Monitoring road safety development at regional level: A case study in the ASEAN region

Accident Analysis and Prevention 106 (2017) 437–449

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Monitoring road safety development at regional level: A case study in the ASEAN region

MARK

⁎

Faan Chena,b, , Jianjun Wangc, Jiaorong Wua, Xiaohong Chena, P. Christopher Zegrasb a b c

The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA 02139, United States Institute for Transportation Engineering Research, School of Highway, Chang'an University, Xi'an 710064, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Road safety development Safety monitoring ASEAN region Performance management Rank-sum ratio

Persistent monitoring of progress, evaluating the results of interventions and recalibrating to achieve continuous improvement over time is widely recognized as being crucial towards the successful development of road safety. In the ASEAN (Association of Southeast Asian Nations) region there is a lack of well-resourced teams that contain multidisciplinary safety professionals, and specialists in individual countries, who are able to carry out this work effectively. In this context, not only must the monitoring framework be effective, it must also be easy to use and adapt. This paper provides a case study that can be easily reproduced; based on an updated and refined Road Safety Development Index (RSDI), by means of the RSR (Rank-sum ratio)-based model, for monitoring/reporting road safety development at regional level. The case study was focused on the road safety achievements in eleven Southeast Asian countries; identifying the areas of poor performance, potential problems and delays. These countries are finally grouped into several classes based on an overview of their progress and achievements regarding to road safety. The results allow the policymakers to better understand their own road safety progress toward their desired impact; more importantly, these results enable necessary interventions to be made in a quick and timely manner. Keeping action plans on schedule if things are not progressing as desired. This would avoid ‘reinventing the wheel’ and trial and error approaches to road safety, making the implementation of action plans more effective.

1. Introduction Road traffic collisions are a critical global problem in terms of their economic and health sector impacts. Resulting in more than 1.2 million deaths, as many as 50 million non-fatal injuries, and costing governments approximately 3% of Gross Domestic Product (GDP) each year (World Health Organization, 2013, 2015). The epidemic of road traffic injuries is particularly urgent as it continues to grow in the low- and middle-income developing world; which accounts for over 90% of the world's fatalities whilst having only half of the world's registered motorized vehicles (Asian Development Bank, 2012; World Health Organization, 2009). This is especially true in the Asia Pacific Region, which is contributing to nearly 60% of the death toll (Asian Development Bank, 2013); more specifically in the Association of Southeast Asian Nations (ASEAN) region where over 75,000 people are killed, and over 4.7 million are injured or crippled, in road crashes every year across the region (Asian Development Bank, 2012). While most countries are experiencing rapid economic growth and motorization, huge economic losses and social costs (as high as around 2% of ⁎

annual GDP) (Asian Development Bank, 2013) are incurred annually in the ASEAN region from those killed, crippled, or injured due to road accidents. This is even greater than the annual development aid received in the region. Unfortunately, most of these casualties are young people (aged 15–44 years), who are the main breadwinners in their families and the most economically active segment of the population (Asian Development Bank, 2012). This in turn contributes significantly to the recession of economic and social development, as well as to the perpetuation of poverty. The Asian Development Bank (ADB) and Association of Southeast Asian Nations (ASEAN) member countries have expressed great concern towards the lack of road safety in the ASEAN region and its adverse impacts on the development of society. They have recognized the requirement for urgency in taking action to solve these problems. In recent years, regional and individual country-specific road safety action plans have been developed and implemented, at national and regional levels, with the support of the ADB to achieve targeted objectives and aims (Asian Development Bank, 2012). These action plans have had special emphasis towards achieving the targeted reduction in deaths

Corresponding author at: The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China. E-mail addresses: [email protected], [email protected] (F. Chen).

http://dx.doi.org/10.1016/j.aap.2017.07.016 Received 3 November 2016; Received in revised form 6 July 2017; Accepted 11 July 2017 0001-4575/ © 2017 Elsevier Ltd. All rights reserved.

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Section 6.

and injuries in relation to road accidents. However, the improvement of road safety is a very complicated task in which strategies, actions and initiatives, both within individual countries and across the region, must be coordinated at different stages and levels. To ensure successful safety improvement, it necessitates that the ASEAN region (e.g. the ASEAN Secretariat, individual Transport Department) must supervise the progress of the proposed action plans. Producing an overview of road safety development for each member country at regular intervals/after each stage of the implementation is completed. By means of monitoring, the progress toward the desired impact can be promptly ascertained. Meaning areas of poor performance, potential problems or delays can be identified early. The results from this monitoring process would then be used as basis for further actions at the next stage, with remedial action and interventions applied as required; if things are not progressing as desired, to keep plans on schedule, and more importantly, to maximize the effectiveness of the implemented strategies and action plans across the ASEAN region as a whole. Yet, there is currently no consistent, socially and scientifically acceptable and easy-to-reproduce approach in monitoring road safety development between member countries in the region. Ununiformed road safety monitoring will result in an unrealistic, partial and inaccurate picture of the true scale, costs, and characteristics of road safety-related problems across the region as a whole. This in turn adversely impacts the cooperation and coordination of the ASEAN member states on Regional Road Safety Strategy and Action Plan formulation. To this end, an effective and uniform framework to effectively monitor the progress of the implementation of action plans in individual member countries and across the region is desperately required. The monitoring framework for road safety development is a methodologically intensive process including three major tasks: (1) creating road safety development indicators; (2) providing a sound and enriched overall picture of progress (by means of weighting and aggregating the indicators); (3) identifying areas with good/poor performance respectively (grouping jurisdictions). Each step (e.g. weighting, aggregating, and grouping) involves some methodological choice. In other words, several methods are required to complete the monitoring process; this not only requires additional skills but also increased effort. Practical application of the methodologies, where additional training is required, would be another challenge for those individuals who are responsible for the implementation in each member country (domestic road safety participators, practitioners and policymakers). Especially, considering that there is a lack of well-resourced teams that contain multidisciplinary safety professionals, and specialists in individual countries, who are able to carry out this work effectively. With this in mind, the characteristic of being easy to use/adapt/reproduce is of particular importance. Alongside other characteristics such as being systemic and scientific, clearly defined, and accurately described. Within this context, this paper demonstrates a case study by means of an easy-to-reproduce monitoring framework; which provides an overview towards the progress of road safety development in each member country, or the consolidated progress across the ASEAN region as a whole. The case study provides supervision of road safety development of countries or of jurisdictions (e.g. states, provinces, areas) in an easy-to-adapt manner; more specifically, it seamlessly integrates weighting, aggregating and grouping into a ‘one-stop’ procedure that enables the ASEAN Secretariat or individual countries to perform the monitoring step-by-step, without the increased effort required by multimethodological approaches. The rest of this paper is organized as follows. Section 2 investigates the road safety development indicators, collects data used for analysis, and develops the RSR-based methodology for the monitoring of road safety development. Section 3 presents the results that were generated from the use of the monitoring framework, followed by discussion of the computational results in Section 4. Section 5 outlines the limitations of the study suffered. The paper closes with the main conclusion in

