Road safety development in Europe: A decade of changes (2001–2010)

To evaluate the road safety development of a country over time, the percentage change in the number of road fatalities is traditionally the main indicator. However, simply considering the reduction in the road fatalities may not correctly reflect the real improvement in road safety because the transport circumstances of a country underlying the road fatalities also change every year. In this study, we present a new way for measuring the road safety performance change over time, which is to use the technique of data envelopment analysis (DEA) and the Malmquist productivity index. In doing so, we can not only focus on the evolution of road safety final outcomes within a given period, but also take the changes of different measures of exposure in the same period into account. In the application, the DEA-based Malmquist productivity index (DEA-MI) is used to measure the extent to which the EU countries have improved their road safety performance over the period 2001–2010. More objective and insightful results are obtained compared to the ones based on the traditional indicator. The results show considerable road safety progress in most of the Member States during these ten years, and the fatality risk rather than the fatality number on Europe's roads has actually been reduced by approximately half. However, the situation differed considerably from country to country. The decomposition of the DEA-MI into ‘efficiency change’ and ‘technical change’ further reveals that the bulk of the improvement during the last decade was attained through the adoption of productivity-enhancing new technologies throughout the road transport sector in Europe, rather than through the relatively underperforming countries catching up with those best-performing ones.     

DEA cross-efficiency evaluation based on satisfaction degree: an application to technology selection

Data envelopment analysis (DEA) has been extended to cross-efficiency evaluation to provide better discrimination and ranking of decision-making units (DMUs). However, the non-uniqueness of optimal weights in the traditional DEA models (CCR and BCC models) has reduced the usefulness of the DEA cross-efficiency evaluation method. To solve this problem, we introduce the concept of the satisfaction degree of a DMU towards a set of optimal weights for another DMU. Then, a new DEA cross-efficiency evaluation approach, which contains a maxmin model and two algorithms, is proposed based on the satisfaction degrees of the DMUs. Our maxmin model and algorithm 1 can obtain for each DMU an optimal set of weights that maximises the least satisfaction degrees among all the other DMUs. Further, our algorithm 2 can then be used to guarantee the uniqueness of the optimal weights for each DMU. Finally, our approach is applied to a real-world case study of technology selection.     

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.     

Association between setting quantified road safety targets and road fatality reduction

Since the 1980s, many countries have set quantified road safety targets as part of their comprehensive road safety strategies. This paper studies the association between the setting of quantified road safety targets and road fatality reduction over the past two decades. Both aggregate and disaggregate levels of hypothesis tests are performed on data from 14 countries that set road safety targets during the period 1981-1999. A before-and-after analysis of the treatment and comparison groups is conducted, and the results show that the majority of countries with quantified road safety targets experienced a reduction in road fatalities in this period. Overall, the establishment of quantified road safety targets is found to have an appreciable association with an improvement in road safety.     

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.     

Supplier evaluation and selection: an augmented DEA approach

Evaluating and selecting suppliers is an essential part of effectively managing today's dynamic and global supply chains. In this paper, we propose a supplier evaluation and selection methodology based on an extension of data envelopment analysis (DEA) that can evaluate suppliers in an efficient manner. Through the incorporations of a range of virtual standards, the proposed methodology termed augmented DEA, has enhanced discriminatory power over basic DEA models to rank suppliers. In addition, weight constraints are introduced to reduce the possibility of having inappropriate input and output factor weights. We demonstrate the application of augmented DEA with comparison experiments and find that the augmented DEA model has advantages over the basic DEA model as well as the cross-efficiency and super-efficiency models. Finally, we present a case application with data obtained from a communication and aviation electronics company to demonstrate the applicability and use of augmented DEA.     

