Road Safety Performance Indicators Research Articles

A road safety performance indicator for vehicle fleet compatibility

This paper discusses the development and the application of a safety performance indicator which measures the intrinsic safety of a country's vehicle fleet related to fleet composition. The indicator takes into account both the ‘relative severity’ of individual collisions between different vehicle types, and the share of those vehicle types within a country's fleet. The relative severity is a measure for the personal damage that can be expected from a collision between two vehicles of any type, relative to that of a collision between passenger cars. It is shown how this number can be calculated using vehicle mass only. A sensitivity analysis is performed to study the dependence of the indicator on parameter values and basic assumptions made. The indicator is easy to apply and satisfies the requirements for appropriate safety performance indicators. It was developed in such a way that it specifically scores the intrinsic safety of a fleet due to its composition, without being influenced by other factors, like helmet wearing. For the sake of simplicity, and since the required data is available throughout Europe, the indicator was applied to the relative share of three of the main vehicle types: passenger cars, heavy goods vehicles and motorcycles. Using the vehicle fleet data from 13EU Member States and Norway, the indicator was used to rank the countries’ safety performance. The UK was found to perform best in terms of its fleet composition (value is 1.07), while Greece has the worst performance with the highest indicator value (1.41).

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.

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.The concept of TM SPIs was developed based on a literature study of performance indicators in TM, a survey of available practices in Europe and data availability examinations. A set of TM SPIs was introduced including 14 indicators which characterize five issues such as: availability of EMS stations; availability and composition of EMS medical staff; availability and composition of EMS transportation units; characteristics of the EMS response time, and availability of trauma beds in permanent medical facilities. Basic information on the TM systems was collected in close cooperation with the national expert group. A dataset with TM SPIs for 21 countries was created. It was demonstrated that the countries can be compared using selected TM SPIs. Moreover, a more general comparison of the TM systems’ performance in the countries is possible, using multiple ranking and statistical weighting techniques. By both methods, final estimates were received enabling the recognition of groups of countries with similar levels of the TM system's performance. The results of various trials were consistent as to the recognition of countries with high or low level of the TM systems’ performance, where in grouping countries with intermediate levels of the TM system's performance some differences were observed. The SafetyNet project's practice demonstrated that data collection for estimating TM SPIs is not an easy task but is realizable for the majority of countries. The TM SPIs’ message is currently limited to the availability of trauma care services. Further development of the TM SPIs should focus on characteristics of actual treatment supplied, based on combined police and medical road crash related databases.

Constructing road safety performance indicators using Fuzzy Delphi Method and Grey Delphi Method

The main goal of this paper is to construct three sets of road safety performance indicators, which are regional road safety performance indicators, urban road safety performance indicators and highway safety performance indicators, respectively. Fuzzy Delphi Method and Grey Delphi Method are applied to quantify experts’ attitudes to regional road safety, urban road safety and highway safety. Comparing the results of two methods, the different results of two methods are analyzed, and then the final safety performance indicators are obtained by taking the intersection of results of two methods. Finally, three sets of performance indicators are constructed, which can be described and evaluated the safety level of region, urban road and highway, respectively. The research findings show that the method used in this paper is feasible and practical and can be provided as a reference for the administrative authority of road safety.

Road safety performance indicators and their explanatory value: A critical view based on the experience of Central European countries

Counts of road crashes and their victims represent essential information for road safety practitioners allowing them to analyse their spatial and temporal aspects. However, they cannot provide details on the factors causing road crashes. As a result, various road safety performance indicators (RSPIs) were introduced, making it possible to gather information on the effectiveness of interventions on road safety in given areas. However, analysis of the trends in road casualties in several Central European countries based on safety performance (measured by RSPIs) suggest that not even these indicators can provide full understanding of road safety trends, and, if they are applied generally without the required background information, this could even lead to serious misinterpretation of the trends in road casualties. Sudden breaks in long-term trends seem to be linked to the transition process and to certain legislative reforms. The exposure and socio-economic climate appear to have had a major impact on road crash outcomes. Various additional indicators describing organisational and structural aspects may be helpful, therefore, in better understanding and predicting the development in road safety for individual countries.