2. Methods In this section, an effective monitoring framework for road safety progress is developed, combining the aforementioned major tasks into a ‘one-stop’ procedure. 2.1. Road safety development index 2.1.1. Safety performance indicators (SPIs) Safety performance indicators (SPIs) are supportive tools for monitoring the status, achievements or evolution of road safety development on behalf of policy makers, decision makers, and participators (Wegman et al., 2008; Hermans et al., 2008; Chen et al., 2015). In recent years, in order to capture a more complete and insightful picture of road safety, increasingly detailed indicators have been suggested and implemented for use in road safety development. However, identifying a core set of appropriate, available, and valuable indicators is by no means easy. In fact this is still a key research hotspot in the field of road safety deserving particular attention (Chen et al., 2016; Wegman and Oppe, 2010). The selection of indicators depends to a large extent on the availability, reliability and quality of the data being collected (Papadimitriou et al., 2013), requiring regular revisions, e.g. updating and refinement (Hermans et al., 2008). Nevertheless, good practice would indicate that both quantitative and qualitative indicators are worthwhile of being promoted and refined for the supervision of road safety progress, e.g. Al-Haji (2005, 2007). Al-Haji (2005) identified eight groups of indicators based on the human-vehicle-infrastructure system of problem decomposing approaches; which are Traffic Risk, Personal Risk, Vehicle Safety, Road Situation, Road User Behavior, Socio-economic Background, Traffic Police and Enforcement, and Road Safety Organization Structure. Each group comprises of one or several indicators. These groups were further divided into two dimensions, i.e. Direct dimension and Indirect dimension. The indicators of direct dimension are derived measures (outcomes or direct symptoms), which are considered as good measures for describing road safety development in a direct manner. The indirect dimension indicators are individual means (processes or indirect symptoms), illustrating the achievement in a particular theme or practice related to road safety (Al-Haji, 2005). Additionally, an extra group of indicators ‘Changing Trend’ was added, making up to a total nine groups (sub-dimensions) that were categorized into three pillars; Safer Product, Safer People, and Safer System (Al-Haji, 2007). These indicators were combined into a composite index, the Road Safety Development Index (RSDI), which was acknowledged internationally. This comprehensive set of road safety indicators provided a solid starting point for measuring national road safety development in the ASEAN region; although it should be noted that ‘organizational structure’ and ‘enforcement measures’ (that were based mainly on subjective assessments) were not available at that time, due to the fact that it was difficult to collect information, or gather experts’ assessment, to measure these subjective indicators in the early stage. However, these indicators are both expected to be developed and embodied for the future. In fact, comprehensive indicators that relate to policy implementation, which can support decision makers in a systematic way, still need to be further developed at present (Bax et al., 2012). Fortunately, Southeast Asian road safety development is increasingly being strengthened by both regional and international coordination, under the leadership and support of some relevant international organizations (e.g. ADB, WHO). Positive progress is occurring. The ASEAN Regional Road Safety Centre (ARSC) was established with the support of the ADB on 27 November 2014 in Mandalay, Myanmar. With the aim of providing information and knowledge for ASEAN Member States, which include traffic laws and regulations, road accident data, road transport data, vehicle regulations concerning standards and 438

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vehicles, and population derived from World Health Organization (2009, 2013, 2015). For C12, C22, C51 and C52, the data is compiled from information relating to the institutional framework of each of these 11 countries, which is provided by World Health Organization (2009, 2013, 2015), being assigned an average score on a scale of 0–10 by experts' assessments. The Human Development Index (HDI) values are derived from the annual Human Development Report published by the United Nations Development Programme (UNDP) (see UNDP, 2009, 2013, 2015). The final dataset on the basic indicators for the 11 Southeast Asian countries is as follows in Tables 1–3. Prior to the analysis, missing data imputations will be performed in SPSS Statistics 20 (a software package used for logical batched and non-batched statistical analysis).

inspections, driver training and driver testing, traffic police activities, road safety training in schools, road safety information/campaigns to the community, etc. (ARSC, 2015). Moreover, the WHO has launched a series of global status reports on road safety, reflecting information from around 180 countries/areas out of a total of 195 WHO Member States. Of which, a comprehensive collection, analysis and assessment of legislative road safety documents from all participating countries are conducted on the basis of a standardized survey; more specifically, the legislative documents relating to the key risk factors: speed, drink–driving, drug–driving, the use of motorcycle helmets, seat-belts, child restraints, and mobile phone use while driving (World Health Organization, 2009, 2013, 2015). The information on vehicle safety standards is also included. This information makes it possible to measure the qualitative and subjective indicators at present, particularly the policy-, legislation-, enforcement- and organization-relevant indicators that were previously unavailable in Less Motorized Countries (LMCs), yet play an essential and vital role in reflecting road safety development. In addition, it should be noted that the ASEAN region covers the whole spectrum of development in terms of motorization levels, national populations, urbanization and income; ranging from very poor developing countries, to those that are as highly developed as any other in the world − presenting immense contrasts (Asian Development Bank, 2005). As mentioned previously, the number and type of selected indicators is based on the availability of data, which depends on the development level of the country (Al-Haji, 2005). Developed countries usually have better data collection, whilst developing countries data systems are relatively poor. Therefore, for the ASEAN region as a whole, a set of core indicators engineered as well-balanced as possible that are applicable to all member countries is urgently required. In fact, the quality and accuracy of a composite index should evolve in parallel with improvements in its sub-components (or sub-indicators) development (Nardo et al., 2005). That is, the road safety performance indicators as supportive tools for monitoring, should be regularly updated and progressively refined alongside the improvement of the data collection process. In this context, this paper promotes, refines and updates the framework of Road Safety Development Index (RSDI) that was developed and suggested for future use by Al-Haji (2005, 2007), introducing a more comprehensive set of road safety development indicators that are geared to the whole ASEAN region. The updated RSDI framework will be tested in its ability to report on road safety progress, achievement or development in the ASEAN region, which is presented in Fig. 1.

2.2. Monitoring model The monitoring system is a methodologically intensive process, comprising of a series of mathematical steps, including weighting, aggregating, and grouping. Undoubtedly, previous methods, such as the DaCoTA-project approach (e.g. Bax et al., 2012), Data Envelopment Analysis (DEA) (e.g. Shen et al., 2012), the ordered weighted averaging (OWA) (Hermans et al., 2010), the SUNflower approach (e.g. Wegman et al., 2008), the weighted sum approach (e.g. Heramns, 2009; Al-Haji, 2005), etc., established a solid foundation for road safety development measurement. However, monitoring should not be viewed as a one-time project, but as an ongoing process and being carried out at regular intervals (Wegman et al., 2008). The methodological process of road safety development monitoring is also a continuous one that needs frequent updates as new methods are worthwhile testing for the road safety case.

2.2.1. RSR method RSR (known as Rank-sum ratio), originally proposed by Tian (1988), is a statistic analysis method integrating the strongpoints of classical parametric estimations and modern nonparametric estimations. The basic principle is that the attribute data of the decision-making alternatives are converted to a dimensionless statistical metric, the Rank-sum ratio, by means of rank transformation. The Rank-sum ratio value refers to the average rank of the decision attributes (row or column), which is a nonparametric composite index with the characteristics of 0–1 interval continuous variables. Subsequently, the alternatives can be assessed through statistical distribution, probability theory, and regression analysis on the basis of the RSR value.

