# Efficient selection of sites for treatment

To implement road safety measures in a way that produces maximum benefits, it is necessary to select areas or units for implementation that have the highest expected number of accidents or injuries. Such selection is only possible if a state- of-the-art road safety management system has been introduced. The problem can be illustrated by reference to Figure 3.

Figure 3 shows the Empirical Bayes (EB) estimate of the expected number of injury accidents per kilometre of road for a major road in Norway. The road has a length of 55 kilometres. The sections shaded in grey are those that have the highest expected number of accidents. These sections are, however, not located next to each other, but are scattered along the road. To produce maximum benefits, road safety measures should be targeted at the sections shaded in grey.

It is important to note the fact that EB-estimates are derived as the weighted average of a normal number of accidents estimated by means of an accident prediction model, and the recorded number of accidents for reach road section.

Ideally speaking, EB-estimates of safety control for regression-to-the-mean, while capturing the effects of local conditions that influence the expected number of accidents on each 1-kilometre section.

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

0 10 20 30 40 50 60

EB estimate of expected number of injury accidents (8 years)

W=3; E(m)=9.36

W=6; E(m)=7.21

Mean = 4.68

Figure 3: EB-estimates of the expected number of injury accidents per kilometre of road for a major road in Norway

Unfortunately, EB-estimates of road safety cannot be developed for all types of roadway elements in Norway. Accident prediction models have only been

developed for road sections of 1 kilometre (Ragnøy, Christensen and Elvik 2002).

Although simple models are available for junctions (Sakshaug and Johannessen 2005), these models do not lend themselves to estimation by means of the EB- method. No models have been developed for other roadway elements, like bridges or tunnels. It is at this time therefore not possible to use the EB-method as a means of selecting the most promising target sites for road safety measures. To model the selection of sites and objects for road safety measures, there are two other possibilities:

1. Study the actual process of selection and identify parameters that are correlated with the benefit-cost ratio of measures.

2. Develop a simple model of selection, based on one or a few variables that are known to be highly correlated with the expected number of accidents or injuries.

The process of selection for road safety treatment in Norway has been studied by Elvik (2004A). One of the objectives of the study was to assess the extent to which high-risk sites were selected for treatment. Some key findings of the study were:

1. About 47% of intersections selected for treatment had a higher-than- normal accident rate, 47% had a lower-than-normal accident rate, and 6%

had an accident rate close to the normal rate. The mean ratio of the observed accident rate in treated intersections to the normal accident rate for intersections at large was about 1.60.

2. For road sections selected for safety treatment, 39% had a higher-than- normal accident rate, 39% had a lower-than-normal accident rate, and 22%

had an accident rate close to the normal rate. The mean ratio of the

observed accident rate for treated road sections to that for road sections in general was about 1.75.

3. Intersections and road sections selected for safety treatment in Norway have a substantially higher traffic volume than the mean traffic volume for intersections or road sections. Traffic volume appears to be an important criterion for selecting a site for treatment.

4. Selection for road safety treatment in Norway is slightly biased in favour of high-risk sites, but the bias is comparatively small and will, at least for road sections, not be associated with a very large regression-to-the-mean effect.

The data referring to roundabouts were analysed in greater detail for the purpose of identifying variables predicting the efficient selection of junctions for

conversion to roundabouts (Elvik 2004B). The term efficient selection, as opposed to inefficient selection, refers to the selection of junctions that can be converted to roundabouts cost-effectively, i.e. at a cost smaller than the benefits of the conversion.

The analysis found that current selection of roundabouts for treatment is

somewhat inefficient. For three-leg junctions benefits were smaller than costs in 13 junctions out of 27 converted. The conversion of these junctions to

roundabouts accounted for 60% of the total costs of converting the junctions to roundabouts. These findings are illustrated in Figure 4.

0 20 40 60 80 100 120 140 160 180

0 20 40 60 80 100 120 140 160 180

Costs (million NOK)

Benefits (million NOK)

Total benefits = 156.9

Total costs = 156.3

Benefits = 104.4

Costs = 62.1

Marginal costs = 94.2 Marginal benefits = 52.5

Figure 4: Costs and benefits of converting 27 three-leg junctions to roundabouts. Source:

Elvik 2004B

The benefit-cost ratio of converting three-leg junctions to roundabouts was only weakly correlated with factors one might expect to influence it. Pearson’s r was .044 for AADT, -.062 for the expected number of injured road users, and -.327 for the cost of conversion. One would, ceteris paribus, expect benefit-cost ratio to correlate strongly with traffic volume (AADT) and the number of injured road users.

In short, the data describing the actual selection of sites for treatment were strongly influenced by random variation and by unknown sources of site-to-site variation with respect to, for example, the cost of the measures and the level of accident risk. It is therefore not possible to model an efficient selection of sites for treatment based on data referring to the actual selection of sites for treatment. The analyses presented in this report are based on a general model of selection for treatment, designed to support marginal analysis of road safety measures.

5.3 The logic of marginal analysis of road safety measures

Outline

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