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Dive into the research topics where Elke Hermans is active.

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Featured researches published by Elke Hermans.


Accident Analysis & Prevention | 2008

Combining road safety information in a performance index

Elke Hermans; Filip Van den Bossche; Geert Wets

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.


Accident Analysis & Prevention | 2009

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

Elke Hermans; Tom Brijs; Geert Wets; Koen Vanhoof

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 countrys 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.


Accident Analysis & Prevention | 2012

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

Yongjun Shen; Elke Hermans; Tom Brijs; Geert Wets; Koen Vanhoof

Currently, comparison between countries in terms of their road safety performance is widely conducted in order to better understand ones 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 countrys 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 models 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.


Expert Systems With Applications | 2011

A generalized multiple layer data envelopment analysis model for hierarchical structure assessment: A case study in road safety performance evaluation

Yongjun Shen; Elke Hermans; Da Ruan; Geert Wets; Tom Brijs; Koen Vanhoof

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 todays 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 models 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.


Knowledge Based Systems | 2010

Road safety risk evaluation by means of ordered weighted averaging operators and expert knowledge

Elke Hermans; Da Ruan; Tom Brijs; Geert Wets; Koen Vanhoof

The road safety performance of countries is conducted by combining seven main risk indicators into one index using a particular weighting and aggregation method. Weights can be determined with respect to the assumed importance of the indicator, whereas aggregation operators can be used to stress better performances differently from worse performances irrespective of the indicators meaning. In this research, both expert weights and ordered weighted averaging operators are explored, evaluated and integrated resulting in a ranking of countries based on a road safety index.


International Journal of Systems Assurance Engineering and Management | 2011

Modeling qualitative data in data envelopment analysis for composite indicators

Yongjun Shen; Da Ruan; Elke Hermans; Tom Brijs; Geert Wets; Koen Vanhoof

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.


Reliability Engineering & System Safety | 2009

Uncertainty assessment of the road safety index

Elke Hermans; Filip Van den Bossche; Geert Wets

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.


soft computing | 2010

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

Yongjun Shen; Tianrui Li; Elke Hermans; Da Ruan; Geert Wets; Koen Vanhoof; Tom Brijs

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.


Transportation Research Record | 2006

Describing the Evolution in the Number of Highway Deaths by Decomposition in Exposure, Accident Risk, and Fatality Risk

Elke Hermans; Geert Wets; Filip Van den Bossche

The general purpose of this research is to improve insight into road safety on Belgian highways by means of a layered model. The monthly number of persons killed on highways in Belgium is decomposed into three parts: exposure, accident risk, and fatality risk. The evolution in each of these dimensions is investigated separately. More specifically, for each dimension a descriptive and explanatory analysis reveals the optimal unobserved components model. The separate analysis of each dimension may reveal different underlying developments. The impact of meteorological, socioeconomic, legislative, and calendar factors on exposure, accident risk, and fatality risk is investigated. The analysis indicates that, although for each dimension the same basic components are available, the optimal model of each dimension has its unique structure of descriptive components and significant variables. Precipitation and snow enhance accident risk, while temperature plays a significant role for exposure. Fatality risk decreases in case of an extra day with precipitation and was significantly affected by the child restraint law. The economic indicators mainly affect accident risk. When the three models are brought back together, the number of highway deaths between 1993 and 2001 is well reconstructed.


Accident Analysis & Prevention | 2014

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

Emmanuelle Dupont; Jacques J.F. Commandeur; Sylvain Lassarre; Frits Bijleveld; Heike Martensen; Constantinos Antoniou; Eleonora Papadimitriou; George Yannis; Elke Hermans; Katherine Pérez; Elena Santamariña-Rubio; Davide Shingo Usami; Gabriele Giustiniani

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.

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Geert Wets

Katholieke Universiteit Leuven

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Tom Brijs

University of Hasselt

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