2.1.2. Data collection The data for the set of the indicators included into the updated Road Safety Development Index (RSDI), presented in Fig. 1, will be gathered for 11 Southeast Asian countries (10 ASEAN Member States plus TimorLeste), i.e. Brunei (BN), Indonesia (ID), Cambodia (KH), Laos (LA), Myanmar (MM), Malaysia (MY), Philippines (PH), Singapore (SG), Thailand (TH), Timor-Leste (TL), Vietnam (VN). The data used is the latest available and collected and compiled from several sources, including international databases, international organizations and recent publications of international working groups. More specifically, data on B11, B12, B13, C41, C42, C43, C44 is directly extracted from the Global Status Report on Road Safety published by the WHO (see World Health Organization, 2009, 2013, 2015). Data on C21 is collected from the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP) (ESCAP, 2016), the library of Central Intelligence Agency (CIA) (Central Intelligence Agency, 2016) and International Road Federation (IRF) (International Road Federation, 2010). Data on C31, C32, C33, C34 is derived from the international database of the World Bank − World Development Indicators (WDI) (World Bank, 2015). Data on A11, A21, A31 (A31 was set as ‘Percentage change of fatalities in recent years (2010–2013)’ in this case study) and C11 is calculated based on the reported road traffic fatalities, registered

2.2.2. RSR-based model In this study, the RSR method is investigated with a natural extension, an integration to develop a brand new synthetic model − the RSRbased monitoring model; more specifically, a seamless integration of weighting, aggregating and grouping into a real ‘one-stop’ procedure; one based only on the single isolated RSR method for monitoring road safety development. The RSR-based monitoring model can be expressed in a series of steps as listed below. Step 1: Identify a decision matrix Assume that a multi-criteria decision-making problem (monitoring of road safety development in this case study) has m alternatives (countries in this case study), each with n criteria (SPIs in this case study). To obtain the aggregated performance of the m alternatives on a given set of n criteria, a decision matrix X = (xij)m × n is firstly established as follows.

439

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Fig. 1. The updated Road Safety Development Index (RSDI) applied to the ASEAN region.

C1 C2 ⋯ Cn A1 x11 x12 ⋯ x1n ⎤ ⎡ A x21 x22 ⋯ x2n ⎥ X= 2⎢ ⋮ ⋮ ⋱ ⋮ ⋮ ⎢ ⎥ Am ⎢ ⎦ ⎣ x m1 x m2 ⋯ x mn ⎥

X ′ij =

Xij − Xj min Xj max − Xj min

(2)

(b) For cost criteria (A11, A21, A31, B11 in this case study). (1)

X ′ij =

where Ai (1 ≤ i ≤ m) denotes the alternatives i; Cj (1 ≤ j ≤ n) represents jth criterion on which the alternatives are judged; xij is the crisp value indicating the performance rating of alternative Ai with respect to criterion Cj. Step 2: Normalize the decision matrix General linear scale transformation techniques are used to transform the various criteria scales into a comparable scale; specifically a scale of the same direction (benefit) and of the same magnitude. (a) For benefit criteria (B12, B13, C11, C12, C21, C22, C31, C32, C33, C34, C41, C42, C43, C44, C51, C52 in this case study):

Xj max − Xij Xj max − Xj min

(3)

(c) For benefit-cost criteria (no indicator within this case study), these indicators are either considered as higher being better, or lower being better.

X ′ij =

Xij∗ − X ∗j min X ∗j max − X ∗j min

Xij

; Xij∗ =

⎧ M , forXij ≥ M ⎨ M , forXij ≤ M ⎩ Xij

, M = med {Xij } 1≤i≤m

(4)

(d) For moderate criteria (no indicator within this case study), these indicators are neither considered as higher being better, nor lower 440

Brunei Indonesia Cambodia Laos Myanmar Malaysia Philippines Singapore Thailand Timor-Leste Vietnam

BN ID KH LA MM MY PH SG TH TL VN

10.9 10.6 12.9 13.4 6.8 23.3 1.5 2.9 21.0 6.5 10.0

A21

A11

1.3 2.5 7.9 6.3 8.4 2.9 2.0 1.6 4.3 11.6 2.2

A2

A1

Safer Product

B11 (%) 9 n/a 15 n/a n/a 23 1 11 26 n/a 34

−14.81 −15.43 7.38 15.19 46.59 0.63 −78.20 −17.62 2.13 −2.63 −16.98

B1

A31 (%)

A3

72 n/a n/a n/a n/a 77 80 n/a 58 n/a n/a

B12 (%)

Safer People

n/a 80 64 n/a 51 97 87 n/a 52 n/a 96

B13 (%) 88.00 17.23 15.81 22.15 13.87 53.45 44.73 85.12 40.98 24.25 5.27

C11 (%)

C1

5 0 0 0 0 7 0 0 3 0 0

C12

Safer System

82.30 57.00 n/a n/a 45.70 80.90 28.23 100 n/a n/a 75.89

C21 (%)

C2

9 10 10 7 10 10 10 10 2 10 10

C22 (%) 76.89 53.00 20.51 37.55 33.55 74.01 44.49 100.00 49.17 32.13 32.95

C31 (%)

C3

41344 3492 1090 1760 1204 10933 2871 56287 5519 1280 2052

C32 (USD) 78.8 68.9 68.4 66.2 65.9 74.7 68.2 83.0 74.4 68.2 75.8

C33 (years) 96.1 92.8 73.9 72.7 92.8 93.1 95.4 96.5 96.4 58.3 93.5

C34 (%) 6 5 4 4 5 6 5 8 3 5 6

C41

C4

9 5 4 2 n/a 5 1 8 6 4 5

C42 6 8 5 2 n/a 4 5 8 6 2 6

C43 10 8 5 7 5 5 6 9 6 6 9

C44 9 10 9 9 5 10 10 9 9 9 9

C51

C5

2 4 4 4 10 4 4 2 8 2 6

C52 0.856 0.684 0.555 0.575 0.536 0.779 0.668 0.912 0.726 0.595 0.666

HDI

441

Brunei Indonesia Cambodia Laos Myanmar Malaysia Philippines Singapore Thailand Timor-Leste Vietnam

BN ID KH LA MM MY PH SG TH TL VN

1.5 4.3 11.0 7.8 10.6 3.4 10.5 2.0 4.8 78.7 3.3

13.5 13.0 12.8 12.7 5.1 24.2 7.4 3.8 19.9 6.8 12.6

86.21 88.75 17.54 29.93 50.43 9.39 485.74 −9.81 10.20 65.22 −13.84

9 n/a 16 50 n/a 23 1 11 26 n/a n/a

B11 (%) 72 n/a n/a n/a n/a 77 87 n/a 61 n/a n/a

B12 (%)

A21

A11

A31 (%)

