risk (fatality per motor vehicle) and the level of motorisation (number of vehicles per inhabitant). This regression represented the best estimates of the mean values of traffic risk for each given value of motorisation (what is called least square). This shows that with annually increasing traffic volume, fatalities per vehicle decrease. Smeed concluded that fatalities (F) in any country in a given year are related to the number of registered vehicles (V) and population (P) of that country by the following equation:

F/V = α (V/P)- β (2.1)

where F is the number of fatalities in road accidents in the country V is the number of vehicles in the country

P is population α is 0.003 and β is 2/3

This formula became popular and has been used in many studies. It is often called as Smeed’s formula or equation despite some authors preferring to call it a law.

This nonlinear relationship can be translated to a linear one by taking the logarithms of the two sides: logY =logα+βlogX, where Y is F/V and X is V/P.

The number of fatalities can be derived from Smeed’s formula as: F = c.Vα.Pβ, where c, α, β are parameters and they are estimated from data by using the least square method. For the Smeed data (year 1938) the formula was: F = 0.0003 P2/3 V1/3

Personal Risk (fatalities per population) is obtained by multiplying both sides of Smeed’s equation (2.1) by V/P as follows: F/P = a (V/P)1-b or F/P = 0.0003(V/P)1/3 Since 1949, many studies have been discussed on the basis of Smeed’s equation (2.1) or made reference to this formula. Some authors followed the equation of estimating the regression parameters (α, β) of the data by calculating the country road safety performance in comparison to other countries; see Jacobs and Hutchinson (1973), Jacobs (1982), Haight (1983), Mekky (1985). These studies found that Smeed’s formula can give a close estimation of the actual data and it can be applied to different sample sizes of countries and years with the use of different values of α and β. Jacob and Fouracre (1977) applied this formula to the same sample of countries used by Smeed for the years 1968-1971 and it was found that the formula remains stable. Jacobs and

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Hutchinson (1973) examined the data for 32 less and high motorised countries from the year 1968. Al-Haji (2001) compared 26 countries around the world with different levels of development and motorisation. The results from this study support Smeed’s view of the relation between motorisation and fatality rates. The correlation was high, 96% of the variations are explained for the low motorised countries and 93% for the highly motorised countries. Mekky (1985) found that the equation significantly captures the relationship between motorisation and traffic risk for the Rich Developing Countries (RDCs). Similarly, this conclusion was also reached by the study Al-Haji & Asp (2002), which used cross sectional data of road safety in Arab countries.

The number of registered vehicles has been replaced by the total vehicle kilometre driven in many late studies (e.g. Silvak, 1983; Fred, 2001). This measure (vehicle kilometre driven) was not available at the time of Smeed’s study. According to Koornstra & Oppe (1992), the development of motorisation (referring to the number of vehicle kilometres per year) follows an increasing S-shaped curve. Furthermore, Timo (1998) has carried out a cross-sectional comparative study in many Eastern and Western European countries to examine the development in the number of fatalities in relation to the development levels of mobility. The study has shown that when the mobility reaches the saturation level, as happened in many Western Europe countries, the decrease in the number of fatalities has stopped or fluctuated only slightly.

Additionally, some other studies have tried to explain why the curve of development (fatality rates) declines as has been noted in many countries and shown in Smeed’s formula. The studies have analysed the factors and measures that influence the development of the curve of road safety. A review of these studies is reported by Elvik & Vaa (2004) and Hakim (1991). Besides, Minter (1987) and Oppe (1991b) showed that Smeed’s law is a result of a national learning process over time. The development in society at the national level is the result of the developments at the local level. In other words, the individuals (road users) can learn by experience in traffic where they improve their driving skills and knowledge, while society as a whole can learn by better national policy and action plans. Figure 2.2 illustrates these factors on the development curve of road safety where the long-term trends are based mainly on repeated cross-section surveys from different countries for different years. An early level of motorisation, first leads to an increase in traffic risk, but not necessarily with the same high growth in personal risk. However, later at a medium level of motorisation, traffic and personal risks increase and both values are high. At the third stage of higher motorisation, when a country is completely motorised, traffic and personal risks decrease. The change between the three stages, as mentioned above, is due to better engineering of vehicles and roads and greater understanding of the system by the road users.

