Road safety risk evaluation and target setting using data envelopment analysis and its extensions

Currently, comparison between countries in terms of their road safety performance is widely conducted in order to better understand one's own safety situation and to learn from those best-performing countries by indicating practical targets and formulating action programmes. In this respect, crash data such as the number of road fatalities and casualties are mostly investigated. However, the absolute numbers are not directly comparable between countries. Therefore, the concept of risk, which is defined as the ratio of road safety outcomes and some measure of exposure (e.g., the population size, the number of registered vehicles, or distance travelled), is often used in the context of benchmarking. Nevertheless, these risk indicators are not consistent in most cases. In other words, countries may have different evaluation results or ranking positions using different exposure information. In this study, data envelopment analysis (DEA) as a performance measurement technique is investigated to provide an overall perspective on a country's road safety situation, and further assess whether the road safety outcomes registered in a country correspond to the numbers that can be expected based on the level of exposure. In doing so, three model extensions are considered, which are the DEA based road safety model (DEA-RS), the cross-efficiency method, and the categorical DEA model. Using the measures of exposure to risk as the model's input and the number of road fatalities as output, an overall road safety efficiency score is computed for the 27 European Union (EU) countries based on the DEA-RS model, and the ranking of countries in accordance with their cross-efficiency scores is evaluated. Furthermore, after applying clustering analysis to group countries with inherent similarity in their practices, the categorical DEA-RS model is adopted to identify best-performing and underperforming countries in each cluster, as well as the reference sets or benchmarks for those underperforming ones. More importantly, the extent to which each reference set could be learned from is specified, and practical yet challenging targets are given for each underperforming country, which enables policymakers to recognize the gap with those best-performing countries and further develop their own road safety policy.     

Uncertainty assessment of the road safety index

Composite indicators aggregate domain-specific information in one index, on the basis of which countries can be assigned a relative ranking. Recently, the road safety community got convinced of the policy supporting role of indicators in terms of benchmarking, target setting and selection of measures. However, combining the information of a set of relevant risk indicators in one index presenting the whole picture turns out to be very challenging. In particular, the rank of a country can be largely influenced by the methodological choices made during the composite indicator building process. Decisions concerning the selection of indicators, the normalisation of the indicator values, the weighting of indicators and the way of aggregating can influence the final ranking. In this research, it is shown that the road safety ranking of countries differs significantly according to the selected weighting method, the expert choice and the set of indicators. From these three input factors, the selection of the set of indicators is most influential. A well considered selection of indicators will therefore establish the largest reduction in ranking uncertainty. With a set of appropriate indicators, the proposed framework reveals the major sources of uncertainty in the creation of a composite road safety indicator.     

An update on the association between setting quantified road safety targets and road fatality reduction

This note is intended to rectify estimates provided in a previous paper (Wong et al., 2006) of the short-term effectiveness in terms of road fatality reduction of the setting of quantified road safety targets using data from 14 OECD countries during the period 1980–1999. This work is important in measuring the association between target setting and road safety improvement, because such targets are intended to serve as a useful tool to motivate timely road safety measures by the road authorities and others. The estimates to be rectified were based on before-and-after analysis using a comparison group of countries for each country that had set a target. This note first provides a correction to the qualification test for the inclusion of a country in any particular comparison group. It then presents the numerical effects of this correction on the estimates of the effectiveness of setting quantified road safety targets, both in individual countries and across the whole group of countries that set targets in the relevant period. Finally, impacts on the findings of the previous paper are discussed, with the conclusion that the changes in those of the numerical estimates that are affected do not alter the main message of the paper.     

Is road safety management linked to road safety performance?