B1

A2

A1

A3

Safer People

Safer Product

n/a 80 65 75 51 76 87 n/a 53 n/a 90

B13 (%) 88.00 17.25 16.94 19.45 17.86 53.23 47.52 63.00 39.19 23.71 5.17

C11 (%)

C1

5 0 5 3 3 7 7 2 7 5 7

C12

Safer System

80.06 57.01 8.07 13.68 48.40 80.45 n/a 100 n/a n/a 53.29

C21 (%)

C2

9 9 10 9 10 9 7 10 2 9 10

C22 (%)

76.22 51.49 20.14 35.37 32.47 72.53 44.81 100.00 46.68 30.83 31.67

C31 (%)

C3

41809 3701 948 1446 1421 10508 2606 54578 5449 1105 1755

C32 (USD)

78.4 70.6 71.4 67.8 64.9 74.8 68.6 82.0 74.2 67.0 75.6

C33 (years)

96.1 92.8 73.9 72.7 89.9 93.1 95.4 96.4 96.4 58.3 93.5

C34 (%)

6 4 7 6 5 5 3 7 3 4 7

C41

C4

9 5 3 3 5 4 0 9 5 3 3

C42

6 8 5 2 n/a 4 8 8 6 2 7

C43

10 8 6 8 6 5 5 9 6 5 9

C44

9 4 9 9 9 9 10 9 9 5 9

C51

C5

2 4 8 4 6 10 4 2 4 2 2

C52 0.855 0.629 0.543 0.543 0.498 0.769 0.654 0.895 0.690 0.576 0.617

HDI

Notes: 1. Data on C12, C31, C32, C33, C41, C42, C43, C44, C51, C52 is for 2012; while data on B11, B12, B13, C21, C22, C34 is for 2012 or the most recent year for which the data was available. Data on A11, A21, A31, C11 is for 2011 or the most recent year for which the data was available. Data on HDI is for 2012 released by UNDP in 2013. 2. n/a = not available.

Country

ISO Code

Table 2 Data set II (year 2013).

Notes: 1. Data on C12, C31, C32, C33, C41, C42, C43, C44, C51, C52 is for 2014; while data on B11, B12, B13, C21, C22 is for 2014 or the most recent year for which the data was available. Data on A11, A21, A31, C11, C34 is for 2013 or the most recent year for which the data is available. Data on HDI is for 2014 released by UNDP in 2015. 2. n/a = not available.

Country

ISO Code

Table 1 Data set I (year 2015).

F. Chen et al.

Accident Analysis and Prevention 106 (2017) 437–449

Accident Analysis and Prevention 106 (2017) 437–449 Notes: 1. Data on C12, C31, C32, C33, C41, C42, C43, C44, C51, C52 is for 2008; while data on B11, B12, B13, C21, C34 is for 2008 or the most recent year for which the data was available. Data on A11, A21, A31, C11 is for 2007 or the most recent year for which the data was available. Data on HDI is for 2008 released by UNDP in 2009. 2. n/a = not available.

6 6 2 8 10 10 10 10 10 6 2 4 7 7 2 6 10 6 10 10 7 7 9 7 2 6 6 6 n/a 9 4 5 7 37799 2168 743 900 904 8487 1929 39722 4102 673 1165 81.10 59.11 6.29 13.54 49.80 79.90 9.90 100 94.00 43.05 47.62 3.57 47.70 87.50 42.72 25.23 0.95 23.31 10.88 −9.25 31.43 6.67 Brunei Indonesia Cambodia Laos Myanmar Malaysia Philippines Singapore Thailand Timor-Leste Vietnam BN ID KH LA MM MY PH SG TH TL VN

1.0 2.6 100.1 9.5 15.7 3.7 2.1 2.5 4.9 17.3 5.6

7.4 7.1 10.7 10.4 3.4 23.6 1.3 4.8 19.6 4.0 14.6

4 n/a n/a 48 n/a n/a n/a 7 4 n/a 34

70 85 0 10 11 70 52 50 56 8 10

98 93 21 77 60 90 34 56 27 70 10

88.00 27.00 16.00 19.00 33.00 50.00 51.00 63.00 36.00 28.00 5.00

1 7 1 1 7 10 0 10 0 1 10

2 2 2 10 10 10 10 10 2 7 10

74.75 48.33 19.53 30.84 30.36 69.23 45.79 100.00 41.42 28.20 29.13

77.6 69.7 69.5 66.0 64.2 74.2 67.9 80.8 73.3 64.8 75.0

94.9 92.0 76.3 72.7 89.9 91.9 93.4 94.4 94.1 50.1 90.3

7 3 1 5 5 6 3 8 2 0 6

6 n/a n/a 3 5 6 n/a 8 5 0 3

7 7 1 n/a n/a 6 3 8 5 0 3

C51 C44 C41 C32 (USD) C31 (%) C21 (%) C12 C11 (%) B12 (%) B11 (%) A31 (%) A11

A21

B1 A3 A2 A1

Country ISO Code

Table 3 Data set III (year 2009).

Safer Product

Safer People

B13 (%)

C1

Safer System

C2

C22 (%)

C3

C33 (years)

C34 (%)

C4

C42

C43

C5

C52

HDI

0.920 0.734 0.593 0.619 0.586 0.829 0.751 0.944 0.783 0.489 0.725

F. Chen et al.

being better.

X ′ij =

Xij∗ − X ∗j min X ∗j max − X ∗j min

M

;

Xij∗

=

⎧ Xij , forXij ≥ M ⎨ Xij , forX ≤ M ij ⎩M

, M = med {Xij } 1≤i≤m

(5)

Step 3: Code rank for the alternatives and criteria (a) Perform the rank transformation for the m alternatives with respect to each criterion.

RijA = 1 + (m − 1) X ′ij

(6)

The rank transformation decision matrix for the m alternatives is: a a ⎡ r11 r12 a a ⎢ r r R A = ⎢ 21 22 ⎢⋮ ⋮ ⎢ rma1 rma2 ⎣

⋯ r1an ⎤ ⋯ r2an ⎥ ⎥ ⋱ ⋮ ⎥ a ⋯ rmn ⎥ ⎦

(7)

(b) Perform the rank transformation for the n criteria with respect to each alternative.

RijC = 1 + (n − 1) X ″ij ; X ″ij =

X ′ij − X ′i min X ′i max − X ′i min

(8)

The rank transformed decision matrix for the n criteria is: c c ⎡ r11 r12 c c ⎢ r r RC = ⎢ 21 22 ⎢⋮ ⋮ ⎢ rmc1 rmc2 ⎣

⋯ r1cn ⎤ ⋯ r2cn ⎥ ⎥ ⋱ ⋮ ⎥ c ⋯ rmn ⎥ ⎦

(9)

Step 4: Weighting the criteria The Rank-sum ratio (RSR) value for each criterion: m

∑i = 1 rijc

RSRCj =

m×n

, j = 1,2, ⋯n

(10)

The weight of each of the criteria is calculated as:

wj =

RSRCj n ∑k = 1

RSRkC

, j = 1,2, ⋯, n (11)

Step 5: Calculate the aggregated performance score The Rank-sum ratio (RSR) value of each alternative is obtained as: n

RSRiA =

∑ j = 1 rija wj m

, i = 1,2, ⋯m

(12)

Step 6: Determine the distribution of the RSR Arrange the set of alternatives in ascending order sorted by RSRi value. List the frequencies fi and cumulative frequencies f↓i, then calculate the percentiles Pi. Then determine the corresponding probit Yi on the basis of this percentiles Pi. Probit Yi is the probit transformation of Pi by means of normal distribution N(0, 1). Probit Y is a common transformation for linearising sigmoid distributions of proportions (Armitage et al., 2008), which is defined as 5 plus the 1-P quantile from the standard normal distribution N(0, 1), where P is a proportion.

