Report numberRA-MOW-2010-009
TitleCombination of policy measures.
AuthorsBetty Nambuusi
Elke Hermans
Tom Brijs
Published byPolicy Research Centre for Mobility and Public Works, track Traffic Safety 2007-2011
Number of pages24
Document languageEnglish
Partner(s)Universiteit Hasselt
Work packageOther: Policy organisation ans monitoring

Road safety plans comprising several road safety measures have been developed throughout the world. To estimate the effects of such plans on road safety, methods that consider the combined effect of road safety measures are required. This report presents three methods for quantifying the combined effects of several road safety measures introduced around the same time: the accident modification factor method (Smeed, 1949), the dominant common residuals method (Elvik, 2009) and the synergy model (suggested in this report). Based on a few studies that investigated the effects of a set of road safety measures, the goodness of fit of the methods is assessed. All methods are found to describe the data with sufficient precision.


The method of accident modification factor is the most common one for modelling the effect of a set of measures. The term “accident modification factor” refers to the proportion of accidents that remain after a measure has taken place. This method assumes that the first order effect of a measure is independent of the first order effects of any other measures and remains unchanged when introducing other measures. (The first order effect is the effect that each measure has when it is the only measure having an effect and everything else is unchanged.) This method assumes measures to be independent and computes their linear effect. The result gained from this method will be compared to the results estimated by means of the dominant common residuals method and the synergy model. If the combined effect of measures is larger or smaller than the sum of their individual effects, there is departure from linearity. A larger effect reflects synergy while a smaller one exhibits substitution.


In the real world, effects of measures implemented simultaneously are not always entirely independent. Measures are likely to influence some of the risk factors at which other measures also aim, thus reducing their likely effects. For instance, pedestrian reflective devices will be less effective on well lit roads than on unlit roads; seat belt ignition interlocks will render any other measure designed to increase seat belt wearing less effective (Elvik, 2009). To account for this, the dominant common residuals method is proposed. The basic idea underlying this method is that the most effective measure in a set dominates the others to some extent, by partly or fully influencing the same group of accidents or the same risk factors. The computation in this method resembles that in the accident modification factor method, except that the product of the accident modification factors is raised to the power of the accident modification factor for the most effective measure included in the set. This method results into a smaller estimate of the combined effects compared to the accident modification factor method and depicts the substitution effect.


On the other hand, some measures reinforce each other. For example, Vaa et al. (2009) found that a combination of road safety campaigns and increased enforcement could be associated with more reduced accident counts. This is taken into account using the synergy model. In this case, the product of the accident modification factors is raised to the inverse of the accident modification factor of the most effective measure in the set. The combined effect estimated then is larger than the effect computed using the accident modification factor method and depicts synergy.


Apart from presenting the three methods, a case study is carried out in this report. By means of the case study, the different stages of the computational model are illustrated. More information on the computational model can be found in Nambuusi et al. (2009). This model starts from the regional road safety explorer (RRSE) model developed by Reurings and Wijnen (2008). The number of injury accidents saved when applying a particular set of measures, and taking into account other factors influencing road safety (e.g. the growth in traffic performance) is computed. For means of illustration, possible measures from literature are used.


In the current report, the computational model is adjusted in order to be able to assess the combined effect of a set of measures which might be dependent of one another. In other words, apart from only applying the accident modification factor method (and assuming all measures to be independent), the dominant common residuals method and the synergy model are integrated in the computational model. More specifically, in the case study, the dominant common residuals method is applied in 2004, the synergy model in 2006 and the accident modification factor method in 2009. Through this case study, the extended computational model is illustrated.


This report identifies valuable methods that can be used to estimate the combined effect of road safety measures. Which method to apply on a particular combination of measures will depend on the kind of relationship between them. In other words, prior knowledge of whether the measures reinforce each other, weaken each other or be independent of each other is necessary before applying the methods. In the future, the three methods (the accident modification factor method, the dominant common residuals method and the synergy model) will be utilized to assess the road safety impact of measures listed in the road safety plan for Flanders.

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The Policy Research Centre for Traffic Safety carries out policy relevant scientific research under the authority of the Flemish Government. The Centre is the result of a

cooperation between Hasselt University, KU Leuven and VITO, the Flemish Institute for Technological Research.


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