This research aims to explore the relationship between road safety management and road safety performance at country level. For that purpose, an appropriate theoretical framework is selected, namely the ‘SUNflower’ pyramid, which describes road safety management systems in terms of a five-level hierarchy: (i) structure and culture, (ii) programmes and measures, (iii) ‘intermediate’ outcomes’ – safety performance indicators (SPIs), (iv) final outcomes – fatalities and injuries, and (v) social costs. For each layer of the pyramid, a composite indicator is implemented, on the basis of data for 30 European countries. Especially as regards road safety management indicators, these are estimated on the basis of Categorical Principal Component Analysis upon the responses of a dedicated road safety management questionnaire, jointly created and dispatched by the ETSC/PIN group and the ‘DaCoTA’ research project. Then, quasi-Poisson models and Beta regression models are developed for linking road safety management indicators and other indicators (i.e. background characteristics, SPIs) with road safety performance. In this context, different indicators of road safety performance are explored: mortality and fatality rates, percentage reduction in fatalities over a given period, a composite indicator of road safety final outcomes, and a composite indicator of ‘intermediate’ outcomes (SPIs). The results of the analyses suggest that road safety management can be described on the basis of three composite indicators: “vision and strategy”, “budget, evaluation and reporting”, and “measurement of road user attitudes and behaviours”. Moreover, no direct statistical relationship could be established between road safety management indicators and final outcomes. However, a statistical relationship was found between road safety management and ‘intermediate’ outcomes, which were in turn found to affect ‘final’ outcomes, confirming the SUNflower approach on the consecutive effect of each layer.     

A statistical model to compare road mortality in OECD countries

The objective of this paper is to compare safety levels and trends in OECD countries from 1980 to 1994 with the help of a statistical model and to launch international discussion and further research about international comparisons. Between 1980 and 1994, the annual number of fatalities decreased drastically in all the selected countries except Japan (+ 12%), Greece (+ 56%) and ex-East Germany (+ 50%). The highest decreases were observed in ex-West Germany (- 48%), Switzerland (- 44%), Australia (- 40%), and UK (- 39%). In France, the decrease in fatalities over the same period reached 34%. The fatality rate, an indicator of risk, decreased in the selected countries from 1980 to 1994 except in the east-European countries during the motorization boom in the late 1980s. As fatality rates are not sufficient for international comparisons, a statistical multiple regression model is set up to compare road safety levels in 21 OECD countries over 15 years. Data were collected from IRTAD (International Road Traffic and Accident Database) and other OECD statistical sources. The number of fatalities is explained by seven exogenous (to road safety) variables. The model, pooling cross-sectional and time series data, supplies estimates of elasticity to the fatalities for each variable: 0.96 for the population; 0.28 for the vehicle fleet per capita; -0.16 for the percentage of buses and coaches in the motorised vehicle fleet; 0.83 for the percentage of youngsters in the population; - 0.41 for the percentage of urban population; 0.39 for alcohol consumption per capita; and 0.39 for the percentage of employed people. The model also supplies a rough estimate of the safety performance of a country: the regression residuals are supposed to contain the effects of essentially endogenous and unobserved variables, independent to the exogenous variables. These endogenous variables are safety performance variables (safety actions, traffic safety policy, network improvements and social acceptance). A new indicator, better than the mortality rate, is then set upon the residuals. Mean estimates of this indicator for the years 1980-1982 and the years 1992-1994 rank the countries in the beginning and at the end of the study period. Countries showing the best ranks (and thus the best performance) in 1980 and 1994 are Sweden, the Netherlands and Norway. The UK and Switzerland reach the top 5 in 1994. Greece, Belgium, Portugal and Spain are the last countries in the classification along with, surprisingly, the USA. France was ranked 18th in 1980 and 15th in 1994 but is ranked amongst the five countries that most improved from 1980 to 1994. This model remains non definitive because it is not able to distinguish between safety performance and unobserved exogenous variables although these exogenous variables could explain more about the differences in levels and trends between the countries. More complex models, particularly highly sophisticated models regarding the number of fatalities with breakdowns by road users or road classes would be needed to give a precise and profound ranking of safety levels and safety improvements between countries.     

Combining road safety information in a performance index

In this paper we focus on an essential step in the construction process of a composite road safety performance indicator: the assignment of weights to the individual indicators. In the composite indicator literature, this subject has been discussed for a long time, and no agreement has been reached so far. The aim of this research is to provide insights in the most important weighting methods: factor analysis, analytic hierarchy process, budget allocation, data envelopment analysis and equal weighting. We will give the essential theoretical considerations, apply the methods on road safety data from various countries and discuss their advantages and disadvantages. This will facilitate the selection of a justifiable method. It is shown that the position of a country in the ranking is influenced by the method used. The weighting methods agree more for countries with a relatively bad road safety performance. Of the five techniques, the weights based on data envelopment analysis resulted in the highest correlation with the road safety ranking of 21 European countries based on the number of traffic fatalities per million inhabitants. This method is valuable for the development of a road safety index.     