Pi =

⎧

f ↓i m

× 100%, i = 1,2, ⋯, m − 1

⎨ (1 − ⎩

1 ) 4m

× 100%, i = m

(13)

Step 7: Construct the regression equation Perform a linear regression that includes independent variable of the probit Y value against the RSR value as the dependent variable.

RSR = a + bY

(14)

where: a, b are regression parameters. Test the regression's statistical significance by means of Analysis of Variance, which is calculated at the 95% confidence level (P < 0.05). Step 8: Grouping the alternatives Choose the appropriate grouping number based on actual 442

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circumstance (e.g. the amount of alternatives). The corresponding percentiles P* and probit Y* are obtained subsequently, according to the chosen grouping number. The common grouping number and its corresponding percentiles P* and probit Y* as well as the calculation example are given in Appendix A. Then calculate the class interval value RSR* (critical value of grouping) using the developed regression equation.

Table 5 The distribution of the RSR value for the countries.

R

RSR

f

LA TL KH MM TH VN MY ID PH BN SG

(15)

RSR* = a + bY *

↓

Country

Grouping the alternatives by their RSR value according to the class interval. Next, perform the Test of Homogeneity of Variances and Analysis of Variance, to ensure that the grouping is of statistical significance; i.e. the variance of each classification is of consistency (P > 0.05), and the differences between groups is statistically significant (P < 0.05). Otherwise, re-choose the grouping number and regroup the alternatives. This step is of crucial importance and indispensable, as the results provided determine whether or not the grouping is meaningful and acceptable as the basis of decision-making for policymakers. Step 9: Identify areas of poor performance The alternatives in the groups that obtain relatively high performance score (RSR value) can be considered as leaders, with significant progress and momentum already established under the support of existing implementation of country-specific and/or regional action plans and strategies. On the other side, the groups with poor performance would be the laggards, of which the progress toward the desired impacts would hugely benefit from the early detection of problems and identification of delays, and more importantly, action plans for the alternatives of these groups can be pointedly managed; so that remedial action can be taken as needed to bring these groups back on schedule.

a

0.449 0.453 0.456 0.457 0.634 0.653 0.670 0.678 0.728 0.770 0.836

R

1 1 1 1 1 1 1 1 1 1 1

1 2 3 4 5 6 7 8 9 10 m = 11

1 2 3 4 5 6 7 8 9 10 11

Calculated using the equation of Pm = (1 −

1 2 3 4 5 6 7 8 9 10 11 1 ) 4m

Pi =

R × 100% m

9.09 18.18 27.27 36.36 45.45 54.55 63.64 72.73 81.82 90.91 97.73a

Y

3.6648 4.0914 4.3953 4.6511 4.8857 5.1143 5.3489 5.6047 5.9086 6.3352 7.0010

× 100% .

As shown in Table 4, SG and BN are the leading countries in the ASEAN with the best road safety progress across the three different years. TL is the laggard, far away from SG and BN. As can also be seen, ID made significant progress with the largest positive change, while some countries, e.g. KH and MM, had large negative change. The remaining countries appear to have a fluctuation phenomenon to a certain extent. As a whole, the results provide a sound overall picture of the road safety progress that the ASEAN countries made. 3.2. Countries grouping The country rankings based on different analyses will probably never be identical, due to various reasons (e.g. data quality, method of analysis) (Wegman et al., 2008). It is, therefore, more meaningful to form groups of countries with similar safety progress. In this study, the countries will be classified into several groups based on the safety progress score (RSR value). The distribution of the RSR value (year 2015) for the countries is shown in Table 5. It is appropriate to group the 11 countries into three classes, considering the amount of countries. According to Table A1 presented in Appendix A, the percentiles P* and probit Y* are determined by the chosen grouping number. Based on the RSR and Y value in Table 5, the regression equation is estimated as shown in Table 6. Then, the class interval value RSR* (critical value of grouping) can be calculated by means of the regression equation. Thus, the countries can be classified into three groups based on the interval value RSR*. The three groups of countries are presented in Table 6.

3. Results In this section, the RSR-based model was employed to monitor the progress in implementing national and regional road safety action plans, producing a sound overall picture of road safety for the 11 Southeast Asian countries (10 ASEAN Member States plus Timor-Leste). 3.1. Overall picture of road safety progress By means of the 20 hierarchical SPIs in regards to Safer Product, Safer People, and Safer System, the overall road safety progress scores related to year 2009/2013/2015 are computed for the 11 ASEAN countries using the RSR-based monitoring model. They are presented in Table 4, together with rankings by using the Human Development Index (HDI) released in year 2009/2013/2015 respectively. Table 4 The rankings based on different years. Country

RSR score and corresponding ranking in different years 2009 RSR score

BN ID KH LA MM MY PH SG TH TL VN

0.771 0.272 0.653 0.481 0.779 0.617 0.663 0.931 0.635 0.436 0.571

2013 Ranking

2015

RSR score

RSR

HDI

3 11 5 9 2 7 4 1 6 10 8

2 6 9 8 10 3 5 1 4 11 7

0.789 0.616 0.596 0.535 0.694 0.615 0.628 0.858 0.571 0.442 0.742

443

Ranking

RSR score

RSR

HDI

2 6 8 10 4 7 5 1 9 11 3

2 6 10 9 11 3 5 1 4 8 7

0.770 0.678 0.456 0.449 0.457 0.670 0.728 0.836 0.634 0.453 0.653

Ranking RSR

HDI

2 4 9 11 8 5 3 1 7 10 6

2 5 10 9 11 3 6 1 4 8 7

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Table 6 Three groups of the countries. Group

Road safety development level

Percentiles P*

Probit Y*

RSR* = −0.0814 + 0.1347Y*

I II III

High Medium Low

84.134— 15.866— < 15.866

6— 4— <4

0.727— 0.458— < 0.458

a

a

Country SG, BN, PH ID, MY, VN, TH MM, KH, TL, LA

At 0.05 significance level, the regression equation is statistically significant (P < 0.001, R2 = 0.918).