Relating traffic fatalities to GDP in Europe on the long term

Modeling road safety development can provide important insight into policies for the reduction of traffic fatalities. In order to achieve this goal, both the quantifiable impact of specific parameters, as well as the underlying trends that cannot always be measured or observed, need to be considered. One of the key relationships in road safety links fatalities with risk and exposure, where exposure reflects the amount of travel, which in turn translates to how much travelers are exposed to risk. In general two economic variables: GDP and unemployment rate are selected to analyse the statistical relationships with some indicators of road accident fatality risk.The objective of this research is to provide an overview of relevant literature on the topic and outline some recent developments in macro-panel data analysis that have resulted in ongoing research that has the potential to improve our ability to forecast traffic fatality trends, especially under turbulent financial situations. For this analysis, time series of the number of fatalities and GDP in 30 European countries for a period of 38 years (1975–2012) are used. This process relies on estimating long-term models (as captured by long term time-series models, which model each country separately). Based on these developments, utilizing state-of-the-art modelling and analysis techniques such as the Common Correlated Effects Mean Group estimator (Pesaran), the long-term elasticity mean value equals 0.63, and is significantly different from zero for 10 countries only. When we take away the countries, where the number of fatalities is stationary, the average elasticity takes a higher value of nearly 1. This shows the strong sensitivity of the estimate of the average elasticity over a panel of European countries and underlines the necessity to be aware of the underlying nature of the time series, to get a suitable regression model.     

Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries

In this paper a unified methodology is presented for the modelling of the evolution of road safety in 30 European countries. For each country, annual data of the best available exposure indicator and of the number of fatalities were simultaneously analysed with the bivariate latent risk time series model. This model is based on the assumption that the amount of exposure and the number of fatalities are intrinsically related. It captures the dynamic evolution in the fatalities as the product of the dynamic evolution in two latent trends: the trend in the fatality risk and the trend in the exposure to that risk. Before applying the latent risk model to the different countries it was first investigated and tested whether the exposure indicator at hand and the fatalities in each country were in fact related at all. If they were, the latent risk model was applied to that country; if not, a univariate local linear trend model was applied to the fatalities series only, unless the latent risk time series model was found to yield better forecasts than the univariate local linear trend model. In either case, the temporal structure of the unobserved components of the optimal model was established, and structural breaks in the trends related to external events were identified and captured by adding intervention variables to the appropriate components of the model. As a final step, for each country the optimally modelled developments were projected into the future, thus yielding forecasts for the number of fatalities up to and including 2020.     

Prediction of traffic fatalities and prospects for mobility becoming sustainable-safe

The macroscopic trend of road traffic fatalities in any motorized country is described and predicted by the product of rather well fitting functions of time for the exponential decay of fatality risk per unit of traffic volume and the S-shaped Gompertz function of traffic volume growth. This product defines a single-peaked development of road traffic deaths, where its peak reaches earlier the sooner and faster a nation or region motorizes massively. Since in developing countries long series of traffic volume data are absent, another model for the fit and prediction of road traffic fatalities for developing countries is used, based on the relationships of income level per capita with road traffic mortality. Also this model implies that at some point in time road traffic deaths will start declining for ever, also worldwide. After empirically derived corrections for missing or incomplete data and police under-reporting, it is estimated that 1·2 million deaths and almost 8 million serious injuries are caused by road traffic worldwide in 2000. Using realistic income level predictions the new income-dependent model predicts markedly later and higher fatality peaks than the verified time-dependent model. It might be assumed that the developing countries could learn faster to increase their road safety by knowledge transfer from developed countries. Four prediction scenarios are specified for modified income-dependent models of road traffic death and serious injury developments up to 2050. Depending on the scenario the world total of road fatalities begins to reduce soon or only after 2035 with a global peak of about 1·8 million road traffic deaths, where the national fatality reduction starts later the lower the national income per capita is. Without the potentially achievable learning scenario the road fatality reductions in developed countries may not be enough to compensate the road fatality increases in developing countries, while road fatality increases may even occur after 2060 in countries with the lowest levels of income per capita.     

DEA交叉模型融合熵值法的战役仓库航材保障能力评估

目的 为科学地检验评估空军战役仓库的航材保障能力,提出构建基于数据包络分析(DEA)法的航材保障能力评估模型.方法 首先确定能够表达战役仓库航材保障军事、经济特性的评估指标,建立基于数据包络分析法的评估模型,计算出各战役仓库的综合交叉效率值,以实现待评估战役仓库航材保障能力的排序.结果 运用模型对15个战役仓库进行实例分析,得出各战役仓库的交叉效率值为0.69048～0.82253,第15号战役仓库的效率值最高,与实际情况相符合;并通过对比验证了新模型具有更好的排序效果.结论 新模型可以为航材保障能力评估工作提供科学依据.     