Benchmarking road safety performances of countries

In order to obtain political interest in road safety problems and to learn from other countries’ ‘good practices’, it is often helpful to compare one’s own safety situation with that of other countries. In a number of projects tools have been developed for such comparisons. These tools range from simple ratings of countries on their safety outcomes, such as the annual number of fatalities per capita or per kilometre driven by (motor)vehicles to more comprehensive comparisons. These comparisons not only show differences in safety between countries, but to a certain extent also explain such differences in terms of their safety background and measures taken. Finally, tools have been defined to support road safety policy makers in developing possible safety measures or actions. Procedures for such complex safety comparisons have been developed and tested in several so-called SUNflower studies. This promising approach can be further developed into standard procedures for safety comparisons between all countries in the European Union, and other countries worldwide. This paper wishes to outline the development of such standards for the benchmarking of road safety and safety trends as well as procedures for quantifying safety performances of countries. Starting point of this conceptual framework is the so-called SUNflower-pyramid in which three types of indicators are distinguished. The first one of these, the road safety performance indicator, is called an outcome indicator and is based on the number of killed and injured road users. The second indicator type indicates the quality of the implementation of road safety policies: the implementation performance indicators. The third type of indicator indicates the quality of response in policy documents to improve road safety (policy performance indicator). The three types of indicators are embedded in a policy context: the structure and culture of a country, which are considered as background variables. This paper sets out to describe the framework for the development of a comprehensive set of indicators to benchmark road safety performances of countries or of sub-national jurisdictions. The paper also discusses the advantages and disadvantages of combining such indicators and if combined, how to aggregate how different indicators in one composite performance index. It is argued to group countries in different classes with more or less comparable countries. Different procedures are used for this grouping. The results are promising and it is recommended to work with classes of countries.

A hybrid system of neural networks and rough sets for road safety performance indicators

Road safety performance indicators are comprehensible tools that provide a better understanding of current safety conditions and can be used to monitor the effect of policy interventions. New insights can be gained in case one road safety index is composed of all risk indicators. The overall safety performance can then be evaluated, and countries ranked. In this paper, a promising structure of neural networks based on decision rules generated by rough sets--is proposed to develop an overall road safety index. This novel hybrid system integrates the ability of neural networks on self-learning and that of rough sets on automatically transforming data into knowledge. By means of simulation, optimal weights are assigned to seven road safety performance indicators. The ranking of 21 European countries in terms of their road safety index scores is compared to a ranking based on the number of road fatalities per million inhabitants. Evaluation results imply the feasibility of this intelligent decision support system and valuable predictive power for the road safety indicators context.

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.

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.

Simultaneous Modelling of Multiple Traffic Safety Performance Indicators by using a Multivariate Generalized Linear Mixed Model

SummaryTraffic safety in the UK is one of the increasing number of areas where central government sets targets based on ‘outcome-focused’ performance indicators (PIs). Judgments about such PIs are often based solely on rankings of raw indicators and simple league tables dominate centrally published analyses. There is a considerable statistical literature examining health and education issues which has tended to use the generalized linear mixed model (GLMM) to address variability in the data when drawing inferences about relative performance from headline PIs. This methodology could obviously be applied in contexts such as traffic safety. However, when such models are applied to the fairly crude data sets that are currently available, the interval estimates generated, e.g. in respect of rankings, are often too broad to allow much real differentiation between the traffic safety performance of the units that are being considered. Such results sit uncomfortably with the ethos of ‘performance management’ and raise the question of whether the inference from such data sets about relative performance can be improved in some way. Motivated by consideration of a set of nine road safety performance indicators measured on English local authorities in the year 2000, the paper considers methods to strengthen the weak inference that is obtained from GLMMs of individual indicators by simultaneous, multivariate modelling of a range of related indicators. The correlation structure between indicators is used to reduce the uncertainty that is associated with rankings of any one of the individual indicators. The results demonstrate that credible intervals can be substantially narrowed by the use of the multivariate GLMM approach and that multivariate modelling of multiple PIs may therefore have considerable potential for introducing more robust and realistic assessments of differential performance in some contexts.