How would setting policy priorities according to cost-benefit analyses affect the provision of road safety?

This paper analyses how setting priorities for road safety strictly according to cost-benefit analysis would affect the provision of road safety in Norway and Sweden. The paper is based on recent analyses of the efficiency of road safety policies in these two countries. The argument sometimes made by critics of cost-benefit analysis, that only a few road safety measures are cost-effective (have benefits greater than costs), is not supported. Cost-effective road safety policies could prevent between 50 and 60% of the current number of road accident fatalities in both Norway and Sweden, if pursued consistently during a period of 10 years (2002-2011). If current policies are continued, only about 10-15% of the current number of road accident fatalities are likely to be prevented during the next 10 years. A number of sources of inefficiency in road safety policy are identified. A source of inefficiency is anything that prevents policy priorities from being set according to cost-benefit analysis. These include: (a) lack of power, which means that national governments do not have the formal authority to introduce a certain road safety measure, in Europe, this applies to new vehicle safety standards, which are passed almost exclusively by the European Union; (b) the existence of social dilemmas, which means that measures that are cost-effective from a societal point of view are not so from the point of view of individual road users; (c) priority given to other policy objectives, in particular regional development. Scarcity of resources, which obtains when public budgets have to be increased to make room for all cost-effective measures, was not found to be a constraint. All cost-effective measures can be funded within current budgets, provided the use of inefficient measures ceases.     

A comprehensive conceptual framework for road safety strategies

Road safety strategies (generally called Strategic Highway Safety Plans in the USA) provide essential guidance for actions to improve road safety, but often lack a conceptual framework that is comprehensive, systems theory based, and underpinned by evidence from research and practice. This paper aims to incorporate all components, policy tools by which they are changed, and the general interactions between them. A framework of nine mutually interacting components that contribute to crashes and ten generic policy tools which can be applied to reduce the outcomes of these crashes was developed and used to assess 58 road safety strategies from 22 countries across 15 years. The work identifies the policy tools that are most and least widely applied to components, highlighting the potential for improvements to any individual road safety strategy, and the potential strengths and weaknesses of road safety strategies in general. The framework also provides guidance for the development of new road safety strategies, identifying potential consequences of policy tool based measures with regard to exposure and risk, useful for both mobility and safety objectives.     

Development of road safety performance indicators for trauma management in Europe

Trauma management (TM) covers two types of medical treatment: the initial one provided by Emergency Medical Services (EMS) and a further one provided by permanent medical facilities. There is a consensus in the professional literature that to reduce the severity and the number of road crash victims, the TM system should provide rapid and adequate initial care of injury, combined with sufficient further treatment at a hospital or trauma centre. Recognizing the important role of TM for reducing road crash injury outcome, it was decided, within the EU funded SafetyNet project, to develop road safety performance indicators (SPIs) which would characterize the level of TM systems’ performance in European countries and enable country comparisons.     

Road safety performance indicators for the interurban road network

Various road safety performance indicators (SPIs) have been proposed for different road safety research areas, mainly as regards driver behaviour (e.g. seat belt use, alcohol, drugs, etc.) and vehicles (e.g. passive safety); however, no SPIs for the road network and design have been developed. The objective of this research is the development of an SPI for the road network, to be used as a benchmark for cross-region comparisons. The developed SPI essentially makes a comparison of the existing road network to the theoretically required one, defined as one which meets some minimum requirements with respect to road safety. This paper presents a theoretical concept for the determination of this SPI as well as a translation of this theory into a practical method. Also, the method is applied in a number of pilot countries namely the Netherlands, Portugal, Greece and Israel. The results show that the SPI could be efficiently calculated in all countries, despite some differences in the data sources. In general, the calculated overall SPI scores were realistic and ranged from 81 to 94%, with the exception of Greece where the SPI was relatively lower (67%). However, the SPI should be considered as a first attempt to determine the safety level of the road network. The proposed method has some limitations and could be further improved. The paper presents directions for further research to further develop the SPI.