DEA cross-efficiency aggregation method based upon Shannon entropy

Existing approaches for DEA cross-efficiency evaluation are mainly focused on the calculation of cross-efficiency matrix but pay little attention to the aggregation of the efficiencies in the cross-efficiency matrix. The most widely used approach is to aggregate the efficiencies in each row or column in the cross-efficiency matrix with equal weights into an average cross-efficiency score for each Decision Making Unit (DMU) and view it as the overall performance measurement of the DMU. This paper focuses on the aggregation process of the efficiencies in the cross-efficiency matrix and proposes the use of Shannon entropy for cross-efficiency aggregation. In the study, we propose an entropy model to generate a set of weights for aggregating and determining the ultimate cross-efficiency instead of the traditional average cross-efficiency. We prove that the set of weight is a unique global optimal solution which can reflect the goodness of this method. Finally, two examples of a flexible manufacturing system and 27 industrial robots are illustrated to examine the validity of the proposed method.     

Analysis of progress in road safety in ten European countries

Classic models for the long-term forecasting of the number of fatalities in road accidents are based on a decreasing exponential form of the rate of fatalities per vehicle×km. We decided to extend this simple model to incorporate intervention functions connected with the major safety measures introduced and to replace the deterministic trend by a stochastic one. Harvey's structural model, known as the local linear trend model, is applied to ten European countries. The relationship between the slope of this trend and the elasticity in terms of the number of vehicles×km yields an indicator of the rate of progress in road safety made in the different countries. The average rate is around −6% per annum, with a minimum of −4.5% and a maximum of −13.5% for Spain in 1994. Europe's road systems can thus absorb a 6% increase in traffic per annum while maintaining the number of fatalities constant.     

Scenario analysis of freight vehicle accident risks in Taiwan

This study develops a quantitative risk model by utilizing Generalized Linear Interactive Model (GLIM) to analyze the major freight vehicle accidents in Taiwan. Eight scenarios are established by interacting three categorical variables of driver ages, vehicle types and road types, each of which contains two levels. The database that consists of 2043 major accidents occurring between 1994 and 1998 in Taiwan is utilized to fit and calibrate the model parameters. The empirical results indicate that accident rates of freight vehicles in Taiwan were high in the scenarios involving trucks and non-freeway systems, while; accident consequences were severe in the scenarios involving mature drivers or non-freeway systems. Empirical evidences also show that there is no significant relationship between accident rates and accident consequences. This is to stress that safety studies that describe risk merely as accident rates rather than the combination of accident rates and consequences by definition might lead to biased risk perceptions. Finally, the study recommends using number of vehicle as an alternative of traffic exposure in commercial vehicle risk analysis. The merits of this would be that it is simple and thus reliable; meanwhile, the resulted risk that is termed as fatalities per vehicle could provide clear and direct policy implications for insurance practices and safety regulations.     

Benchmarking road safety: Lessons to learn from a data envelopment analysis

Road safety performance indicators (SPI) have recently been proposed as a useful instrument in comparing countries on the performance of different risk aspects of their road safety system. In this respect, SPIs should be actionable, i.e. they should provide clear directions for policymakers about what action is needed and which priorities should be set in order to improve a country’s road safety level in the most efficient way. This paper aims at contributing to this issue by proposing a computational model based on data envelopment analysis (DEA). Based on the model output, the good and bad aspects of road safety are identified for each country. Moreover, targets and priorities for policy actions can be set. As our data set contains 21 European countries for which a separate, best possible model is constructed, a number of country-specific policy actions can be recommended. Conclusions are drawn regarding the following performance indicators: alcohol and drugs, speed, protective systems, vehicle, infrastructure and trauma management. For each country that performs relatively poor, a particular country will be assigned as a useful benchmark.     

A perspective on road fatalities in Jeddah, Saudi Arabia.

