A computational model to assess the impact of a set of policy measures on road safety at the regional level

Abstract

Policy measures in road safety are frequently taken at a locational level (e.g. at a particular risky intersection). This appoach does not facilitate assessing the effects of policy measures in a broader perspective. To achieve this, a method estimating road safety effects at a regional level is a valuable tool. Moreover, several policy measures are often implemented simultaneously in the real world. To model the effect of a set of measures on road safety, methods for estimating the combined effect of policy measures are required. In most cases, the accident modification factors are multiplied. The term "modification factor" refers to the accidents that remain after a measure has taken place. In that case, a measure effect is assumed to be independent of the effects of any other measure and to remain unchanged when introducing other measures. However, this assumption is likely to be incorrect in a lot of cases. One would expect dependence among measure effects applied around the same time. Therefore, the dominant common residuals method will be illustrated in this paper. 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. A model assisting regions to assess the road safety effects of a set of measures on a broader area and aiding in selecting measures resulting in the most efficient cost-benefit ratios is discussed. The Regional road safety explorer (RRSE) model developed by SWOV (Reurings and Wijnen, 2008) is used as a starting point for the region of Flanders in Belgium. The model consists of five stages: the reference situation, the baseline prognosis, the measure prognosis, the number of saved injury accidents and the cost-benefit analysis. The reference situation describes the current traffic performance (exposure) and the current road safety situation in the region. The model considers a long time perspective and therefore, the main future evolutions in exposure and autonomous risk are taken into account in the baseline prognosis. The measure prognosis relates to the situation after applying and estimating the effectiveness of measures on road safety. The main outputs of the model are the number of saved injury accidents(and/or casualties) and the cost-benefit ratios of the measures taken. By expressing the saved injury accidents in monetary values, the cost-benefit analysis determines whether the applied measures are cost-effective. Through this analysis, policy makers are assisted in selecting policies that make the most efficient use of resources.