2013, and 2015). As seen in Fig. 3, SG, the country which was the top performer in road safety development in 2009, was still the top performing 4 or 6 years later. This is not surprising, as SG is leading the way in almost all sectors regarding to road safety in the ASEAN region. On the other hand, a dramatically positive shift in the relative ordering of ID is observed. Seeing it move from being a bottom performer in 2009 to the fourth best performer in 2015. This is a significant and inspirational change that may be mainly attributed to the downtrend change in fatalities, increase awareness towards road safety education involving drink-driving, and more funding invested to finance road safety activities. In contrast, some countries, e.g. KH, LA and MM, remained regressive; among which MM exhibited the largest negative change, moving from rank two in 2009 to rank eight in 2015; due to a doubling the fatalities per 100,000 inhabitants. Whist, VN made great progress from 2009 to 2013, from rank eight to rank three, with dramatic improvements in seat belt and helmets use; from 2013 to 2015 it suffered a decline in its relative position, slipping three places to be rank six, due to a rise in deaths involving alcohol and poor enforcement of vehicle safety standards.

Table 7 Test of homogeneity of variances. Levene Statistic

df1

df2

Sig. (P)

2.456

2

8

0.147

Table 8 Analysis of variance.

Between Groups Within Groups Total

Sum of Squares

df

Mean Square

F

Sig. (P)

0.169 0.037 0.206

2 8 10

0.084 0.005

18.282

0.001

To test the statistical significance of the grouping, the Test of Homogeneity of Variances and the Analysis of Variance are performed in SPSS Statistics 20, The results are respectively shown in Tables 7 and 8. As shown in Tables 7 and 8 the variance of each group is consistent (P = 0.147 > 0.05), and the differences between groups are significant (P = 0.001 < 0.05). The results indicate that the grouping of the countries is statistically meaningful and acceptable.

4.2. De-constructing the overall progress The overall picture of road safety progress provides a starting point for analysis. However, it should be kept in mind that the overall safety progress is measured from the composition of sub-components and individual indicators. In order to better understand the progress in terms of guiding policy-making, it is necessary to take a closer look at the data behind each subcomponent or individual indicator; requiring some backtracking to the details that underlie the panoramic picture. This can further shed light on the overall progress of a given country, whilst simultaneously providing insight towards its safety problems, potential delays, and where interventions should be made in the future. The decomposition of the road safety progress for the latest stage (year 2013–2015) is shown in Fig. 4. It can be seen from Fig. 4 that:

4. Discussions 4.1. Cross-country comparisons of road safety development 4.1.1. Transverse comparison The indicator (RSR score) measuring road safety development could be linked to socio-economic development, e.g. Human Development Index, as with a good performance on road safety progress, it would be expected that the well-being and lives of the population would also be improved; as road accidents can contribute significantly to perpetuating or even increasing poverty, cause adverse social impacts such as serious physical, psychological, and material harm to the victims and their families, as well as huge economic losses. Correlating the rankings based on safety development (RSR score) with the ones based on HDI exhibits a link which is illustrated in Fig. 2. Fig. 2 shows that the rankings of most countries by safety progress across the three years are closely correlated to their HDI ranking. This indicates that the variation in the two data sets is similar. Although, a change in the HDI does not necessarily lead to a change in the road safety progress and vice versa. However, countries with high HDI might invest more in education, infrastructure and health; which might in turn lead to safer road user behavior, a safer road environment, better trauma management, etc. Hence a higher road safety development as expected. This comparison helps to gauge both the explanatory power of the monitoring result, reinforcing its credibility and acceptability.

• Overall, countries like SG and BN confirm their position among the

•

4.1.2. Longitudinal comparison To capture insight into the related progress in road safety across the three years (year 2009, 2013 and 2015), the comparison of countries' rankings based on the RSR score is illustrated in Fig. 3. One point of interested in this study is the relative performance of the 11 countries considered at the three points in time (year 2009, 444

top performers in regard to Traffic Risk (A1), Safer Roads (C2), Socio-economic Level (C3), and Traffic Police and Enforcement (C4). These two countries are the leaders in almost all respects. PH and ID are very strong in Road User Behavior (B1) and Safer Roads (C2), but weaker in Safer Vehicles (C1). MY’s performance is robustly balanced with respect to every dimension, representing a sound momentum. The same is true of VN, apart from a seriously downfall in respects to Safer Vehicles (C1). The remaining countries would be the laggards, significantly far away from SG and BN. Seven countries (i.e. SG, BN, PH, ID, MY, VN and TH) constitute the league of relatively well-performing countries with fatality rates per 10 000 registered motor vehicles (Traffic Risk) of five or less (see Table 1). In terms of Personal Risk, the best-performing countries are SG, PH, MM and TL with a fatality rate of less than 2 deaths per 100 000 inhabitants. In terms of Change Trend, the number of road fatalities has declined about 13.9% overall between 2010 and 2013 in the ASEAN countries for which data is consistently available and verifiable. However, this decline is not consistent with all of the respective countries Change Trends: Six countries managed to

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Fig. 2. Comparison of each country's respective RSR and HDI rankings in 2009, 2013 and 2015.

•

reduce their road death toll, whilst five countries saw an increase in fatalities. Among which, the greatest improvements were in PH with a dramatically reduction (-78.2%), while SG, BN, ID and VN all had reductions of 15–20% (see Table 1). Road accident prevention is best improved by concentrating on tackling offensive and unsafe driver behavior on key risk factors for road traffic injuries. These include speed, drink–driving, failure to

•

use helmets/seat-belts and mobile-phone use while driving. In this regard, SG, BN, PH, ID and MY were the strongest. This is partly a reflection of the effective traffic police enforcement in these countries. Vehicle safety standards are necessary to ensure unsafe vehicles are not imported. Periodic, effective and thorough vehicle inspection has the greatest potential to improve the safety and resource Fig. 3. Comparison showing the change in each country's respective ranking based on the RSR score over the three time periods of 2009, 2013 and 2015.

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Fig. 5. Plot of the 11 countries in the two-dimensional plane resulting from MCA.

Fig. 4. Decomposition of the overall road safety development (year 2013–2015) for each country respectively.

•

•

•

•

reality, all countries have room for improvement, by making more funding and resources available for the work required.

efficiency of vehicles, which is the most important means to ensure that the overall standard of vehicles is gradually raised. SG, BN, PH and MY have invested heavily in Safer Vehicles, whilst the remaining countries do not appear to have much effective activity in this respect. It is worth mentioning that some countries like MM and KH imported secondhand vehicles that have the steering wheel on the opposite side from neighboring countries, significantly increasing the risk of road accidents. In this regard, significant improvements are required in most ASEAN countries. Road safety audits on new or rehabilitated roads is the crucial process to ensure that road networks are in operation at a safe standard, which has a clear impact on the number of accidents (Proctor et al., 2001). SG and MY were the strongest in this sector, whilst TH and LA need very significant improvements. For these far behind countries the introduction of compulsory safety audits, and training of specialists capable of carrying out these audits, should be a high priority. It is worth noting that TL, which is usually the laggard, was one of the best-performers in this regard. As mentioned previously, the ASEAN countries covers a region of immense contrasts with respect to socio-economic factors, such as economic level, motorization level and urbanization level, which has a significant effect on the fatality rate (Bester, 2001). SG and BN as the wealthy and fully developed countries perform again the best in this area, while KH, TL and LA as the poor underdeveloped countries have a sizable scope for significant improvement. Significant safety improvements could be made by effective traffic police and law enforcement supported by ample resources (e.g., more staff, more or better equipment and more vehicles). In this sector, SG and BN appeared strongest, with the countries like PH, KH, TL and LA trailing far behind. This may be attributable in part to the very limited resources available in these less-developed countries. However, some improvements can still be achieved with the limited resources available to these countries, such as enforcement of motorcycle helmet wearing. Organizational performance measures the funding level spent on road safety, and highlights the countries ambition towards target setting in regards to fatality reduction. TH appears reasonably well resourced, PH, ID and MY are the next strongest, whilst MM have yet to do much in this sector, needing significant improvements. In

