Assessing Road Safety Performance by Data Envelopment Analysis--The Case of Brazil (original) (raw)
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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.
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The rapid growth of the economy has led to the increased in road traffic networks and had indirectly led to the rapid cases of road accidents in Malaysia. Road accidents are one of the main contributors to human deaths in Malaysia. This paper attempts to measure road accidents in Malaysia by looking at the road accidents of 13 states and a federal territory. The aim is to measure the numbers and causes of road accidents by using Data Envelopment Analysis (DEA). Due to that, the input and output are identified to compute the efficiency level of road accidents. Apart of that, the trends in the number of road accidents in Malaysia is also depicted. For this study, the data from 2008 to 2011 for each Decision Making Unit (DMU) is analyzed. The result shows that the efficiency level did not determined by the number of vehicles on the road and the size of the state but it is determined by the utilization of resources by the authorities. It shows that managing input is important when the level of efficiency for the Decision-Making Unit (DMU) for the output is concerned. The outcome of this study supports the government measures to level up road maintenance in order to improve the efficiency level and curb the numbers of road accidents in Malaysia.
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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.
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Data envelopment analysis (DEA) is a powerful analytical research tool for measuring the relative efficiency of a homogeneous set of decision making units (DMUs) by obtaining empirical estimates of relations between multiple inputs and multiple outputs related to the DMUs. To further embody multilayer hierarchical structures of these inputs and outputs in the DEA framework, which are prevalent in today's performance evaluation activities, we propose a generalized multiple layer DEA (MLDEA) model. Starting from the input-oriented CCR model, we elaborate the mathematical deduction process of the MLDEA model, formulate the weights in each layer of the hierarchy, and indicate different types of possible weight restrictions. Meanwhile, its linear transformation is realized and further extended to the BCC form. To demonstrate the proposed MLDEA model, a case study in evaluating the road safety performance of a set of 19 European countries is carried out. By using 13 hierarchical safety performance indicators in terms of road user behavior (e.g., inappropriate or excessive speed) as the model's input and 4 layered road safety final outcomes (e.g., road fatalities) as the output, we compute the most optimal road safety efficiency score for the set of European countries, and further analyze the weights assigned to each layer of the hierarchy. A comparison of the results with the ones from the one layer DEA model clearly indicates the usefulness and effectiveness of this improvement in dealing with a great number of performance evaluation activities with hierarchical structures.
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The data envelopment analysis (DEA) based Malmquist productivity index measures the productivity change over time. It can be further decomposed into two components: the change in efficiency and the technical change. In this study, a specific road safety outputoriented DEA-based Malmquist productivity index is introduced to assess the changes in road safety performance of 26 EU countries from 2000 to 2007. The results show a considerable road safety progress in most of the member states during this period. The decomposition into the two components further reveals that the bulk of the improvement was attained through the adoption of new road safety technologies or strategies, i.e., the technical change, rather than through the relatively inefficient countries catching up with those efficient ones, known as the efficiency change.
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International Journal of System Assurance Engineering and Management, 2011
Composite indicators (CIs) are useful tools for performance evaluation in policy analysis and public communication. Among various performance evaluation methodologies, data envelopment analysis (DEA) has recently received considerable attention in the construction of CIs. In basic DEA-based CI models, obtainment of measurable and quantitative indicators is commonly the prerequisite of the evaluation. However, it becomes more and more difficult to be guaranteed in today's complex performance evaluation activities, because the natural uncertainty of reality often leads up to the imprecision and vagueness inherent in the information that can only be represented by means of qualitative data. In this study, we investigate two approaches within the DEA framework for modeling both quantitative and qualitative data in the context of composite indicators construction. They are imprecise DEA (IDEA) and fuzzy DEA (FDEA), respectively. Based on their principle, we propose two new models of IDEA-based CIs and FDEA-based CIs in road safety management evaluation by creating a composite road safety policy performance index for 25 European countries. The results verify the robustness of the index scores computed from both models, and further imply the effectiveness and reliability of the proposed two approaches for modeling qualitative data.