The aim of this paper is to study an epidemiological aspect of fatalities from motor vehicle accidents in Jeddah, Saudi Arabia. All fatal accidents that occur in Jeddah between 1 January and 31 December 1987 are analyzed. The death rates per hundred million vehicle kilometers of travel, per 100,000 registered motor vehicles, and per 100,000 resident population in 1987 were about 1.87, 19.7, and 26.5, respectively. An attempt is made to use international death rates to compare Jeddah with other countries. The figures reveal higher death rates per vehicle travel and per resident population but lower rate per registered vehicle than in some industrialized countries. An additional attempt was made to estimate the cost of road traffic fatalities in Jeddah, which is an extremely difficult task due to lack of reliable data. This study shows that the cost of 1987 road fatalities in Jeddah is estimated to be 648.7 million Saudi Riyals (US$172.5 million).     

Effectiveness of maritime safety control in different navigation zones using a spatial sequential DEA model: Yangtze River case

This paper aims to analyze the effectiveness of maritime safety control from the perspective of safety level along the Yangtze River with special considerations for navigational environments. The influencing variables of maritime safety are reviewed, including ship condition, maritime regulatory system, human reliability and navigational environment. Because the former three variables are generally assumed to be of the same level of safety, this paper focuses on studying the impact of navigational environments on the level of safety in different waterways. An improved data envelopment analysis (DEA) model is proposed by treating the navigational environment factors as inputs and ship accident data as outputs. Moreover, because the traditional DEA model cannot provide an overall ranking of different decision making units (DMUs), the spatial sequential frontiers and grey relational analysis are incorporated into the DEA model to facilitate a refined assessment. Based on the empirical study results, the proposed model is able to solve the problem of information missing in the prior models and evaluate the level of safety with a better accuracy. The results of the proposed DEA model are further compared with an evidential reasoning (ER) method, which has been widely used for level of safety evaluations. A sensitivity analysis is also conducted to better understand the relationship between the variation of navigational environments and level of safety. The sensitivity analysis shows that the level of safety varies in terms of traffic flow. It indicates that appropriate traffic control measures should be adopted for different waterways to improve their safety. This paper presents a practical method of conducting maritime level of safety assessments under dynamic navigational environment.     

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.     

Determinants of road traffic safety: New evidence from Australia using state-space analysis

This paper examines the determinants of road traffic crash fatalities in Queensland for the period 1958–2007 using a state-space time-series model. In particular, we investigate the effects of policies that aimed to reduce drink-driving on traffic fatalities, as well as indicators of the economic environment that may affect exposure to traffic, and hence affect the number of accidents and fatalities. The results show that the introduction of a random breath testing program in 1988 was associated with a 11.3% reduction in traffic fatalities; its expansion in 1998 was associated with a 26.2% reduction in traffic fatalities; and the effect of the “Safe4life” program, which was introduced in 2004, was a 14.3% reduction in traffic fatalities. Reductions in economic activity are also associated with reductions in road fatalities: we estimate that a one percent increase in the unemployment rate is associated with a 0.2% reduction in traffic fatalities.     

Benchmarking road safety of U.S. states: A DEA-based Malmquist productivity index approach

In this study, a DEA based Malmquist index model was developed to assess the relative efficiency and productivity of U.S. states in decreasing the number of road fatalities. Even though the national trend in fatal crashes has reached to the lowest level since 1949 (Traffic Safety Annual Assessment Highlights, 2010), a state-by-state analysis and comparison has not been studied considering other characteristics of the holistic national road safety assessment problem in any work in the literature or organizational reports. In this study, a DEA based Malmquist index model was developed to assess the relative efficiency and productivity of 50 U.S. states in reducing the number of fatal crashes. The single output, fatal crashes, and five inputs were aggregated into single road safety score and utilized in the DEA-based Malmquist index mathematical model. The period of 2002–2008 was considered due to data availability for the inputs and the output considered. According to the results, there is a slight negative productivity (an average of −0.2 percent productivity) observed in the U.S. on minimizing the number of fatal crashes along with an average of 2.1 percent efficiency decline and 1.8 percent technological improvement. The productivity in reducing the fatal crashes can only be attributed to the technological growth since there is a negative efficiency growth is occurred. It can be concluded that even though there is a declining trend observed in the fatality rates, the efficiency of states in utilizing societal and economical resources towards the goal of zero fatality is not still efficient. More effective policy making towards increasing safety belt usage and better utilization of safety expenditures to improve road condition are derived as the key areas to focus on for state highway safety agencies from the scope of current research.