4.3. Verification of the grouping Due to a variety of reasons (e.g. method of analysis), the country groupings from different analyses will probably never be identical. To better gauge the reliability of the countries grouping, Multiple Correspondence Analysis (MCA) is also conducted in SPSS Statistics 20 to further cluster the countries, as a reference. The outcomes for the countries across the two dimensions from MCA are displayed in Fig. 5. As illustrated in Fig. 5, countries that are close together have more in common than those that are far apart. SG is rather close to BN. VN, MY, ID, PH and TL are close to each other. TH is somewhat isolated, but closest to MY and ID on the first dimension. MM, KH and LA are close to each other on the second dimension, but not on the first. As such, three groups of countries can be distinguished from Fig. 5, shown in Table 9. The countries marked with * in Table 9 are similarly grouped as with the RSR-based model proposed in this study. Overall, the groups derived from the two methods are almost identical, improving the reliability of the grouping. The final grouping based on the RSR-based model are shown in Table 10. The international map of the ASEAN countries is developed to Table 9 Grouping of countries derived from different analyses. Country

SG BN PH ID MY VN TH MM KH TL LA

446

Grouping derived from two analyses RSR-based model

MCA

1 1 1 2 2 2 2 3 3 3 3

1* 1* 2 2* 2* 2* 2* 3* 3* 2 3*

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proxy of speeding. Speeding, one of the key risk factors alongside drink–driving, and use of helmets, seat-belts and child restraints, is a major road safety problem in all countries. However, detailed data such as the percentage of drivers exceeding the speed limit, which allows for more in-depth safety performance analysis, is unfortunately not available for the ASEAN countries. Fortunately, many countries have been able to implement and enforce effective legislation to reduce speeding within a relatively short timeframe. Thus, the law enforcement data on speed limits are used as a proxy. Finally, and further compounding the weakness of the second limitation, is the fact that the law enforcement scores on various elements of national road safety legislation are subjective and should be seen only as an indication of how law enforcement is perceived in the country. These scores are directly extracted from the Global Status Report on Road Safety published by the WHO (see World Health Organization, 2009, 2013, 2015) and were rated by respondents, as individuals, based on their professional opinion or perception. For details on the law enforcement data collection and validation see Explanatory Note 2 in World Health Organization (2015) pp. 68.

Table 10 Final grouping of the 11 countries. Final Grouping

Road safety development level

Country

I II III

High Medium Low

SG, BN, PH ID, MY, VN, TH MM, KH, TL, LA

indicate the geographical distribution of countries grouping in relation to road safety development level and is shown in Fig. 6. It can be seen from Table 10 and Fig. 6 that SG, BN and PH are grouped into the first class (Group I), making the greatest progress toward the maximum possible level of road safety development. MM, KH, TL and LA have a low level of road safety development, being classified in Group III. These countries with low level of road safety development have more room for significant improvement, achieved through the completion of action plan implementation in future years (if any), than those classified in Group I with a high starting level. The remainder countries are allocated at the medium progressive level (Group II) of road safety development. The comparisons of the results indicated the feasibility of the application of RSR-based model for regular monitoring of country wide and regional action plans. The final results provide the local policy makers with a reference about where they stand and where they are going in relation to road safety, providing them with a solid base towards their next phase of action program formulation (e.g. policy making, target setting, and priority alignment).

6. Conclusion By means of the case study, this paper contributes to the literature on this topic and practice in several ways. Firstly, the indicators involved in the ASEAN acknowledged Road Safety Development Index developed by Al-Haji (2005, 2007) were updated in this paper; as in recent years the availability, accessibility and quality of data required for this framework has dramatically improved in the ASEAN region (with support from ADB-ASEAN Regional Road Safety Program). This improved indicator framework provides a new starting point for the monitoring of road safety development in the ASEAN region. Secondly, this paper completed the whole monitoring process based only on one method − RSR-based model. This allows the analysis easier to adapt and reproduce by the performer without requiring an excessive amount of additional knowledge, skill and effort, compared to other approaches, e.g. the SUNflower approach, where more techniques are needed to complete this process, including Principal Component Analysis (PCA), the weighted sum approach, and Multiple Correspondence Analysis (MCA), Thirdly, this paper conducts the mathematical

5. Limitations of the study This study suffers two main limitations. First, this paper combined traffic risk (fatalities per 10,000 vehicles) and personal risk (fatalities per 100,000 inhabitants) as the indicators of road safety outcomes; whilst the most acceptable measure would have been fatalities per person- or vehicle-km travelled (Papadimitriou et al., 2013). Unfortunately, the total number of kilometers travelled by vehicles/passengers is not routinely collected in most ASEAN member states. Second, this paper used the law enforcement data on speed limit as a

Fig. 6. Geographical distribution of the ASEAN countries related to the relative level of road safety development (RSD Level) (year 2013–2015).

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progressively improved, with potential delays and problems detected, and timely interventions implemented. From the above points of view, future progression towards road safety development monitoring in the ASEAN region suggest that it would be preferable to use the RSR-based model which require less additional effort, combined with the use of the updated Road Safety Development Index (RSDI) framework which is originally applied in the ASEAN region.

methodology processes in a refined manner, seamlessly integrating weighting, aggregating, and grouping in a ‘one-stop' procedure. This enables the policymakers of individual countries and the ASEAN Secretariat to perform the monitoring step-by-step; which is of particular significance considering that in the ASEAN region there is a lack of well-resourced teams that contain multidisciplinary safety professionals, and specialists in individual countries, who are able to carry out this work effectively. Moreover, the proposed monitoring model includes both descriptive and deductive statistics, integrating the strongpoints of both classical parametric and modern nonparametric estimations. The mathematics of which are far more manageable when compared with the previous methods such as Singular Value Decomposition (SVD), where complex orthogonal matrices operation is involved. For those practitioners who are not specialists in the area, the application of the RSR-based model would be more understandable, featuring increased visualization and clarity. Overall, the RSR-based model monitoring model provides a brand new and easy-to-use approach for reporting on the progress and development of road safety; allowing existing road safety practices to be

Acknowledgments This research was jointly supported by the Chinese Government Scholarship (CSC No. 201606260077), the Fundamental Research Funds for the Central Universities P.R. China (Grant No. 310821153402), and the Transportation Science & Technology Projects of Shaanxi province P.R. China (Grant No. 16-39K & 15-26K). The authors would like to thank the Associate Editor, Dr. Jeremy Broughton, and four anonymous reviewers for their valuable comments and suggestions, which have been of great help in improving the quality of this paper.