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Figure 2.2: The influencing factors on the development curve of road safety

(Adapted from several works)

Conversely, at the same time, many studies have criticised Smeed’s model because it only concentrates on the motorisation level of country and ignores the impact of other variables, see (Broughton, 1988), (Andreassen, 1985), (Adam, 1987), where according to Smeed’s model, population and vehicles are the only country values, that influence the number of fatalities. This means that road safety measures have no meaning because road fatalities can simply be predicted from population and vehicle numbers in any country and any year. Andreassen (1985) criticised the model’s accuracy because there would always be a decline in traffic risk for any increase in the number of vehicles, but generally in non-linear way. Andreassen proposed relating fatalities to (V)B4 where B4 is

a parameter highly related to each particular country, even to countries with a similar degree of motorisation. Furthermore, Smeed’s study analysed data for one year. It was a cross-sectional analysis with no time series analysis (Adam, 1987). Smeed’s formula expected the downtrend in fatalities rate but not the number of absolute fatalities, which occurred in the HMCs in the seventies (Broughton, 1988). In other words, the trend failed to fit with the real figures in HMCs. Broughton has concluded that: “Smeed’s formula has no generally validity”

In later years Smeed (in Oppe, 1991a) has commented on some of these remarks that: “…We must be guided by the data and not by our preconceived ideas...The number of fatalities in any country is the number that the country is prepared to tolerate…” LMCs HMCs Motorisation Fatalaties Rate [per vehicle (1) and per person (2)] Learning and society force Engineering force Economy force (1) (2) High Medium Low

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Also, Haight (in Andreassen, 1985) has referred to Smeed’s equation that:

“…When the formula disagrees with the observations we tend to assume that the particular area under investigation is safer or less safe than it ought to be…” Regardless of whether one agrees or disagrees with Smeed’s model, the fact remains that Smeed’s model gave a simplified and fairly good representation of traffic risk and motorisation in different parts of the world during the earlier stages of road safety development.

To summarise, the models previously mentioned (in general) are in some way based on regression models or multiple regression models or quadratic regression models. They employ a small number of indicators (motorisation, personal risk and traffic risk) to check the goodness of fit to data from different countries and to find the appropriate related equation(s) for making comparisons between the chosen countries.

2.1.2 The second generation: Linking traffic risk, motorisation and personal risk with time

In this generation, many benchmarking models have been developed to describe and predict safety development between countries on the basis of time series models and theories. They relate the variables to a function of time to determine the long run change in safety development over time either in a monthly form or annually. These models attempt to find the smoothed curves to the time series data between countries.

Koornstra (1992) has shown that motorisation is considered to be dependent on time and the relationship between deaths and population should include time. The author found the following formula for approximating the number of fatalities from country to another in a particular year:

y c k t w k t x t t V V V zV F _⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ − = − − ( max) 1 (2.2)

where Ft is the number of fatalities for a country in a year t,

Vt is the number of vehicle kilometres travelled in the year t,

Vmax is the maximum number of vehicle kilometres,

k is the time lag in years, and x, w, z, y, and c are constants

Oppe (1989) assumes that fatality rates follow a negative exponential learning function in relation to the number of vehicle kilometres and time. This method has been found to be most effective when the components describing the time series behave slowly over time as follows:

Road Safety Development Index: Theory, Philosophy and Practice

Linköping Studies in Science and Technology

Dissertation No. 1100

Road Safety Development Index (RSDI)

Theory, Philosophy and Practice

Ghazwan Al-Haji

Department of Science and Technology

Linköping University, SE-601 74, Norrköping, Sweden

ABSTRACT

SAMMANFATTNING

Acknowledgments

List of Abbreviations

Table of Contents

List of Figures

List of Tables

1 Chapter 1: Introduction

Chapter 1

Introduction

2 Chapter 2: Literature Review

Chapter 2

Literature Review