a b s t r a c tIn 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.

a b s t r a c t a r t i c l e i n f o Available online xxxx Keywords:Facility location Organ transplant Long-term planning Mixed integer linear programming This paper presents a mixed integer linear programming (MILP) long-term decision model to optimize the location of organ transplant centers. The objective is to minimize the sum of the weighted time components between the moment a donor organ becomes available and its transplantation into the recipient's body. The weight factor for the elapsed time before the organ's removal from the donor body allows to assign a lower weight to this time component in the objective function in order to reflect the criticality of the process after the organ's removal. The specificity of organ transplants makes the model more complex than a traditional facility location model. The model is applied to the Belgian organ transplant path. Extensive numerical experiments reveal the key factors that impact the long-term decision of centralizing versus decentralizing transplant centers.

Road safety has recently become a major concern in most modern societies. The identification of sites that are more dangerous than others (black spots) can help in better scheduling road safety policies. This paper proposes a methodology for ranking sites according to their level of hazard. The model is innovative in at least two respects.Firstly, it makes use of all relevant information per accident location, including the total number of accidents and the number of fatalities, as well as the number of slight and serious injuries. Secondly, the model includes the use of a cost function to rank the sites with respect to their total expected cost to society. Bayesian estimation for the model via a Markov Chain Monte Carlo (MCMC) approach is proposed. Accident data from 519 intersections in Leuven (Belgium) are used to illustrate the proposed methodology. Furthermore, different cost functions are used in the paper in order to show the impact of the proposed method on the use of different costs per injury type.

a b s t r a c tComposite 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.

These days, road safety has become a major concern in most modern societies. In this respect, the determination of road locations that are more dangerous than others (black spots or also called 'sites with promise') can help in better scheduling road safety policies. The present paper proposes a multivariate model to identify and rank sites according to their total expected cost to the society. Bayesian estimation of the model via a Markov Chain Monte Carlo (MCMC) approach is discussed in the paper. To illustrate the proposed model, accident data from 23184 accident locations in Flanders (Belgium) are used and a cost function proposed by the European Transport Safety Council is adopted to illustrate the model. It is shown in the paper that the model produces insightful results that can help policy makers in prioritizing road infrastructure investments.