Appendix A The example of percentiles P* and probit Y*, conditional on a given grouping number, are given as follows (see also Tian, 1993). 6 Assume that a line segment is 6 cm ( −3 + 3 = 6) long and is expected to be divided into 5 grades. Thus, 5 = 1.2 is used as unit length. The Table A1 Number of grouping and the corresponding percentiles and probit. Grouping number

Percentile P*

Probit Y*

3

< 15.866 15.866— 84.134— < 6.681 6.681— 50— 93.319— < 3.593 3.593— 27.425— 72.575— 96.407— < 2.275 2.275— 15.866— 50— 84.134— 97.725— < 1.618 1.618— 10.027— 33.36— 67.003— 89.973— 98.352— < 1.222 1.322— 6.681— 22.663— 50— 77.337— 93.319— 98.678— < 0.99 0.99— 4.746— 15.866— 37.07— 62.93— 84.134— 95.254— 99.01—

<4 4— 6— < 3.5 3.5— 5— 6.5— < 3.2 3.2— 4.4— 5.6— 6.8— <3 3— 4— 5— 6— 7— < 2.86 2.86— 3.72— 4.57— 5.44— 6.28— 7.14— < 2.78 2.78— 3.5— 4.25— 5— 5.75— 6.50— 7.22— < 2.67 2.67— 3.33— 4— 4.67— 5.33— 6— 6.67— 7.33—

4

5

6

7

8

9

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grouping is made on the basis of the differential of normal distribution u.

The normal curve area P can be determined by checking the Standard Normal Curve Areas Table, and the corresponding probit Y can be determined by means of probit transformation.

For the readers’ reference, the common grouping number and its corresponding percentiles P* and probit Y* are given as shown in Table A1.

Hermans, E., Ruan, D., Brijs, T., Wets, G., Vanhoof, K., 2010. Road safety risk evaluation by means of ordered weighted averaging operators and expert knowledge. Knowl.Based Syst. 23, 48–52. International Road Federation (IRF), 2010. World Road Statistics. IRF, Geneva, Switzerland. Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., 2005. Handbook on ConstructingComposite Indicators: Methodology and User Guide. OECD Statistics Working. Papadimitriou, E., Yannis, G., Bijleveld, F., Cardoso, J.L., 2013. Exposure data and risk indicators for safety performance assessment in Europe. Accid. Anal. Prev. 60, 371–383. Proctor, S., Belcher, M., Cook, P., 2001. Practical Road Safety Auditing. Thomas Telford Publishing, London, United Kingdom. Shen, Y., Hermans, E., Brijs, T., Wets, G., Vanhoof, K., 2012. Road safety risk evaluation and target setting using data envelopment analysis and its extensions. Accid. Anal. Prev. 48, 430–441. Tian, F., 1988. Primary investigation of composite index and RSR. Chin. J. Health Stat. 4, 234–235. Tian, F., 1993. Grouping issue in the methodology of RSR. Chin. J. Health Stat. 10 (2), 26–28. United Nations Development Programme, 2009. Human Development Report, Overcoming Barriers: Human Mobility and Development. UNDP, New York. United Nations Development Programme, 2013. Human Development Report, The Rise of the South: Human Progress in a Diverse World. UNDP, New York. United Nations Development Programme, 2015. Human Development Report, Work for Human Development. UNDP, New York. Wegman, F., Oppe, S., 2010. Benchmarking road safety performances of countries. Saf. Sci. 48, 1203–1211. Wegman, F., Commandeur, J., Doveh, E., Eksler, V., Gitelman, V., Hakkert, S., Lynam, D., Oppe, S., 2008. SUNflowerNext: Towards a Composite Performance Index. SWOV, Leidschendam, The Netherlands. World Bank (WB), 2015. Data: Indicators. WB, Washington, D.C., USA. http://data. worldbank.org/indicator. (last Accessed 20 May 2016). World Health Organization, 2009. Global Status Report on Road Safety: Time for Action. WHO, Geneva, Switzerland. World Health Organization, 2013. Global Status Report on Road Safety: Supporting a Decade of Action. WHO, Geneva, Switzerland. World Health Organization, 2015. Global Status Report on Road Safety. WHO, Geneva, Switzerland.

References ASEAN Road Safety Centre (ARSC), 2015. Informal Document WP.29-165-11 (165th WP.29, Agenda Item 6). Malaysian Institute of Road Safety Research, Malaysia. Al-Haji, G., 2005. Towards a Road Safety Development Index (RSDI). Development of an International Index to Measure Road Safety Performance. Linköping Studies in Science and Technology. Development of Science and Technology, Linköping University (Licentiate Thesis, No. 1174). Al-Haji, G., 2007. Road Safety Development Index (RSDI): Theory, Philosophy and Practice. Department of Science and Technology. Campus Norrköping, Linköping University, Norrköping (Ph.D. dissertation). Armitage, P., Berry, G., Matthews, J.N.S., 2008. Statistical Methods in Medical Research. John Wiley & Sons. Asian Development Bank (ADB), 2005. ARRIVE ALIVE − ASEAN Commits to Cutting Road Deaths. ADB, Manila, the Philippines. Asian Development Bank (ADB), 2012. Road Safety Action Plan − Implementation of Sustainable Transport Initiative: Mainstreaming Road Safety in ADB Operations Action Plan. ADB, Manila, the Philippines. Asian Development Bank (ADB), 2013. Learning Lessons: Improving Road Safety in Asia and the Pacific. ADB, Manila, the Philippines. Bax, C., Wesemann, P., Gitelman, V., Shen, Y., Goldenbeld, C., Hermans, E., Doveh, E., Hakkert, S., Wegman, F., Aarts, L., 2012. Developing a Road Safety Index. Deliverable 4.9 of the EC FP7 Project. DaCoTA. Bester, C.J., 2001. Explaining national road fatalities. Accid. Anal. Prev. 33, 663–672. Central Intelligence Agency (CIA), 2016. The World Factbook. CIA, Washington, D.C., USA. https://www.cia.gov/library/publications/resources/the-world-factbook/. (last Accessed 20 May 2016). Chen, F., Wang, J., Deng, Y., 2015. Road safety risk evaluation by means of improved entropy TOPSIS-RSR. Saf. Sci. 79, 39–54. Chen, F., Wu, J., Chen, X., Wang, J., Wang, D., 2016. Benchmarking road safety performance: identifying a meaningful reference (best-in-class). Accid. Anal. Prev. 86, 76–89. UN Economic and Social Commission for Asia and the Pacific (ESCAP), 2016. ESCAP Statistical Database. http://www.unescap.org/stat/data/statdb/DataExplorer.aspx. (last Accessed 20 May 2016). Hermans, E., Brijs, T., Wets, G., 2008. Developing a theoretical framework for road safety performance indicators and a methodology for creating a performance index. In: RAMOW-2008-010. Steunpunt Mobiliteit and Openbare Werken ?Spoor Verkeersveiligheid. Diepenbeek, Belgium.

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